rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Rice Market**

# _Supply_

The supply of rice to the Sierra Leone market comes from three primary sources; (i) domestic production from the five rice ecosystems, (ii) commercial imports and (ii) donations (food aid). Domestic production of milled rice has averaged 620,000 metric tons over the last ten years to 2018. According to the 2015, Comprehensive Food Security and Vulnerability Analysis, more than nine out of every 10 rice farmers reported that they sold a portion of their rice harvest to generate income. This is despite the fact that a clear majority of farmers (96 percent) operate at the subsistence level. It has been argued that the subsidization of imported rice is a major disincentive to farming household producing consistent surpluses, as they cannot compete with the price for imported rice.

Empirical work suggest that the supply of rice is elastic with respect to price. Conteh et al (2012) used an Ordinary Least Squares (OLS) log model with a small sample (2001 -2010) to analyze the domestic supply response to price as well as the effects of the price of imported rice. They found a supply response elasticity with respect to price of 1.14. Although imported rice is normally considered a substitute for domestic rice and _a priori_ , the elasticity coefficient is expected to be negative, the coefficient for the price of imported rice was 0.69. Their plausible explanation for the positive coefficient is that increase in domestic production of rice (which does not meet the quality standard of the urban population) does not cause a decline in the quantity/volume of rice imported into the country, since the urban population depend on the imported rice for their consumption. Kargbo (1979) also used OLS technique with a single equation model to estimate the supply response to price. The analysis is based on data from 1961 to 1976. He found an inelastic supply response with elasticities of 0.178, 0.376 and 0.349, respectively for the
became available. Our total sample comprises of 57 rounds of data across 36 countries amounting to over 94,000 individual-level data points (Table A1). Our experimental results focus on five of these 36 countries that are located in Sub-Saharan Africa.

The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank’s LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.

The source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated.<sup>3</sup> It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors. The Our World in Data COVID-19 vaccination dataset has been extensively used during the pandemic, for example to supply the data for the WHO’s official COVID-19 dashboard, by global media outlets, and for social science and epidemiological research (Mathieu et al. 2021). The dataset is publicly available through the Our World in Data GitHub repository.<sup>4</sup> We additionally access a second source of administrative data stemming from the WHO’s COVID19 vaccination dashboard (WHO
households under this design, prices do not distort consumption by charging some households more than others, achieving the goal of economic efficiency. The recommended tariff design also leads to progressive prices: it generates an average gain of Rs. 4.5 to Rs. 10 for households at the lowest quintile of the income distribution and an average loss of Rs. 20 per month for households in the top quintile.

Due to the poor quality of outages information maintained by the utility, the relationship between household demand and supply quality is not considered in this paper. Although a future line of research can explore this relationship in greater detail, the proposed tariff design addresses a key feature of how supply is rationed across consumers during periods of load-shedding. Under current prices, a utility has a greater incentive to supply power to larger consumers during periods of rationing. As the per-unit price of electricity is higher for these users, during periods of scarcity, the utility earns more per unit of dispatch by supplying to these consumers rather than smaller consumers situated at lower consumption brackets. In contrast, a uniform energy price across consumers under the recommended design, takes away the incentive of dispatching power to larger consumers and rebalances the existing disparities in dispatch across users. To the extent that the utility does offer greater dispatch to high consumption households during periods of scarcity, under the recommended tariff plan, the share of power supply to rural areas will likely increase as rural consumption share is lower than urban areas. Thus, rebalancing of supply dispatches across households is an additional virtue of our proposed plan in addition to its efficiency, equity and cost-neutral properties.

Finally, the simulated results in this paper are only as good as the utility’s ability to send bills and collect revenues from households based on their actual monthly consumption. The analysis of the utility’s billing data set suggests recurring cases of “average billing”, in which the household’s bill amount was imputed based on its average half-yearly consumption rather than the cotemporaneous consumption. There could be
EM‐DAT provides data on over 22,000 mass disasters around the world since 1900.<sup>11</sup> We use for our analysis the ratio of damages to GDP (30‐year median) as the indicator of countries’ vulnerability to extreme weather events. Even though this is a backward‐looking measure which is silent on the attributability of these events to climate change, it is a good indicator of countries’ exposure to the impact of climate change through increased frequency and intensity of extreme weather events.

Floods and storms are the most frequent severe weather events, with the East Asia and Pacific region showing the highest number of events (Figure 5). EM‐DAT data show that since the 1980s 45 percent of all incidents were floods, while storms accounted for 34 percent. The rest of the extreme weather conditions such as droughts, heat waves, landslides and wildfires accounted for 20 percent of events (Figure 5B). While all regions faced an increase in extreme weather events since the 1980s, East Asia and the Pacific experienced considerably more disasters than any other region (Figure 5D). The second most affected region is Latin America and the Caribbean, closely followed by Europe and Central Asia.

