rafmacalaba/data-use-mentions-extended huggingface dataset:
622 million, a reduction of 5 percent. The majority of the poor by 2015 are projected to be in Sub-Saharan Africa, and there the reduction would be 6 percent.<sup>8</sup> Under the Doha scenarios reported in Table 12, the poverty impacts are far more modest. The number of poor living on $1/day or less would fall by 2.5 million in the case of the core Doha Scenario 5 (of which 0.5 million are in SSA) and by 6.3 million in the case of Doha Scenario 6 (of which 2.2 million are in SSA). This corresponds to the relatively modest ambitions of the merchandise trade reforms as captured in these Doha scenarios. If only agriculture was reformed (Doha Scenario 1) there would be much less poverty alleviation globally and none at all in SSA. This shows the importance for poverty of including manufactured products in the Doha negotiations.

# **_Developing countries could trade off Special and Differential Treatment for_**

**_more market access._** If developing countries were to tone down their call for Special and Differential Treatment (see Josling 2005), in terms of wanting smaller cuts and longer phase-in periods, reciprocity means they could expect bigger tariff and subsidy cuts from developed countries. Similarly, if they were to forego their call for lesser cuts for “Special Products”, they could demand that developed countries forego their call for some “Sensitive Products” to be subject to smaller tariff cuts. A comparison of Scenarios 5 and 6 in Tables 7, 8 and 9 shows that the economic payoffs for low-income countries even if high-income countries do not reciprocate with larger offers is considerable. Moreover, by embracing those options to reform more in the context of the Doha round

8 The approach here has been to take the change in the average per capita consumption of the poor, apply an estimated income-to-poverty elasticity, and assess the impacts on the poverty headcount index. We have done this by calculating
# **A ONLINE APPENDIX**

## **A.1 Determination of Rewards and the Reward Ceremonies**

Table A.1 shows the mapping between the performance indicator and rewards. The rewards ranged from bronze to platinum. There was one national ceremony (for recipients of the platinum certificate), one ceremony in each of the eight regions (for the gold certificate), one ceremony in each of the 38 prefectures (silver), and one ceremony in each of the 243 sub-prefectures (bronze). The ministry of education organized the national ceremony; the Regional Authorities of Education organized the regional and prefecture-level ceremonies; and the “School Delegation of Elementary Education” (DSEE) organized the sub-prefecture ceremonies. The ceremonies were attended by government officials, trade unions, national or local press, locally elected officials, and school parent associations.

Table A.2 shows the breakdown of awards received after the first year of the program and the value of the awards as a proportion of the average civil servant teacher salary. Over a third did not receive any reward, and a quarter of teachers received the highest possible reward. The value of the reward was highly convex, with 4% of salary for the lowest tier (rice and radio), and almost 49% of a teacher’s annual salary for the highest tier (rice, TV, cell phone, and generator).

## **A.2 Tests for Internal Validity**

### **A.2.1 Balance and Attrition**

We investigate internal validity by comparing baseline data from Grade 2 and Grade 3 students from the student assessment, as well as school-level data as reported by school principals, across the groups. The sample is balanced on key variables–test scores and gender–collected at the baseline; only age shows a difference between control and treatment arms (Table A.7). School characteristics (number of Grade 2 and Grade 3 students, headteacher gender and age) are
striking observation from Figure 3 is how much more important SR yields are in comparison to the rest of the covariates when it comes to log predictions. We assess the relative importance of covariates in the accuracy of the prediction in the following subsection.

# **5.1.2 Relative importance of SR yields and GPS extracted variables**

We run the ensemble of methods described in Appendix A for 25 different cross-validation splits to predict CC yields, testing for the inclusion of SR yields and geospatial variables extracted using the EA GPS coordinates<sup>16</sup> among the set of predictors. This approach allows us to evaluate the impact and relevance of these specific variables in the context of our machine learning models and the overall accuracy of the yield predictions.

Table 6<sup>17</sup> shows the impact of including SR yields and/or GPS extracted variables on the quality of the predictions, measured in terms of _R_<sup>2</sup> for the whole set of crops and individually for each crop. The reference model set up does not include SR or geospatial variables in the set of covariates.

