rafmacalaba/data-use-mentions-extended huggingface dataset:
_GLOBAL FOOD INSECURITY OUTLOOK_ _3_ of April 2022, a database of macro-economic projections that is updated semiannually by the International Monetary Fund (IMF), to reconstruct and project severe food insecurity from 2005 through 2027.<sup>1</sup> The resulting food insecurity outlook covers all 144 IDA and IBRD countries that together cover approximately 98% of global historical food insecure populations, and is internally consistent with the economic outlooks that serve as the input for the projections.

The results show that the 2021-2023 severely food insecure population may reach above 1 billion in the World Bank’s 144 IDA-eligible and IBRD countries, an increase of over 172 million people over the 2017-2019 pre-pandemic estimate. The projection exceeds the estimate of under 780 million for the 2008 World Food Price Crisis period and may remain above 1 billion through 2027 without large-scale targeted interventions. It is important to note that these results are a forecast of unmitigated impacts extrapolated from 2019 data. At the same time, the WEO outlook of April has not yet factored in the full effects of the Ukraine crisis. Due to the light-weight specification of the model, future results can easily be updated and revised based on new data. The framework can also utilize country specific growth projections available from other authorities and incorporate future relevant data streams that may become available.

Because finance is often unlocked contingent on some estimate of needs or projected response costs, the paper shows how the country level food insecurity estimates can be combined with response costs assumptions to assess temporal trends in development assistance needs. The different predictions work in tandem to decompose projected development assistance needs into the combined effects of changes in the prevalence of severe food insecurity, continued growth in the overall population, and changes in the per capita response costs. In the baseline forecast, the prevalence of severe food insecurity is projected to drop by about 0.5% points by 2025 from 2021-2023 highs, but the combined effect of continued population growth and elevated commodity prices would still lead to a doubling in development assistance needs compared to pre-pandemic
.9|32.9|9.2|
|II|15.5|21.1|20.6|14.3|25.1|9.8|
|III|19.5|21.8|21|20|18.4|16.4|
|IV|24.7|21.3|19.6|25|11.5|21.6|
|V|30.3|19.4|17.7|31.9|12.2|43.1|
|||**Russia**||**Serb**|**ia and Monten**|**egro**|
|I|10.9|13.1|17.6|21.5|14.6|27.5|
|II|17.6|17.8|15|19.2|20.5|20.2|
|III|20.8|20.9|16|19|24|14.6|
|IV|23.1|24.1|17.9|19.1|26.6|21.1|
|V|27.7|24.1|33.6|21.3|14.3|16.6|

_Source_ : Household surveys (Table 1). _Note_ : Urban apartment sector, income proxies (for definitions, see data descriptors in Table 1).

In contrast, low-income households in Armenia, Romania, and also Lithuania<sup>55</sup> are strongly over-represented in the urban public rental sector (see also Figure 8 for Romania). However, this empirical result reflects the sector’s marginalization after the almost completed massive privatization rather than an outcome of purposeful public policy. Still, even in these countries some income-rich tenants remain in the public stock and continue to pay very low
.K. (Stanko et al., 1998). To the best of our knowledge, there are only two such direct cost studies for gender-based violence in developing countries. Mansingh and Ramphal (1993) estimated that the direct costs of treating victims of intimate partner violence in Kingston Public Hospital (Jamaica) totaled U.S. $454,000 in 1991 (in 2001 dollars). Sanchez et. al. (2004) found that the Colombian national government spent approximately 184 billion pesos (U.S. $73.7 million) in 2003 to prevent, detect and offer services to survivors of family violence-an amount equal to approximately 0.6% of the total national budget.

