rafmacalaba/data-use-mentions-extended huggingface dataset:
|**_Theme_ / Layer**|**Description**|**approximate grid**<br>**size**|**Source**|
|---|---|---|---|
|irrigated<br>~~c~~rop<br>~~s~~h|Share of area with irrigated<br>crop|1km2|GFSAD1000<br>V1.0: (Telu-<br>guntla et al.<br>2014)|
|agr<br>~~o~~ppt<br>~~c~~ost|Global map of gross eco-<br>nomic rents from agricul-<br>tural lands (2000 US dol-<br>lars per hectare per year)|10km2|Naidoo and<br>Iwamura (2007)|
|_Climate_||||
|bio1|Annual Mean Temperature|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|bio4|Temperature Seasonality<br>(standard deviation *100)|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|bio12|Annual Precipitation|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|bio15|Precipitation Seasonality|1km2|Bioclim (Hi-|
||(Coefcient of Variation)||jmans et al.<br>2005)|
|bio16|Precipitation of Wettest|1km2|Bioclim (Hi-|
||Quarter||jmans et al.<br>2005)|
|bio17|Precipitation of Driest<br>Quarter|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|oxmap<br>~~d~~aylst|Day time land surface tem-<br>perature (Celcius)
these proxies are then entered linearly in an equation that predicts a variable of interest. However, there is no reason to believe that the scales are designed to detect material effects of the determinant on the variable of

> America’s agriculture is conducive to small farms, so that more egalitarian institutions emerged. We focus on the AJR (2001) measure of settler mortality and latitude and not on agricultural endowments because the data on settler mortality and latitude is available for a broader cross-section of countries.

> 5 Since we need a categorical variable to do variance decomposition, we use dominant religion dummies calculated from the percentage of the population of each religion within each country instead of using the percentages directly.

> 6 As noted by Alesina et al (2003), ethnic fractionalization has also been found empirically to predict lower levels of trust, less efficient public services and less favorable economic outcomes in US localities, 7 In an earlier version of the paper we used a measure of ethnic fractionalization from Easterly and Levine (1997) that Alesina et al (2003) show relies largely on linguistic distinctions.

7
-0.332<br>[0.0306]***|
|Observations|2,909,416|2,909,416|2,909,416|

Sources: 2002 and 2018 National Census of Guatemala (INE).

Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves and who live in municipalities appearing in both censuses (331 municipalities). Individual characteristics include age and its squared, dummies of educational level and an indicator variable of belonging to an indigenous group. Household characteristics include a dummy of being married, number of children ages 0 to 3, ages 4 to 6 and ages 7 to 12, number of household members 13 years old or older, presence of grandmother, household headship. Economic situation includes an indicator of whether the spouse works, an indicators of income stability, and a household & WASH index. Local labor market variables include the share of male employment by economic sector. Geography factors include an indicator of urban area and department fixed effects. Table A1 in the Appendix provides details on variables’ definitions.

27
This is an important point as the _methodologically consistent_<sup>procedure for</sup> fixing the non-food basket does not produce a _welfare_<sup>_consistent_ranking. That is,</sup> we know from basic consumer theory that method I, using<sup>a Laspeyres index of food</sup> and non-food inflation rates based on actual consumption<sup>shares of the poor, should</sup> _overstate_ the welfare impact of those at the poverty line because<sup>it does not allow for</sup> consumers response to changing relative prices by changing their<sup>consumption</sup> patterns. As is well known, the Laspeyres index will exceed<sup>the "exact" inflation</sup> rate from an expenditures function since the increase in the amount<sup>of money</sup> expenditures needed to reach the same level of utility is lower<sup>when one allows for</sup> substitution across commodities (e.g. buying relatively<sup>less of items whose prices</sup> increased).

Method II (using the poverty basket food share) also overstates<sup>poverty</sup> increases, not only (as in Method I) because it does not allow<sup>substitution but also</sup> because it does not use the actual consumption bundle of<sup>households at the poverty</sup> line. But method II is perhaps defensible as the food weight in<sup>the price index</sup> represents the actual consumption pattern of some group in poverty<sup>(although a</sup> group considerably below the poverty line).