First, including SR yield or GPS extracted variables increases unambiguously the accuracy of yield prediction as the difference in _R_<sup>2</sup> is for the most part positive and statistically significant. Second, combining SR and GPS provides on average better accuracy than using one or the other set of covariates. Third, there is some heterogeneity in terms of crops. The SR yield variable is more relevant for sorghum and cowpea imputations, although the overall accuracy of prediction of SR yield without including GPS extracted variables is low. GPS extracted variables, both with and without including SR yields, are particularly important for groundnut, sorghum and millet yield predictions. Finally, cowpea yield prediction benefits particularly from the inclusion of both SR yield and GPS extracted variables. The heterogeneity of the impact of the different combinations of covariates for each crop predictions is difficult to interpret especially since
, Hogrefe and Wrona (2015) develop a theoretical model examining the impact of offshoring on individual skill upgrading through on-the-job training. The model predicts that offshoring, by scaling up workers’ wages, creates previously unexploited skill upgrading possibilities and, thus, leads to more on-the-job training. Using individual-level survey data from Germany, they document a positive relation between an industry-level measure of offshoring and job training.<sup>1</sup>

> 1More broadly, our paper is also related to the literature on labor market adjustment to globalization;

4
- 42 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

Kasanko, M; Barredo, JI; Lavalle, C; McCormick, N; Demicheli, L; Sagris, V; Brezger, A (2006). Are European cities becoming dispersed? A comparative analysis of 15 European urban áreas. Landscape and Urban Planning, 77:111–130

- Li, C., & Lian, L. (2012). Theoretical Research of the Urban Comprehensive Carrying Capacity in the Epoch of Urbanization. International Journal of Financial Research, 3(1), 105–113. https://doi.org/10.5430/ijfr.v3n1p105

- Lin, G.C.S., 2002. The growth and structural change of Chinese cities: a contextual and geographic analysis. Cities 19 (5), 299–316.

- Lo, C. P. (2001). Modeling the Population of China Using DMSP Operational Linescan System Nighttime Data. _Photogrammetric Engineering and Remote Sensing_ , _67_ (9), 1037–1047.

- Lopez, E., Bocco, G., Mendosa, M., Duhau, E., 2001. Predicting land-cover and land-use change in the urban fringe. A case in Morelia city, Mexico. Landscape Urban Plan. 55 (4), 271–285.

Lynch, K (1981) _A theory of good city form_ . MIT Press, Cambridge, MA

- Ma, L., Wu, J., Li, W., Peng, J., & Liu, H. (2014). Evaluating saturation correction methods for DMSP/OLS nighttime light data: A case study from China’s cities. _Remote Sensing_ , _6_ (10), 9853–9872. https://doi.org/10.3390/rs6109853

- Pandey, B., Joshi, P. K
Table 7 Results of backcasting consumption from 2018/19 NLSS using sectoral GDP growth rates mapped to household head's employment sector

||Poverty rates at the US$1.90<br>poverty line (%)|Number o<br>US$1.90<br>(millions)|f poor at the<br>poverty<br>line|Poverty<br>US$3.20 p|rates<br>at<br>the<br>overty line (%)|Number o<br>US$3.20<br>(millions)|f poor at the<br>poverty<br>line|
|---|---|---|---|---|---|---|---|
||Backcast, pass-<br>through=1<br>HNLSS/NLSS|Backcast,<br>pass-<br>through=1|HNLSS/NLSS|Backcast,<br>pass-<br>through=1|<br>HNLSS/NLSS|Backcast,<br>pass-<br>through=1|<br>HNLSS/NLSS|
|2009<br>2010|46.3<br>56.4<br>42.8|71.4<br>67.8|89.4|76.5<br>73.5|79.5|118.0<br>116.5|125.9|
|2011|41.8|68.1||72.3||117.7||
|2012|40.3|67.3||71.3||119.3||
|2013|38.9|66.9||69.8||119.9||
|2014|37.4|65
# **Financial Risk Management in Agriculture: Analyzing Data from a New Module of the Global Findex Database**

Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess<sup>1</sup>

**JEL Classification:** D14; G21, G22, O16; Q14

**Keywords:** Financial Inclusion, Sub-Saharan Africa, Agriculture, Risk, Insurance, Household Finance

> 1 Leora Klapper (lklapper@worldbank.org), Dorothe Singer (dsinger@worldbank.org), Saniya Ansar (sansar1@worldbank.org), and Jake Hess (jhess1@worldbank.org) are with the World Bank. This paper’s findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, their Executive Directors, or the countries they represent. We are grateful to the Bill & Melinda Gates Foundation for providing financial support making the collection and dissemination of the 2017 Global Findex data possible and to the AXA Foundation for financial support to develop the module on financial risk management in agriculture.
continue to rise (“hot house”), in line with NGFS climate scenarios.<sup>5</sup> These assessments have that under a short-term, three-year disorderly transition risk scenario and the two physical risk scenarios—flood risk and drought and heat risk—the combined credit and market risk losses for the 41 banks providing projections would amount to around €70 billion (ECB 2022).

**Measuring climate risks in European CGSs portfolios is relevant for public policy and fiscal risks.** CGSs are a traditionally important policy tool to ease access to finance for SMEs in Europe, with several studies demonstrating their financial and economic additionality (see Neuberger 2020 for a review of the literature). Financial sustainability is a necessary though not sufficient condition for CGSs to achieve financial and economic additionality. Therefore, measuring (and eventually managing) climate-related financial risks is an important step towards improving the performance of CGSs and hence potentially strengthening the effectiveness of public policy. Measuring climate-related financial risks is also important for mitigating fiscal risks, given that CGSs represent a contingent liability for many governments in Europe.

# 3. Data and methodology

**The aim of this paper is to explore the vulnerability of European CGSs to climate-related financial risks.** To this end, we collect unique exposure data from 29 CGSs across Europe on their sectoral and spatial disaggregation of guarantee portfolios. We then link the CGS portfolio data to a set of sectoral and spatially disaggregated transition and physical risk vulnerability indicators, which allows us to identify the portfolio shares at risk per indicator. We also identify sectors that are most relevant for CGSs to focus on by creating a transition risk score which combines exposure with GHG intensity. Finally, we conduct a stylized stress test to assess potential financial losses that CGSs could incur in adverse climate-related scenarios.

**Exposure data is collected through a survey among AECM members while vulnerability data has been obtained from a range of other sources.** Outstanding guarantees with sectoral and spatial breakdowns have been collected from 29
in urban areas from 2006 to 2008 constructed by the Government of Afghanistan.<sup>19</sup> During the NRVA survey timeframe (August 2007-September 2008), the urban food CPI increased by nearly 60%, while the nonfood CPI increased by only 10%. Our calculations using the NRVA district price survey data similarly indicate a 60% weighted average increase in food prices in urban areas during this period (with the overall increase at 40%).

> 19 The consumer prices indices were constructed by the Afghan Central Statistics Organization, based on data from six urban areas of Afghanistan.

7
# **1. Introduction**

The world is rapidly losing biodiversity. Pimm et al. (2014)<sup>1</sup> found that the current rate of species extinction is at least 1,000 times the background rate. Supporting evidence from the Living Planet Index (LPI), which tracks population trends for vertebrate species in terrestrial, freshwater, and marine habitats, shows a 69% decline since 1970.

The LPI informs the Convention on Biological Diversity (CBD) and its Conference of Parties (COP). In response to indicators of rapid decline, 188 governments ratified the Kunming-Montreal Global Biodiversity Framework (GBF) at COP 15 in December 2022. Among other measures, the Framework commits participants to protecting 30% of the planet by 2030. This has been labeled the Global 30x30 Initiative and commonly abbreviated as “30x30”.

Effective implementation of 30x30 requires addressing two key questions: (1) What is the spatial distribution of global biodiversity that should be protected? (2) How can protecting 30% of the planet best conserve this biodiversity?

In a previous paper (Dasgupta et al. 2024), we addressed the first question using the Global Biodiversity Information Facility (GBIF), which has expanded to include occurrences for over 2 million species. In the past two years, the GBIF has added about 1.3 million occurrence records daily. Most records include locational coordinates, enabling new estimates of spatial distributions for previously unmapped species and improved estimates for species with existing maps. Using machine-based pattern recognition, we estimated spatial occurrence maps for over 600,000 species. These maps complement previous work by greatly expanding representation for plants, invertebrates and other non-vertebrate species. Our algorithm allows rapid updates and new maps as the GBIF data increase.