Direct cost estimates are problematic in developing country contexts, where the lack of services (or serious under-funding of services) generally means that direct cost estimates associated with gender-based violence will be low, giving a mistaken impression that that the problem is not important. In such settings, indirect cost estimates may be more useful than direct cost estimates. Indirect cost estimates have typically focused on foregone earnings due to death and lost productivity (CDC, 2003), job loss, lost productivity of women victims, lost productivity of abusers due to incarceration, and mortality (Laurence and Spalter-Roth, 1995); loss of tax revenues due to death and incarceration (Greaves et al., 1995), and decreases in women’s earnings (Morrison and Orlando, 1999; Sánchez et al., 2004). For example, Morrison and Orlando (1999) estimated that lost wages due to family violence amounted to 1.6 and 2.0% of GDP in Nicaragua and Chile, respectively, by estimating earnings equations of the determinants of women’s earnings. Using a non-parametric matching methodology to analyze DHS data from 1995, Sánchez et al. (2004) found that Colombian women who suffered physical violence had 14% lower earnings than women who did not suffer violence. Using more recent data from 2003, they estimated
- c. A household can be a duplicate if the head of household’s name, with the same characteristics, shows up more than once on the household list targeted to be interviewed in an enumeration area. In this case, only interview the household with the smallest number and replace the other household.

- d. A household cannot be reached after four hours. This could happen if all household members are out of town, or adult household members are too busy to meet.

- e. A household on the preprinted data listing is unknown to village authorities and villagers.

All replacements must be authorized by a supervisor.

# **5. DATA COLLECTION**

This section provides information about the datasets used in the evaluation and compares the characteristics of the treatment and control communities. We also examine the data for differential sample attrition across program and comparison villages.

A baseline survey was conducted in both treatment and control communities in August – September 2008. A total of 2,087 households in 160 sub-villages were interviewed. A health nutrition module to record recent illness for children under 5 years of age was also administered, providing a baseline sample of 2,353 children.

The follow-up data collection was conducted approximately 24 months later, between November 2010 and February 2011. The endline survey used the same field methodology as the baseline survey. The final sample size was 2,500 households. Of these, 1,908 were also surveyed in the baseline and so constitute our “panel sample” (see Table 1).

# **6. VALIDATION OF EVALUATION DESIGN**

# _6.1 Baseline Comparison of Means of Tests for Balance_

We use the 2008 baseline survey data to compare characteristics of treatment and control groups. Table 2 compares the means of selected key variables across treatment and control groups. Randomization aims to minimize systematic differences between the two groups. Table 2 shows that the means of the variables are similar in magnitude for the two groups and we cannot reject that they are equal for most
|
|Tobago|||||assessment; 1985-90,<br>Bank projection|
|Tunisia|Mar84|6966 F|Bank projection from census|Bank est.|BasedonU.N. 1984<br>assessment|
|Turkey|Oct 85|50664 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessmen.|
|Turks andCaico<br>Islands|sMay80|7 F|UNPVSR<br>1/88 assessment|||
|Tuvwlu|May 79|<br>7 F|UNPVSR<br>4/87 (official<br>est.)|Bank est.|Bank est.|
|Uganda<br>|Jan 80<br>|12637 F<br> <br>|Official<br>eat.<br>|U.N. 1988 revision (prelim.)|Bankest.|
|United Arab<br>Emirates|Dec 80|<br>1043 F|U.N. 1988 revision (prelim.)|BasedanU.N. 1988 revision<br>(prelim.)|Based on U.N. 1984<br>assessment|
|United Kingdo|m Apr 81|55678 F|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|United States<br>of America|Apr 80|226546 F|UNPVSR<br>1/87 (official<br>est.)|Based on 1981-84 TFR, USBOC<br>1985, and 1985 CBR, UNPVSR<br>4/87|Based on UNPVSR<br>4/81|
|Uruguay|Oct 85|<br>2931 F|Bank projection<br>from<br>official<br>1986 est.|1980-85,<br>Celade printout<br>2/87; 1985-90, Sank<br>projection|
advice based on relationships observed from data in other countries may be quite risky, if not misleading, unless the same relationship is also validated with country-specific data. DML estimates from the Republic of Congo suggest that HAZ and stunting have a direct causal effect on early childhood development. In contrast, in São Tomé and Príncipe only HAZ has a positive effect on the ECD index, but otherwise the growth measures do not have a relationship with child development outcomes. This is very much in line with the conclusion of Tran et al (2019) that “...overall, growth is not a sensitive and therefore suitable indicator of child development.” The reasons for the absence of a relationship (causal or not) between stunting and child development outcomes in São Tomé and Príncipe are not clear, but certainly deserving of more investigation.

