rafmacalaba/data-use-mentions-extended huggingface dataset:
Additionally, highly signicant in terms of parental ability to invest in children, exposed women have signicantly less wealth. Combined with having more children, this drastically decreases parents' ability to invest in children during their critical period of development.

# 6 Concluding Remarks

This paper exploits variation in conict at a detailed geographical level to establish causality between early childhood exposure to conict and women's nal stature. Additionally, along with Akresh et al. (2018), this paper is among the rst to document the intergenerational transmission of the impact of early childhood conict exposure on second generation health. Nepal experienced a decade-long violent civil conict between 1996 and 2006, which resulted in more than 13,000 fatal casualties, signicant infrastructure damages, and severe hindrance in delivery of basic services and generated extreme fear, sense of insecurity and stress among its citizens. Considered the most reliable, I use INSEC's database on the conict casualties and create an individual level victims' data set with exact geographical location (village) and dates of the incidents. This allows me to exploit variation in conict intensity at the village level for identication.

Fueled by international remittances, Nepal enjoyed consistent economic growth and poverty reduction (Uematsu et al., 2016) during the period of conict. The country also made significant improvement in other dimensions of development including health (Headey and Hoddinott, 2015) and non-income based multidimensional poverty (OPHI, 2013). These aggregate development trends, however, may mask disparities at a more disaggregated level due to signicant variation in conict intensity. The little research documenting the consequences of the war thus far is focused on district-level disaggregation, and results are mixed - little to zero impact on human capital accumulation (Valente, 2014; Pivovarova and Swee, 2015) and positive impact on women's employment (Menon and van der Meulen Rodgers, 2015). Libois (2016), on the other hand, using conict measurement at a more
|**ction**||
|Sample Mean|0.466|35.7|0.022|0.191|0.257|2405|
|Std. dev.|[0.499]|[10.9]|[0.147]|[0.393]|[0.437]|[3293]|
|Observations|2216|2198|2213|2207|2208|1629|
|||**Panel C**|**: Third-Deg**|**ree or More Con**|**nection**||
|Sample Mean|0.532|32.3|0.028|0.158|0.304|2220|
|<br>Std. dev.|[0.499]|[10.5]|[0.165]|[0.365]|[0.460]|[3096]|
|Observations|4008|3981|4005|3996|3997|2761|

_Notes_ : This table reports summary statistics of citizen demographics by degree of connection to the village government. The dependent variable is: in column 1, a dummy equal to 1 for female citizens; in column 2, the age of citizens in years; in column 3, a dummy for whether the citizen reports having a disability; in column 4, a dummy for whether the citizen has a tertiary level education; in column 5, a dummy for whether the citizen reports not working; in column 6, the citizen’s average monthly income in the last 6 months (conditional on working) winsorized at the top 1% and reported in thousands of Indonesian rupiah.

56
war-induced crop losses in Ukraine in near real time to strengthen local and global food security. Food Policy 115, 102418

- Deininger, K., Ali, D.A., Neyter, R., 2022. Impacts of Transparent Online Auctions on Public Land Lease Revenue: Evidence from Legal and Administrative Changes in Ukraine. World Bank Policy Research Working Paper 10201, Washington DC

- Deininger, K., Nizalov, D., Singh, S.K., 2018. Determinants of Productivity and Structural Change in a Large Commercial Farm Environment: Evidence from Ukraine. The World Bank Economic Review 32, 287-306

- Duncan, E., Skakun, S., Becker-Reshef, I., 2023. Identification and mapping of missiles craters and unexploded ordnance in agricultural fields in Ukraine using very high spatial resolution satellite imagery. In: Paper Presented at the 9th International Conference on Agricultural Statistics. World Bank, Washington DC

- FAO, 2022a. The importance of Ukraine and the Russian Federation for global agricultural markets and the risks associated with the war in Ukraine. In: Information Note, Dec. 5, 2022 update. Food and Agriculture Organization of the United Nations, Rome