In this paper, we draw on the previous work to explore some implications for 30x30 in a sample of 10 countries in Latin America, Africa and the Asia-Pacific region. We focus on individual countries to highlight the role of conservation stewardship in local
employees participate in these economic sectors, compared with 8 percent to 10 percent in the case of men wage employees. Presumably, those women who self-select into these sectors do so because they expect to have good jobs there given either their ability or their training. In other services, the average JQM is 16 percent higher among men than among women. Women and men may be choosing different occupations in this case because other services encompass a wide range of occupations.

Figure 7. The Job Quality Measure among the Wage Employed, by Industry and Sex <!-- Start of picture text --> 4.00 100%<br>90%<br>80%<br>3.00<br>70%<br>60%<br>2.00 50%<br>40%<br>30%<br>1.00<br>20%<br>10%<br>0.00 0%<br>Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male<br>Agriculture Mining Manufacturing Public utilities Construction Commerce Transport and Financial and Public Other Services<br>Communications Business Services Administration<br>Income Benefits Stability Working Conditions Share of Wage Employees<br>JQM<br>Share of Wage Employees<br><!-- End of picture text --> _Source:_ Estimates based on multiple labor force and household surveys (see annex table A1). _Note:_ The sample includes 37 countries with information on all dimensions. The figure shows JQMs by industry, disaggregated by sex, for the wage employed only.

# **b. The main correlates of job quality**

Given the high correlation across various JQM dimensions, the paper follows Brummund, Mann, and Rodríguez-Castelán (2018) and runs the following regression to identify conditional correlations and to understand more clearly the characteristics that may be the main predictors of job quality: where JQjcs is
33

# Figure 7. Financial Systems: 2008-2010 vs 2000-2007 (Financial Institutions) (cont’d)

<!-- Start of picture text --> Efficiency<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Average 2000-2007<br>Stability<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Average 2000-2007<br>Average 2008-2010<br>Average 2008-2010<br><!-- End of picture text --> Source: authors, based on the Global Financial Development Database.
The Brazilian legislation for primary, lower, and upper secondary education determines a school year with a minimum of 200 days and 800 hours of instruction time. The state-managed schools are monitored by the State Department of Education, under the state government administration, and locally-managed schools are monitored by the Municipal Department of Education, under the municipal government administration. The respective Departments of Education are in charge of educational policies implemented at the school level, hiring teachers, providing textbooks, appointing principals, financing school infrastructure, and determining the length of the school day, and school breaks, as well as the beginning and the end of the school year in the schools under their responsibility. Usually, the academic year starts in February, and ends in November, summer break occurs in December and January, and winter breaks cover three to four weeks in July.

Data on students’ proficiency in reading and math come from the School Census, the Brazilian Institute of Geography and Statistics (IBGE), and from _Prova Brasil_ , which is the standardized national proficiency exam used to assess students’ learning in primary and lower secondary education.<sup>7</sup> Since 1995, all private and public schools offering primary and secondary education have participated in the annual School Census. The Census is implemented by the National Institute of Educational Studies and Research (INEP), a research agency under the Brazilian Ministry of Education.<sup>8</sup> The School Census collects information on (i) school facilities, such as libraries, sports courts, and science and computer labs; (ii) school infrastructure such as filtered water, electricity, and internet access; (iii) social services, for example, school transportation and provision of meals; (iv) students, such as gender, color of the skin, age, physical disabilities or mental illness, grade level, instruction time per day, class-size, subjects they are enrolled in, grade promotion, repetition and dropout rates; and (v) teachers,
- Mbiti, I., Weil, D. 2015. Mobile Banking: The Impact of M-Pesa in Kenya, NBER Chapters, in: African Successes: Modernization and Development, Volume 3 National Bureau of Economic Research, Inc.

- Merel P., Sexton R., Suzuki A. 2009. Optimal Investment in Transportation Infrastructure when Middlemen have Market Power: A Developing Country Analysis. _American Journal of Agricultural Econonomics._ 91(2): 462-476.