However, poverty line inflation rates, such as that of method III,<sup>that expand</sup> the 1996 inflation line to 1999 by more than the amount<sup>of method II are creating a</sup> poverty line at which the welfare
- _59-_

as the Police Force, under the control of the Ministry of Defense (80,000), the National Guard (15,000), and the Home Guard (15,200).

GDP and Consolidated Central Government wages and salaries are for 1994 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) is taken from the United Nations' Statistical Yearbook for Asia and the Pacific 1995 and refer to 1993.

# Thailand

Unemployment information is for 1995 and is taken from IMF Report No. SM 96/155 of June 28, 1996 and relates to 1995 (projected). Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Central and local government data are staff estimates based on information provided by Embassy of Thailand, several Public Expenditure Reviews and the Country Economist of Thailand, Sudhir Shetty. They are for the year 1992. Data on education and health are taken from UNESCO Yearbook 1995 and WHO Yearbook, 1993 and relate to the year 1993. Military employment data do not include paramilitary forces, e.g., Thahan Phran (18,500), the National Security Volunteer Corps (50,000) the Marine Police and Police Aviation (2,500 and 500 respectively), the Border patrol police (40,000) and the Provincial police (50,000).

GDP estimate is from IMF Report No. 96/83 of August 1996 and relates to fiscal year 1995/96. Wages and salaries are taken from IMF Report No. 96/83 of August 1996 and relate to 1995/96 (projections). Average wages in Manufacturing are taken from IMF Report No. 96/83 of August 1996 and relate to 1995.

Vanuatu Data on paid employment in non-agricultural activities are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1989. Unemployment data are taken from the United Nation'
<mark>Source: authors</mark> <mark>similar rates of social-gaps to Gauteng in South Africa. The incidence of multidimensional social gaps in South Africa ranges from 46 to 82 percent and its intensity from 45 to 56 percent. In Peru this spread is narrower for incidence (56-81 percent) and for intensity (44-50 percent). Puno in Peru and North West in South Africa are the regional units with highest social-gap rates (Figure 4).</mark>

**<mark>Table 4: Social Sustainability Index (SSI), Incidence (H) and Intensity (A)</mark>**<sup>**12**</sup>

# **<mark>Panel A: Peru</mark>**

|<sup>**(a)**</sup>|**Nil**||**Gende**|**r**||||**Ethnici**|**ty**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Metric**|**atona**|**Male**|**Female **|**T-test**<sup>**(b)**</sup>|**Quechua**|**T-test**<sup>**(b) **</sup>|<sup>**Aymara **</sup>|**T-test**<sup>**(b)**</sup>|**Other**<br>**Indigenous**|<sup>**T-test(b)**
study methodology, we examine the role of different policy initiatives on addressing the stress to banks. To better understand the impact of the measures implemented by monetary and supervisory authorities, we exploit the cross-sectional variation in the stock price of banks. In other words, we are interested in both the aggregate response of bank stocks to a particular announcement, as well as the differential effect across banks with different characteristics, such as size, liquidity, ownership and others.

We use bank data including stock prices, balance sheets, and ownership, for 53 countries covering 896 commercial banks. We first document a systematic underperformance of bank stocks at the onset of the COVID-19 crisis, between March and April of 2020. More precisely, for most countries, bank stocks underperform relative to other publicly traded companies in their home country, and relative to non-financial institutions. The evidence highlights the nature of the COVID-19 shock, and the expectations of market participants that banks will experience deeper and more protracted profit losses than other firms. Even within the financial sector, banks are expected to face greater losses than other financial institutions.

The initial phase of the crisis was characterized by liquidity shortages, exacerbated by volatility in securities and foreign exchange markets. We show that banks with lower liquidity buffers experienced larger than normal price drops (particularly in March), revealing an increase in the interbank liquidity premium. In addition, the dual shock resulting from the Russia and Saudi Arabia price war further exacerbated bank stock price declines, particularly for lenders with large exposure to the oil sector.