**Table 6: Summary of the effects of a child stature and maternal education on early childhood development based on the DML approach**

||**Dependent variable: ECD**<br>|**Index(standardized)**<br>|
|---|---|---|
||**Republic of Congo **|**São Tomé & Príncipe **|
|**Specification A**|||
|Child HAZ score|Positive effect|Positive effect|
|Mother’s years of education|No direct effect,<br>(but indirect effect through improved<br>child stature)|Positive effect<br>(no effect on child stature)|
|**Specification B**|||
|Whether child is stunted|Negative effect|No effect|
|Mother’s years of education|No direct effect,<br>(but indirect effect through improved<br>child stature)|Positive effect<br>(no effect on child stature)|
||**Dependent variable: Binary variable(0,1)**
-|---|---|---|---|---|
|Vl USD 1000|<sup>DRC-RC</sup>|22,693|67,312|89,401|106,978|
|aue ( ,)|RC-DRC|0|0|n.a.|17|
|Wih|DRC-RC|22,168|52,436|84,129|106,256|
|egt (tons)|RC-DRC|0|0|4,000|11,160|
|Shi|DRC-RC|370|797|1,543|2,022|
|pments|RC-DRC|0|0|1|2|

Source: DGDA.

**Formal transit flows predominantly run from Kinshasa to Brazzaville** (see Table 4). Brazzaville is partially supplied through Matadi and Kinshasa due to the poor condition of the rail and road infrastructure in RC. Rail freight fares from Pointe Noire to Brazzaville are 90 to 165 percent higher than average tariffs per ton-kilometer charged by other African rail concessions (Pozzo di Borgo, 2008), and the road can only be passed during the dry season. There are also reports of sporadic military threats from former rebels. Work is being undertaken to repair the Pointe Noire-Brazzaville road. This is scheduled for completion in 2012. The RC government is furthermore preparing the privatization and repair of the railway from the coast to the capital.

**Port freight data point to customs underreporting but confirm general patterns of DRC-RC trade.** Volumes at the port of Brazzaville had peaked in 1984 with 590,000 tons (of which 51 percent was wood from upriver) but amounted to a mere 373,000 tons in 2009 (of which 8 percent was wood, see Table 5). At the same time, the volume of official imports from the DRC to the RC, and data on transit flows through the DRC only add up to 101,000 tons
firm.|3<br>(11)|0<br>(0)|3<br>(15)|
|**E.**<br>**Type Of Financial Conglomerate Structures Involving Banks That**<br>**Are Allowed**|**% Yes**|**% Yes**|**% Yes**|
|Conglomerates whose parent is a bank.|60|50|64|
|Conglomerates whose parent is a non-bank financial institution (e.g. insurance<br>company or securities firm).|22|26|20|
|Conglomerates whose parent is a non-financial institution (e.g. non-operating<br>financial holdingcompany).|44|59|37|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

31
effectively as a lender of last resort.

The purpose of this paper is to determine empirically the causes and significance of currency substitution in Latin America, as well as to explore some of its macroeconomic consequences in the last decade. Using panel data for thirteen Latin American countries from 1990 to 1998, we estimate a static (one-period) portfolio balance model, where optimal bank deposits denominated in both local currency and dollars are chosen simultaneously. Our results reveal that the dollar deposits to broad money ratio is strongly influenced by depreciation expecta'In the current debate of adopting the dollar as legal tender, a new distinction has developed: unilateral full dollarization, meaning a country adopts the U.S. dollar on its own, versus the unlikely but theoretically feasible multilateral agreement of an economic block with the U.S. dollar as the common currency. 2A good discussion of seigniorage losses due to the adoption of a foreign currency is provided in Fischer (1982).