- Mitchell, T. 2014. Is Knowledge Power? Competition and Information in Agricultural Markets, The Institute for International Integration Studies Discussion Paper Series iiisdp456, IIIS.

- Mitra, S, Mookherjee, D, Torero, M, Visaria, S. 2015. Asymmetric Information and Middleman Margins: An Experiment with Indian Potato Farmers. HKUST IEMS Working Paper Series 2015-29, HKUST Institute for Emerging Market Studies.

- Molony, T. 2008. Running out of Credit: The Limitations of Mobile Telephony in a Tanzanian Agricultural Marketing System. _J. Modern African Studies_ . 46(4), 637-658.

- Muto M., Yamano, T.2009. The Impact of Mobile Phone Coverage Expansion on Market Participation: Panel Data Evidence from Uganda. _World Development_ 37(12), 1887-1896.

- Nakasone, E., Torero, M., Minten, B.. 2014. The power of information: The ICT revolution in agricultural development. _Annual Review of Resource Economics_ 6, 533-550.

- Nyarko Y, Hildebrandt N, Romagnoli G, Soldani E. 2013. _Market Information Systems for Rural Farmers Evaluation of Esoko MIS – Year 1 Results._ New York University, Draft.

- Ogutu, S.O., Okello, J.J., Otieno, D.J., 2014. Impact of information and communication technology-based market information services on smallholder farm input use and productivity: The case of Kenya. _World Development_ , 64, 311-321.

- Oliver, Y., Robertson, M., Wong, M. 2010
As an alternative, Table 17 shows the return differentials conditional on periods in which the simulated global funds perform poorly. In these situations, global funds yield slightly higher weekly returns, with differentials between 0.14% and 0.95% p.w., although these return differentials are not always statistically different from zero. Table 18 reports the results of a similar exercise that focuses on periods when global funds do not perform well. In this case, the simulated global funds perform significantly better than global funds. For example, when the return of global funds is less than 10% in one week, the simulated global funds yield an extra 2.65% p.w. when maximizing the expected return with the largest number of funds. If the longest available sample for variance minimization is considered, the difference in returns is 1.88% p.w.

# **6. Conclusions**

Using a novel micro dataset of portfolio holdings of U.S. mutual funds, this paper studies how institutional investors diversify their portfolios across countries. This dataset allows us to analyze important aspects of international asset allocation, documenting new stylized facts and shedding new light on existing explanations of international portfolio allocation. In particular, we take advantage of the fact that mutual funds belong to families, each having several funds with different scopes for international investment. As the investment scope broadens, one would expect funds to hold more securities and to be better diversified internationally, to the extent that asset returns are not perfectly correlated. This feature of the mutual fund industry enables us to study to what extent organizational aspects and other factors are important for international asset allocation and whether the investment practices exploit the potential gains from international diversification.

We find that global funds have expanded substantially, giving investors more options to diversify risk internationally. However, regardless of their investment scope, mutual funds tend to invest in a relatively small number of countries and firms, in about 100 stocks. In fact, as their investment possibilities widen
Intellinews-Czech Republic Today (Czech Republic), Irish Times (Ireland), The Belfast News Letter (Ireland), Sunday Business Post-Cork (Ireland), Baltic News Service (Latvia), The Southland Times (New Zealand), Sunday Star-Times (New Zealand), and El Pais (Spain).

# 4 Tax changes: Identifying motivation

This section explains the actual identi...cation of tax changes. We ...rst discuss some general considerations and then classify all the tax changes in our sample into endogenous and exogenous. Then, within each category, we further classify each change into various subcategories that will prove critical for the remainder of the analysis.

## 4.1 Identi...cation strategy

We identify a total of 96 VAT rate changes in 35 countries (18 industrial and 17 developing). Given the time coverage of these 35 countries, there is, on average, a VAT rate change every 11 years with an average change (in absolute value) of 1.9 percentage points. The remaining 16 economies in our sample of 51 countries show no VAT rate change. Out of those 96 VAT rate changes, 60 occurred in industrial countries and 36 in developing ones.

10