To study the stock market reaction to different policy measures, we identify financial sector initiatives by government authorities from February 2 to April 17. The data was compiled and made publicly available by the World Bank (World Bank, 2020). Our final sample contains 389 3
The main contributions of this article lie in identifying and overcoming these challenges in order to construct a consistent and complete set of origin–destination matrices of international migrant stocks for 1960–2000, disaggregated by gender. The starting point is a master set of 226 origin or destination countries and regions. Despite border changes, all migrants are assigned to this master set so that migrations can be meaningfully tracked over time. These assignments, especially in cases where only aggregate data are available, are made using several alternative propensity measures based either on a destination country‘s propensity to accept international migrants or on an origin country‘s propensity to send migrants abroad.

Cases of omitted data occur when destination countries do not collect or publicly disseminate the information on migrants. When data from census rounds are missing altogether, the approach taken depends on the extent of the omission (see appendices 3 and 4). When sufficient data are available for other decades, interpolation is used. When not enough data are available, propensity measures are used to generate bilateral data. When a gender breakdown is missing, gender splits are calculated based on supplementary statistics or other data in the matrices (see appendix 5). The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds.

Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973,
>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: This figure shows the trends in the value of international investment positions and share of active links by block for portfolio investment. The value of positions distinguishes old and new links. Old links are those that have a positive value in 2001. New links are those that have a zero or missing value in 2001. By definition, there are no new links in 2001. Values are expressed in trillions of 2011 U.S. dollars (USD). The share of active links is the number of country pairs that have a positive value in each year over the number of country pairs with a positive or zero value in the same year. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
growth rate of GDP per capita (percent, annual); and the percentage of women in the 35-64 years age group relevant for managers. The data source for all these variables is WDI, World Bank.

We control for several firm characteristics that may be correlated with the gender of the top manager and vary systematically with credit supply. Direct evidence on differences between women- and men-led firms is limited to a few firm characteristics. These include labor productivity and growth rate of sales and employment (see Allison et al. 2023, Islam et al. 2020), firm size (see Fairlie and Robb 2009), age of firm (see Blum et al. 1994, Islam et al. 2020), exposure to international markets (see Marques 2015, Haddoud et al. 2021), gender composition of the owners (Sekkat et al. 2015), and gender composition of the workforce (Hurley and Choudhary 2016). We go beyond and account for additional factors such as, quality of the regulatory and business environment that may have gendered effects on firm functioning (see Vershinina et al. 2022. Hanousek et al. 2019), and therefore may affect the choice of woman vs. man top manager.

Based on the discussion above, the baseline firm-level controls include the following (all taken from WBES): firm size proxied by log of number of workers employed at the firm 3 fiscal years ago (lagged) and a dummy variable equal to 1 if the firm is part of a larger establishment and 0 otherwise ( _Multi establishment firm_ ); log of age of the firm; labor productivity equal to log of the ratio of annual sales (in 2009 USD) to the number of workers three fiscal years ago; firm growth proxied by the average annual growth rate of employment (log difference) at the firm over the last 3 fiscal years; dummy variable equal to 1 if the firm purchased fixed assets during the last fiscal year and 0 otherwise; dummy variable equal to 1 if the firm
log expenditures is 10.90, resulting in a ratio of 0.0566.10 This implies that for a typical person in this sample, if the headcount poverty rate is 20 percent, the probability of at least one episode of apparent poverty in three years is 58 percent, while the probability that this person will be in poverty at least once in three years is 41 percent. To be "invulnerable" to poverty when facing such large variability in expenditures requires a level of log expenditures of at least 10.967 (which is called "Vulnerability to Poverty Line"), which is even higher than the mean (log) expenditures of 10.90. Because of this, 59 percent of the population is vulnerable to an apparent episode of poverty, almost three times of the currently poor." The level of vulnerability in 100 Village Survey sample is lower because the coefficient of variability is smaller. With a coefficient of variability 0.0345, the proportion of vulnerable population is 42 percent, about twice the poor.