3
-110-

# Table 1

This table gives a summary English language presentation of basic data on output, value added and employment levels for the three countries. For the USA the 1977 figures were derived from the General Summary of the 1977 Census of Manufactures (US Dept. of Commerce, 1981a), and the 1975 figures from the Annual Survey of Manufactures 1975-1976 (US Dept. of Commerce, 1979), for Brazil from the Censo Industrial: Brasil (IBGE, 1981a), and for Mexico from the X Censo Industrial 1976, Datos de 1975 (SPP, 1979a). None of these countries follows the internationally standardised ISIC classification. Gross value of output in the USA refers to value of shipments of all products which are produced by the establishments classified in that industry (including interplant transfers within the company to which the establishment belongs). Brazilian and Mexican gross value of output refer to production plus net inventory change. Chapter II explains the different concepts of value added. The employment figures refer to average number of employees for the year.

## Tables 2, 3, 4

The basic census information on production in physical terms and gross value of shipments (in national currencies) is given in tables 2 (USA), 3 (Brazil) and 4 (Mexico). The tables also show unit values derived from the census listings. English translations are provided for all specifications in Brazil and Mexico, which were originally in Portuguese and Spanish respectively. In the very small number of cases in which we were unable to find proper translations, the original specifications are shown between inverted commas. The total census values for some industries are sometimes different from the total we obtained by summing the values of specified items. In these cases we show both the "Census Total" and "Our Total" in tables 2, 3 and 4. Virtually all of these differences are very small. The only big discrepancies are for the Brazilian motor vehicle and petroleum refining industries, where information was probably withheld because of confidentiality
shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents. * Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time Data source: PSF Survey, waves 1 and 2 (2006-2007 and 2010-2012)
Policy Research Working Paper 10336

# **Abstract**

Forest fires are among the main drivers of deforestation and forest degradation in the drylands of Sub-Saharan Africa. This paper uses remote sensing data on forest fires and remaining tree cover to estimate the effectiveness of a project targeted at reducing fire incidences in twelve protected forests in arid Burkina Faso. The project consisted of two components that were implemented in the villages surrounding the target forests: a campaign aimed at raising community awareness about the detrimental effects of forest fires, and a program to support establishing and maintaining forest fire prevention infrastructures. Using the Synthetic Control Method the paper finds that the project resulted in a 35% reduction in forest fire occurrences in the period of the year when they tend to be most prevalent —in November, at the very end of the agricultural season. However, this impact is short-lived (as the reduction only occurred in the first four years of the program). The reduction in forest fires also did not result in a detectable increase in vegetation cover—because the reduction in November was not sufficiently large to be captured via remote sensing, or because the duration of the reduction was too short for the vegetation to recover. The paper then tries to uncover the underlying mechanisms to shed light on which of the project’s components were effective and to also learn how the program can be improved.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at d.p.vansoest@tilburguniversity.edu. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of
_The Cultural Trade Index_ Page | **2** of **14**

# **I. Introduction**

Trade in cultural goods and services is among the most opaque areas in economic statistics. What is the contribution of cultural tourism to Colombia’s economy? How much do art crafts contribute to Ghana’s gross domestic product (GDP)? Who knew that since 2010 Vietnam has been among the fastest-growing piano exporters, reaching more than 400 percent in 2014?<sup>1</sup> If the United States is the largest global music market, how much do its music imports add to employment in places where the music is imported? Answers to such questions are not easy to find, nor are they the easiest to begin to tackle. Cultural commerce feeds into the GDP and the gross national product (GNP),<sup>2</sup> even though questions about sampling and the treatment of intangibles abound. Yet its contribution to economic development is less understood. What is more, although GDP is a key economic indicator — and improvements in <mark>capturing investment in “artistic originals” mean that GDP could be a better measure for such investments anyway —</mark> it does not measure services well, and, needless to repeat, it falls short on capturing wellbeing.<sup>3</sup> Such limitations have in part encouraged other indices to crop up. These include the Human Development Index (HDI), the Gross National Happiness (GNH), the Global Innovation Index (GII), and the Social Progress Index (SPI). There is also the Measure of Economic Welfare (simply called MEW), the Genuine Progress Indicator (GPI), the Better Life Index (BLI), and so on.