- FAO, 2022b. Note on the impact of the war on food security in Ukraine. Food and Agriculture Organization of the United Nations, Rome

- FAO, 2022c. Ukraine: Impact of the war on agriculture and rural livelihoods in Ukraine: Findings of a nation-wide rural household survey. Food and Agriculture Organization of the United Nations, Rome

- Federle, J., Meier, A., Muller, G., Sehn, V., 2022. Proximity to War: The stock market response to the Russian invasion of Ukraine. CEPR Discussion Paper 17185, London

- Fergusson, L., Ibanez, A.M., Riano, J.F., 2020. Conflict, Educational Attainment, and Structural Transformation: La Violencia in Colombia. Economic Development and Cultural Change 69, 335-371

- George, J., Adelaja
is rejected with a p-value of 0.00. Regional and federal organizations have almost identical distributions of errors, though regional organizations are slightly more prone to outliers.

Figure 2B plots the distribution of z-scores where adjustments are made to skewed underlying distributions when necessary. Conditional on differences in the ranges of the distribution produced by the two different methods, the graph is qualitatively similar to that of the unadjusted z-scores. The adjusted scores have a more even distribution of extreme values, as would be expected. Figure 2C plots the rank-proportion transformation, and while district errors continue to be substantially smaller than those at higher tiers, some distinctions arise between the regional and federal government levels. Overall, along the margin of decentralization across tiers, the three measurement methods provide highly consistent messages.

# **3.2 What is the Appropriate Conception of Information in the Public Sector?**

These errors in bureaucratic beliefs matter because they are used as the basis of public policy decisions. In our representative survey of public officials, we asked public servants, “What are the three most important sources of information you use to find out about the state of service delivery in your jurisdiction?” along the following margins: ‘Informal interactions e.g. through co-workers’, ‘Management Information Systems’, ‘Formal field visits’, ‘Reports from the frontline’, ‘Information from higher-tier institutions’, ‘Formal citizen/user interactions’, or ‘Media sources’. Officials were asked to rank their top three sources or specify that others are more important.<sup>16</sup> In our setting, information is dominantly sourced from inside the public service rather than externally from the media. Only 8.93% of public officials in our survey responded that media sources were one of their top three sources of information, and 2.12% of officials selected it as their top source of information. This finding was consistent across tiers, with 8.54% of federal, 8.56%
variables for each manufacturing subsector and each time period are introduced. The analysis of a policy change using the D-in-D methodology assumes that the treatment and control group show similar trends prior to the tax reform. In figure 6 of the previous section we have observed comparable trends, before 1996, in the evolution of exports across the South African manufacturing sectors of the sample.

34. We use a panel database of 9 manufacturing subsectors (automotive, chemicals, machinery other than motor, textiles, leather, rubber, cork and wood, paper, iron and steel, and other manufacturing), sourced from the UNIDO ISIC database and spanning 10 years, from 1994 to 2004<sup>18</sup> . The dependent variable in the analysis is the natural logarithm of total South-African manufacturing exports (US$ nominal, deflated by the South African Producer Price Index (PPI)), obtained from UNComtrade. The choice of the South African PPI as deflator is done in order to capture the macroeconomic dynamics of the South African economy and the national price into the dependent variable. Alternative deflators, namely the US PPI, were tried and did not alter the results. The main constraint in conducting this study has been the lack of complete sectoral data.

35. The starting set up gives the following specification: (1) 36. Where i (i=1....9) denotes the sector and t (t=1994...2004) the year. is the automotive (treatment sector) dummy variable taking the value 1 for automotive and 0 otherwise. The coefficient captures possible differences between the automotive sector and the other manufacturing sectors prior to the policy change. is the time dummy variable taking the value 1 in the post treatment period (> 1995) and 0 in the pre-treatment period (≤1995). Consequently, captures aggregate factors that would cause changes in manufacturing exports after 1995 even in the absence of a policy change. is an indicator variable coded 1 if the observation is in
Internet adoption variable for the intensive and extensive margins will sum up to the coefficient for exports. If<sup>βnijt</sup> = 0.7 where β is the coefficient of the Internet adoption and the subscripts refer to ⁄βVijt the regression of the trade variable, then 70 percent of the effect of Internet on exports is explained by the extensive margin.