As countries faced an increase in disasters over time (Figures 5A and C),<sup>12</sup> the associated fatalities fell around the globe, suggesting that countries adopted disaster response and emergency strategies to minimize the number of human casualties. Simultaneously, the total number of affected people (including those who became homeless or were injured) increased over time, though disproportionately more in EAP and SA, reflecting the overall large populations of those regions. Sub‐Saharan Africa also experienced a rise in total victims since the 1980s.

Extreme weather events result in material damages that also rose over time and were not equally distributed around the globe with EAP affected most. For instance, since the 1980s, median annual economic losses due to disasters totaled about US$18 billion in
207) (0.032) (0.055) (0.010) (0.025)<br>Net aid flow 0.178*** 0.072*** 0.015* 0.002 -0.001 -0.001<br>(millions of 2010 dollars) (0.024) (0.021) (0.008) (0.009) (0.001) (0.002)<br>Number of observations 3,894 3,402 3,894 3,402 3,894 3,402 3,894 3,402 2,602 2,365 2,602 2,365<br><!-- End of picture text -->

|||Table 4B:|Regression|results from|the full samp|le of153 cou|ntries and al|l years 1988-|2015||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||)|Q)<br>Numbero|8<br>fprojects|@)|6)|6<br>Numberof|0<br>HDprojects|8|©)|1)<br>ShareofH|dy)<br>Dprojects|(2|
|IBRD country|-0.571**|<br>-1.634*%**|<br>-0.680**|= -0.852**«|-0.189%**|<br> = -0.423***|<br>=-0.342*** =|-0.360***|-0.040*<<
collection to shorten the recall period (50x2030 Initiative, 2020). Higher frequency data collection over the phone can also be suitable for agricultural input and output prices (Hermosilla, 2017; Hoogeveen et al., 2014), though community- rather than household-level data collection is likely appropriate. Recall bias has also been documented in the livestock sector, with Zezza et al. (2016) providing evidence on recall bias in the measurement of milk off-take in Niger. The Global Strategy for Improving Agricultural and Rural Statistics successfully used phone surveys in the collection of livestock productivity data, also in Niger (Bako, 2018).

There are other agricultural variables to which similar reasoning applies, and which would likely benefit from higher frequency data collection by phone, though this has not been validated empirically. These include non-labor inputs such as fertilizers and pesticides as well as weather shocks such as droughts, flooding, extreme heat, or insect infestations, and the damage these shocks cause (Knippenberg et al., 2019). Some agricultural variables are generally static over a crop growing season, such as access to irrigation, land ownership, stocks of large ruminants, and more frequent data collection is unlikely to improve the quality of these variables, diminishing the possible benefits of collecting such information via phone.

Food consumption (expenditure) data could in principle benefit from more frequent data collection, particularly to temper the excess variation that may be associated with collecting data over a short time interval (Gibson and Alimi, 2020). Full-fledged consumption modules are however too time intensive to be suitable for implementation over the phone (Abate et al., 2021). More synthetic measures of consumption like dietary diversity indicators or food frequency questions can be adapted and collected 16
main contribution of this research is that in the short run, discretionary monetary policy in Egypt is somewhat circumscribed and constrained. However, if the Central Bank of Egypt were to make its decisions more transparent and pre-announce its policies, then velocity would be more predictable and monetary policy would be more potent and effective.

In an attempt to investigate whether Egypt should implement an inflation-targeting approach to pursue its monetary policy, the Central Bank of Egypt’s annual report for the year 2010 examined the stability of the money demand function. Using monthly data over the period July 1997–June 2009, this exercise starts by estimating the long-run co-movement vector based on the Johansen method. The model incorporated data on real GDP, 3-month Treasury bill rate, annual inflation rate, and an impulse dummy to account for the exchange rate regime change in 2003, while 315
41 Chi Minh City Statistical Office provides information on population in each district in the city and for the city as a whole. The most recent data we were able to obtain on poverty rates is from 2003 from the Inter-Ministerial Poverty Mapping Task Force work, as cited by the Asian Development Bank (2010).<sup>25</sup> We use this data, coupled with the Steering Center for Flood Control’s GIS map of buildings in districts and catchments to determine the number of poor and non-poor in each subcatchment. District population and poverty rates are listed in Table A.1.