What is common among these indices is that they attempt to enrich the development debate. Along those lines, it is worth exploring the creation of a Cultural Trade Index (CTI), an index that would
**Figure 3. Average industrial tariffs in industrial & developing ctries, % Simple Average**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20 Developing<br>Industrial<br>15<br>10<br>5<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: TRAINS data accessed 23 January 2011 using WITS. Specific tariffs converted to _ad valorem_ using UNCTAD Method 1. Simple average of applied rates, WTO Agricultural Goods and WTO Non-Agricultural Goods.

Ng and Martin (2004) examine the sources of the decline in overall developing-country tariffs and conclude that the overwhelming share of the reduction in these tariffs resulted from countries’ own reforms, with liberalization under the auspices of the WTO the second-most important cause, and liberalization under the much-discussed preferential trade agreements coming a poor last. Part of the unilateral liberalization seems likely to be attributable to the lessons learned in the 1970s and 1980s, when countries with restrictive trade regimes found it much more difficult to adjust to major trade and financial shocks than the countries with more 11
.16|5491.54|11051.33|9708.87|13507.27|
|<br>Value of livestock (excl oxen)|3622.00|3256.67|4356.81|4326.31|3777.50|5561.12|3005.10|2768.20|3438.48|
|No. of adult children|1.34|1.37|1.27|1.40|1.39|1.42|1.28|1.34|1.16|
|A member held /party position|0.24|0.20|0.32|0.23|0.20|0.32|0.24|0.20|0.31|
|<br>Member of service coop|0.20|0.17|0.27|0.20|0.18|0.24|0.21|0.17|0.29|
|Member of microfinance inst.|0.14|0.10|0.24|0.13|0.09|0.22|0.16|0.10|0.25|
|Number of observations|1587|1060|527|741|513|288|846|547|299|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07.

18
: Encuesta de Hogares, 2000

> 2 Despite the above shortcomings, it should be noted that the Colombian formal education system expanded greatly during the last two decades. According to the World Development Indicators 2002, preschool gross enrollment grew from 11 percent in 1985 to 35 percent in 1998, primary net enrollment from 66 percent to 87 percent, secondary gross enrollment from 44 percent to 53 percent and tertiary gross enrollment grew from 11 percent to 22 percent. However, 87 percent primary enrollment is not impressive in a region where universal primary education is the standard, and the improvements only benefited today’s young generation.

4
# Poverty Alleviation through Geographic Targeting: How Much Does Disaggregation Help?

## Chris Elbers, Tomoki Fujii, Peter Lanjouw, Berk Özler, and Wesley Yin<sup>1</sup>

In this paper, using recently completed “poverty maps” for Cambodia, Ecuador, and Madagascar, we simulate the impact on poverty of transferring an exogenously given budget to geographically defined sub-groups of the population according to their relative poverty status. We find large gains from targeting smaller administrative units, such as districts or villages. However, these gains are still far from the poverty reduction that would be possible had the planners had access to information on household level income or consumption. Our results suggest that a useful way forward might be to combine fine geographic targeting using a poverty map with within-community targeting mechanisms.

**Key Words:** Targeting, Poverty, Poverty Maps.

**JEL Classification Numbers:** C15, I32, H53.

### World Bank Policy Research Working Paper 3419, October 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Elbers is with Free University, Amsterdam; Fujii is with University of California, Berkeley; Lanjouw and Özler are with the World Bank; and Yin is with Princeton University. The authors are grateful to Martin Ravallion, Norbert Schady, and participants in the “Poverty and Applied Microeconomics” seminar series at the World Bank for useful discussions and comments. Corresponding author: Berk Özler