The Internet can affect trade between countries by reducing the fixed and marginal costs of trade. The Internet allows firms and consumers to find each other faster and easier, thus reducing the fixed search costs. The Internet also reduces the marginal communication costs between the seller and the customer. We assume that other components also affect trade costs, such as distance, whether the two countries are contiguous and have a common language and legal origins, and colonial relationship post 1945.

The empirical specification is: ln Yijt = β0 + β1 ln Internetit + β2 ln Internetjt + β3 ln GDPit + β4 ln GDPjt + δt + δij + εijt where ln GDPit ( ln GDPjt ) is the log of the nominal value of GDP and ln Internetit ( ln Internetjt ) is the log of Internet adoption of country i ( j ) in time t . We estimate three separate regressions where the dependent variable ln Yijt is the log of each trade variable, Vijt , xijt or nijt . δt is year fixed effects that accounts for the changes in trade that are unrelated to the changes in the annual Internet adoption and GDP levels, such as the global financial crisis in 2008. δij is country-pair fixed effects that account for variations in trade that are related to the idiosyncratic relationships between two country pairs. An alternative set of fixed effects is to include separate exporter and importer fixed effects, which will allow us to control for contiguity, common language, common legal origins and colonial relationship. As Baldwin and Taglioni (2006) explain, country-pair fixed effects are more suitable to deal with the estimation bias in a gravity model with panel data. A drawback of using the country-pair fixed
we construct the Frankel and Romer predicted trade measures by industry. Second, we do it over time. And ...nally, rather than looking at total trade, we look solely at exports.

Armed with a working model for predicting exports to GDP in each industry, it is straightforward to construct the instrument for external ...nancing need of exports, based on predicted export patterns rather than actual ones. That is, our instrument will be, in a manner identical to equation (11): Here, the predicted share of exports in industry i, in country c and time t, !b<sup>X</sup> ict<sup>,isconstructed</sup> from the predicted exports to GDP ratios, X<sup>b</sup> ict in a straightforward manner: Note that even though X<sup>b</sup> ict is exports in industry i normalized by a country’s GDP, every sector is normalized by the same GDP, and thus they cancel out when we take the predicted export share.

We proceed by describing the data sources in the next section. We provide a snapshot of our data, focusing on the patterns of external ...nancing needs of exports that we obtain. Then, in the following section we document stages of constructing our instrument, and present OLS and 2SLS regression results for both a cross-section of countries and a panel of 5-year averages going back to the 1970’s.

# 4 Data Description

International trade ‡ows come from the World Trade Database described in Feenstra et al. (2005). This database contains bilateral trade ‡ows between some 150 countries, accounting for 98% of world trade. Trade ‡ows are reported using the 4-digit SITC Revision 2 classi...cation. Since our variable of interest, EFNXct, is constructed using information on total exports from each country in each industry, we ...rst aggregate bilateral ‡ows across countries to obtain total exports
nancing needs.

# 3.1 The Sanitary Risk Index

This section details the construction of the Sanitary Risk Index, and motivates its use as a measure of ...nancing needs at the product level. The risk index is computed using data from the EU Rapid Alert System for Food and Feed (RASFF). The RASFF database reports all agri-food shipments to the EU between 2001 and 2008, that have su¤ered rejection due to food safety reasons. The database provides rejections by product, exporting country, importing country (EU member state) and year. The index is the coe¢ cient on the product dummy, �k in the following regression: where "ck is an error term. For a product k and an exporter c, the dependent variable is the combined count of noti...cations from all EU member states between 2001 and 2008<sup>3</sup> . The unit of observation is an exporter � product pair and the regression is crosssectional. To avoid picking up on any particularities generated by exporting countries’

> 3There is indeed, consistent di¤erences in the number of noti...cations among notifying EU states. In an average year, Germany with 20% of noti...cations is among the top notifying countries, while Ireland only account of 0.21% of noti...cations. Aggregating the number of noti...cations across all importing (notifying) countries and all years, smooths temporal ‡uctuations and reduces the e¤ects of outliers.