This is interesting that the Mini-Susenas shows a _higher_ level of variability in expenditures, as this panel did _not_ span the worst of the crisis. One would have thought that the 100 villages data, by being conducted before and after the worst of the crisis would have much larger variability. There are a couple of possible explanations. First, we have done nothing about seasonality-because we can't. So perhaps the May to August comparison has less seasonal fluctuation than December '°This is a handy ratio, but it is not the "coefficient of variation" as this is the ratio of the standard deviation _offirst differences_ to the average of the _level._ We call this ratio the "coefficient of variability." " One way of thinking about this vulnerabili **t** y opoverty is a fornalization of the concept of "near poor," which is often used to illustrate the sensitivity of poverty rates to the poverty line. Vulnerability defnes "near poor" using a
the status of bank lending to SMEs are particularly relevant for the Middle East and North Africa (MENA) region. As shown in Figures 1b and 1c, enterprise-level surveys conducted by the World Bank suggest that SMEs are particularly financially constrained in MENA countries – only 20 percent of SMEs in MENA have a loan or a line of credit, a lower share than any other region, and only 10 percent of their investment expenditures are financed by a bank loan, a share that is higher only to the one in Sub-Saharan Africa. These results have motivated the design and implementation of a survey of bank lending to SMEs in the MENA region that complements the information provided by enterprise surveys and provides further insights into the challenge of enhancing SME access to finance.

The objective of this paper is to report the results of a joint survey of the Union of Arab Banks and the World Bank (henceforth the MENA survey), including not only the bank responses but also the statistical analysis of the dataset. The MENA survey was conducted between December 2009 and April 2010 and secured a high response rate across the region. It draws on material from the two previous surveys conducted by the World Bank, thus allowing for comparisons with previous results. However, the MENA survey also contains new material, designed to address the specificities of the MENA region and provide more information and granularity to

> 1 According to Ayyagari et al., (2007) SMEs account for more than 60% of manufacturing employment across 76 developed and developing economies.

> 2 Schiffer and Weder (2001), IADB (2004) and Beck et al. (2005, 2006 and 2008) show SMEs perceive access to finance and cost of credit to be greater obstacles than large firms and these factors affect their growth.
in areas surrounding Kyiv and Kharkiv by the Russian army. The share of VCs experiencing crop damage then dipped below 1% in periods 5 and 6, while surpassing it (with 1.4% and 1.1%) in periods 7 and 8. In affected villages, the intensity of fighting was about equal in periods 2 and 4, damaging about 450 ha per affected VC before subsiding and then increasing again in periods 7 and 8.

<mark>In addition to image-based data on direct damages, information on whether VCs were o</mark> ccupied, a scene of active conflict, or counted with Russian troop presence <mark>is available online based on daily situational reports from local and regional military administrations at the level of settlements.</mark><sup>12</sup> <mark>We draw on these sources to compute indicators of relevant parameters including a dummy for any conflict activity. Columns 3-6 of</mark> table 1 show that by phase 4, 18.4% of VCs had been affected by any conflict activity. As subsequently the conflict entered a more static phase with active fighting concentrated in the East of the country, information on the location of fighting or troop presence shows less change over time and information on field damages provide a more informative signal.

# **3.2 Winter crop cover**

To provide training data for generation of crop cover estimates using machine learning techniques, _in-situ_ data collection along main roads following standard guidelines (Waldner _et al._ 2019) was undertaken yearly from 2019-2021. In each year, two extended field trips, one for the winter and one for summer crops, were conducted (see figure 1 for route maps in each year). While conflict conditions

> 12 Open source conflict monitoring data (https://novy.tv/ru/news/2022/06/02/karta-bojovyh-dij-v-ukrayini/ and <u>https://liveuamap.com/ru/time).<
J. M. C. (2015). Upgrading a Piped Water Supply from Intermittent to Continuous Delivery and Association with Waterborne Illness : A Matched Cohort Study in Urban India. _PLOS Medicine_ , _12_ (10), 1–24.