The Ho Chi Minh City Statistical Office also provides information on tax revenues in each district in the city and for the city as a whole. Vietnam’s GDP in 2011 was estimated by the World Bank at US$106.4 billion and the ADB estimates that Ho Chi Minh City contributes 23% of this. The GDP for Ho Chi Minh City is then US$24.5 billion. We use this data, coupled with the Steering Center for Flood Control’s GIS map of buildings in districts and catchments to determine the economic value in each subcatchment. District revenue and GDP data are provided in Table A.2.

**Table A.1. 2010 population and 2003 poverty rates in Ho Chi Minh City and in districts in the study area.**<sup>**26**</sup>

||2010<br>Population<br>(millions)|Percent of<br>Total Ho Chi<br>Minh City<br>Population|2003<br>Poverty<br>Rates|
|---|---|---|---|
|_Ho Chi Minh_<br>_City Total_|_7.4_||_5.4%_|
|Binh Thanh|0.50|6.4%|5.0%|
|District 1|0.78|2
govermnent, or alternatively the local insurers could purchase additional insurance to protect against such basis risk. The latter arrangement, however, would likely increase the cost of coverage to the ultimate customer, and thus make the scheme less attractive. Catastrophe bonds could also be triggered based on reported losses/indemnification claims in the industry rather than weather indexes, however, investor appetite might lessen without direct knowledge of local insurance industry characteristics, hence the attractiveness of the easily verifiable weather indexed triggers. The basic elements of such a structure are outlined below:

**Scheme 2: Weather Indexed Catastrophe Bonds** - **Securitization of Catastrophe Risk**

**Objective: Establish easily measurable weather indexed insurance/bond contracts which would attract international investors given the risk-diversifying characteristics these have vis-a-vis global debt and equity markets, while permitting catastrophe risks to be spread into the broader capital markets.**

# **Financial Structure:**

**a. A multilateral development institution would examine the possibility of sponsoring, with the assistance of international investment and reinsurance companies the**

> **15** It should be clarified that "parametric" measures generally refer to physically measurable (parametrized) phenomena such as wind speed, rainfall, or earthquake intensity. The term "index" is usually used in the context of summarizing insurance/financial loss data across a certain region or a number of companies in the industry. In the case of temperature degrees, this can also be considered a special type of index since the temperature scale is a standardized measure. Similarly, with earthquake events, the Richter scale can be considered as a specialized index reflecting a number of factors contributing to that measurement. For risk securitization terminology though, direct physical data measurements are normally known as 'parametric' while the term 'index' is generally used for expressing aggregations of insurance dollar losses across a specified sector or region.
the secondary data source, primarily contains data on top incomes but generally not on lower incomes. Estimates of income inequality will be biased if computed using any single one of these databases. It takes a combination of the two to obtain consistent estimates of inequality. DB-1 commonly represents a household income or expenditure survey. For DB-2 researchers often look at tax record data, as is discussed in the introduction.

> 3Recently, the EU-SILC survey in some countries began using register-based information (including tax records) for some questions (J ̈antti et al, 2013). This is of course preferable to any ex-post combination of these different data sources, as we use in this paper. In the year after the introduction of the register data, the Gini index for France increased from 39 to 44, which is consistent with the previously used household data underestimating top incomes (Burricand, 2013).

> 4See also the study on global interpersonal inequality by Anand and Segal (2015) who append for every country the estimated top 1% share to the household survey distribution. The latter is assumed to represent the bottom 99%. For the majority of countries, the top 1% share is predicted from a cross-country regression using the top 10% share in the household survey.

4
Policy Research Working Paper 9340

# **Abstract**

This paper studies foreign direct investment spillovers on the gender-related labor market practices of domestic firms, based on a unique firm-to-firm data set of Bangladesh’s textiles and garment sectors. The paper looks at the female employment of domestic firms that are directly and indirectly related to foreign direct investment firms through supply chain linkages. These domestic firms are either the local suppliers or customers of foreign direct investment firms, or they share local suppliers and customers with foreign direct investment firms. The estimates show that domestic firms related to foreign direct investment firms have significantly more female administrative workers, but not necessarily female non-administrative workers, due to the former participating in more firm-to-firm interactions.

This paper is a product of the Development Research 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 afernandes@worldbank.org and hlkee@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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implication of this mechanism is that open defecation will have a steeper association with child height in urban places than in rural places (Bateman and Smith, 1991; Bateman et al., 1993). Table 4 investigates this using two additional collapsed datasets, one containing only the urban observations in each DHS survey and one containing only the rural observations. Although GDP per capita is not available for urban and rural parts of countries, urban and rural women’s height controls can similarly be computed from the 12