7
for our primary outcome measures using the administrative payment data on 6 August 2018 and 28 October 2018 to coincide with the download dates for the data sets used for the randomized assignment of the engagement and enforcement interventions respectively. Appendix Table A3 presents comparisons for each group and we find balance on most key measures covered.<sup>21</sup> Only 2 out of 45 comparisons are statistically significantly different from zero at conventional levels.

> 21Regressions used to estimate the treatment effects reported below are replicated including variables that are not balanced as covariates and neither the sign nor significance of any of the estimates change.

16
# **1. Introduction**

Gender violence constitutes a serious violation of women’s human rights. UN Women defines violence against women and girls as _"any act of gender-based violence that results in, or is likely to result in, physical, sexual, or mental harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life.”_ Violence against women has serious short- and long-term consequences on women’s physical, sexual, and mental health and, in its most extreme form, can result in death. Gender violence not only affects the overall well-being of women and their children, but also prevents them from participating and developing their full potential in society.

Violence against women is prevalent worldwide. According to the most recent data from the World Health Organization, 30 percent of women worldwide have experienced intimate partner physical and/or sexual violence or non-partner sexual violence in their lifetime (WHO, 2021). While women may be exposed to other forms of violence, this figure already represents a large percentage of women. Additionally, a report by the United Nations Office on Drugs and Crime found that nearly 60 percent of all female homicides in 2017 were committed by an intimate partner or family member (UNODC, 2018). Gender-related killings are known as "femicide" or "feminicide." Intimate partner violence against women is among the most prevalent forms of violence against women and includes psychological, physical, sexual, and economic violence, as well as controlling behaviors by an intimate partner. Over 640 million women worldwide are subject to acts of violence perpetrated by an intimate partner, which corresponds to 26 percent of all women aged 15 years and over (WHO, 2021). Additionally, in 2017, 34 percent of homicides of women were committed by their current or former partners (UNODC, 2018).

This study aims to assess violence against women in Chile, focusing on intimate partner violence given its high prevalence
corresponding estimate in the synthetic panel was 19.3 percent.<sup>14</sup> Similarly 18.6 percent of the population in

> 13 Monte Carlo simulations provided in Dang and Lanjouw (2013) suggest that estimates on poverty dynamics based on synthetic panels remain rather robust even where ρ changes its values over a range of up to 40 percent. The estimated values of ρ for the periods 1987/88-1993/94, 1993/94-2004/05, and 2004/05-2011/12 using the NSS data are respectively 0.74, 0.57, and 0.57.

> 14 In Table 1 we apply a vulnerability line that is twice the poverty line of INR 486. As our focus was on probing the validity of the synthetic panel method, we chose not to also apply the method for estimating a vulnerability

18
shock measures are _T_ (+) _dt_ , a positive temperature anomaly (also referred to as heat shock), _T_ ( _−_ ) _dt_ , a negative temperature anomaly (cold shock), _P_ (+) _dt_ , a positive precipitation anomaly (flood shock), and _P_ ( _−_ ) _dt_ , a negative precipitation anomaly (drought shock). Figure 2 shows the averages of these weather shocks between 2004 and 2019. Eventually, our measure of weather shocks captures short-term temperature and precipitation extremes in relation to long-term climatic conditions in the respective districts. In doing so, we capture meteorological conditions as expressed by the SPI and STI, instead of more complex indices such as the Standardized Precipitation Evapotranspiration Index (SPEI) or other drought indices for several 11
for males (left-hand side columns) and females (right-hand side columns). The share of NEET young women in 2010 is already four times bigger than that of young men. Therefore, since the labor market is particularly immobile, after 10 years these women have an 80 to 90 percent probability of remaining NEET, with little chance of improvement. The men’s results confirm that for them is less of a problem: most of them are non-NEET in 2010 and remain that way after 10 years.