https://doi.org/10.1371/journal.pmed.1001892 Erman, A. E., Motte, E. G., Goyal, R., Asare, A. B., Takamatsu, S., Chen, X., ... Hallegatte, S. (2018). The 24
[0.00214]***<br>|.<br>[0.00319]***<br>|.<br>[0.00350]***<br>|
|Spouse works|0.00313<br>[0.00194]|0.00397<br>[0.00483]|0.00366<br>[0.00480]|0.00312<br>[0.00191]|0.00379<br>[0.00481]|0.00301<br>[0.00457]|
|Income stability|0.124|0.128|0.128|0.124|0.128|0.129|
||[0.00819]***|[0.0182]***|[0.0184]***|[0.00816]***|[0.0183]***|[0.0184]***|
|At least one person receives remittances regularly|-0.0273<br>***|-0.0289<br>***|-0.0295<br>***|-0.0268<br>***|-0.0293<br>***|-0.0283<br>***|
|Household Home & WASH index|[0.00332]<br>0.000312|[0.00654]<br>-0.000279|[0.00669]<br>-0.00142|[0.00328]<br>0.000604|[0.00637]<br>-0.00185|[0.00633]<br>-8.60E-05|
|Urban|[0.00277]<br>0.0211|[0.00500]<br
**Table 5. Sources of data**

|**Data**|**Source**|
|---|---|
|Production, consumption and stocks<br>in each region|<br>U.S. Department of Agriculture’s Production, Supply<br>and Distribution Data base(1)|
|Domestic price of grains, sugar and<br>oilseeds|Food and Agriculture Organization of the United<br>Nations(FAO) (2)|
|World energy price|International Monetary Fund Primary Commodity<br>Prices(3)|
|Biofuelproduction and consumption|Renewable Fuels Association(4)|
|Exchange rates|U.S.Federal ReserveStatistical Database(5)|
|Price and income elasticities of<br>supplyand demand for crops|Food and Agricultural Policy Research Institute<br>ElasticityDatabase and USDA elasticitydatabase(6)|

1. http://www.fas.usda.gov/psdonline

2. http://faostat.fao.org/ 3. http://www.imf.org/external/np/res/commod/index.asp 4. http://www.ethanolrfa.org/industry/statistics/#E 5. http://www.federalreserve.gov/releases/G5A/ 6. http://www.fapri.iastate.edu/tools/outlook.aspx Finally, to test the robustness of the inventory demand parameters, we simulate a fourth scenario using Carter et al.’s estimates for the inventory demand function as opposed to our own. Note that different from us, Carter et al. [14] estimate the inventory demand based on U.S. data for 2006 through 2008, while we use world data for 2001 through 2008.

# _6.7. Sources of data_

The various data sources are shown in Table 5. Data on production, consumption, beginning and ending stocks
|1993|33,081 - -|7,224 - -|
|---|---|---|
|1997|39,714 1,701 4.28|7,699 1,304 16.94|
|2000|54,991 2,835 5.16|10,435 2,022 19.38|
|2007|73,016 6,352 8.70|13,536 3,787 27.98|

Table 2 shows the gender composition of these migration flows: almost two thirds of those recorded as migrants in 2007 are men, but women are more than twice as likely as men to migrate internationally, and this is particularly true for mothers who leave their family behind. On the other hand women, especially those who leave their children behind, tend to stay away for a period of time that is significantly shorter than that of men.<sup>12</sup>

**Table 2.** Migrants by Gender. IFLS 2007.

||Number|% of total<br>migrants|% international|Migration spell<br>(months)|
|---|---|---|---|---|
|Migrants|6,352|-|11.04|62.89|
|Men|4,046|63.70|7.74|64.83|
|without children|3,255|51.24|7.74|64.10|
|with children along|493|7.76|2.44|78.23|
|withchildren left behind|298|4.69|16.55|51.23|
|Women|2,306|36.30|17.00|59.48|
|without children|1,892|29.79|13.27|59.76|
|with children along