**Table 6: Transition Matrix Based on the Synthetic Panel 2010/2018 by Gender**

|**Status in 2010,**|**_Ma_**<br>|**_le_**<br>|**_Fem_**<br>|**_ale_**<br>|
|---|---|---|---|---|
|**2018**|**Lower-bound**<br>**estimate (%)**|**Upper-bound**<br>**estimate (%)**|**Lower-bound**<br>**estimate (%)**|**Upper-bound**<br>**estimate (%)**|
|_NEET, NEET_|18.31|3.20|74.23|67.75|
|_NEET, non-NEET_|0.14|14.69|8.45|20.90|
|_Non-NEET,_<br>_NEET_|0.00|15.11|0.00|6.48|
|_Non-NEET, non-_<br>_NEET_|81.55|67.01|17.32|4.87|

_Notes:_ results are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in 2018. “Lower” and “Upper” refer to
# **1. The CGE Model**

The CGE model of China used in this study is the latest in a long line of model developments based at the Development Research Center of State Council in Beijing. The model has its intellectual roots in the group of single-country, applied general equilibrium models used over the past two decades to analyze the impact of trade policy reform (Dervis, de Melo and Robinson, 1982; de Melo, 1988; Shoven and Whalley, 1992). Here, we focus on the main features of the model.

**_Household Behavior:_** In order to come to grips with the poverty question, it is critical that that we disaggregate households to the maximum extent possible, subject to the limitations posed by survey sampling, computational constraints, and human capacity for analysis. Following our previous work (Hertel, Zhai and Wang, 2004; Hertel and Zhai, 2004), we disaggregate rural and urban households into 40 rural and 60 urban representative households according to their primary source of income and relative income level. Recent analysis of trade and poverty by Hertel et al. (2004) suggests the merit of distinguishing those households that are specialized (95% or more of their income from one source) in transfer payments, labor wages and salaries, or self-employment income. According to the available survey data, we stratify the rural households by agriculture-specialized and diversified (all other), and the urban households by three strata: transfer-specialized, labor-specialized and diversified. Within each stratum, we order households from poorest to richest, based on per capita income, and then group them into 20 vingtiles, each containing 5% of the stratum population.

Household income derives from labor income, profits from family-owned agriculture and non-agriculture enterprises, property income and transfers. Households consume goods and 3
**Figure 3. 10  Requests for informal lending services**

<!-- Start of picture text --> 100% 90.7%<br>86.4%<br>90% 79.8%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30% 20.2%<br>20% 9.3% 13.6%<br>10%<br>0%<br>Banked Unbanked Total Population<br>Requested an informal loan during the past 3 years<br>Did not request an informal loan during the past 3 years<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 23. **The unbanked have less access to credit sources with a higher degree of formality.** As Figure 3.11 shows, the unbanked mostly use friends or family and money lenders as sources of credit. In contrast, the banked are more frequent users of credit services provided by commercial stores, employment funds or informal savings funds. This suggests that access for the unbanked to these sources of lending is limited.

**Figure 3. 11  Source of informal lending**

<!-- Start of picture text --> 100%<br>88.8% 90.5%<br>90%<br>80% 74.7% 76.8%<br>70%<br>60% 51.3% 54.0%<br>48.7% 46.0%<br>50%<br>40%<br>30% 25.3% 23.2%<br>20%<br>11.2% 9.5%<br>10%<br>0%<br>Friends or Money Commercial Employment Informal Other<