rafmacalaba/data-use-mentions-extended huggingface dataset:
safeguard measures. In this case, the government’s decisions privileged the defense of the competitiveness of the productive chain.

# **1. Rice**

Rice stands out as one of the agricultural products with the highest number of investigation applications, both for dumping and safeguards. However, it should be noted that the commercial defenses imposed during the period under analysis were almost exclusively concentrated in the Andean market and suspended the benefits derived from the Andean free trade area.

# **a) Sector evolution**

Rice represents approximately 12% of Colombia’s cultivated area, it is the third most important crop in terms of extension, after coffee and corn, and represents 6% of the agricultural production.<sup>21</sup> This product is covered by the _price band system_ , whereby the average _ad valorem_ tariff between 1995 and 2003 was around 40%.

According to 1999 data, the country has around 34,000 rice production units and a milling industry that employs approximately 5,000 people. The milling industry has an advanced level of technological development when compared to countries such as the United States, Brazil and Venezuela. In 2001, Colombia was ranked 23<sup>rd</sup> in world rice production and third in the FTAA after the United States and Brazil. Yields per hectare are above the international average, with 4.9 tons as compared to a world average of 3.9.

The trade balance of rice had a deficit throughout almost all the 1990’s. During the decade, the imports of rice have mainly originated from three countries: Ecuador, the United States and Venezuela. Ecuador and Venezuela’s share of imports are 43% and 21%, respectively, while the United States and the Asian countries have shares of 21% and 13%.

> 21 See Ministry of Agriculture (2002), Características y estructura de la cadena de arroz en Colombia. Observatorio de competitividad agrocadenas, Colombia.

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the parents-children nucleus, and the chances for unequal distribution of resources among household members are particularly large. The household structure is in fact quite complex, due to polygamy, to the frequent presence of foster children (Beck et al 2015) and of extended family members.

Most of the empirical work attempting to exhibit intra-household inequalities concentrates on food consumption or health and education (see Haddad _et al._ 1997 for an excellent introduction to the early research on these topics). More recently, some works based on structural models of intra-household allocation of resources (collective household models, see Chiappori 1988) deliver estimates of consumption shares by type of household members for nuclear households (Dunbar et al. 2013). In general, these estimates cannot be confronted to the actual sharing of resources, as these data are rarely available. The only exceptions we know of are two recent papers, using Bangladeshi data and concentrating on food consumption (Bargain et al. 2018, Brown et al. 2019). The complex structure and budgetary organization of WestAfrican households with separate spheres of spending suggest that the Pareto optimality assumption supporting those structural models is unlikely to be verified (see Baland and Ziparo 2018). Hence, this route might not be particularly promising in such context.

Recent attempts at describing intra-household resource allocation with non-monetary measures of poverty also exist. Klasen and Lahoti (2016) study inter-individual inequality in India using individual multidimensional poverty indices (MPI), compared with the distribution of a household-based MPI. They find that intra-household inequality accounts for 30% of total inequality. More relevant to our context, Brown, Ravallion and van de Walle ( _forthcoming_ ) use nutritional status as a proxy for individual poverty and observe that in Africa, around one half of undernourished women and children are not found in the (asset-) poorest 40% of households.

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Policy Research Working Paper 10769

# **Abstract**

Despite evidence that women’s political preferences differ from those of men, women are less likely to participate in political and social discussions on Twitter and other social media. Following recent evidence that in-person social support matters for women’s political participation, women are hypothesized to form similarly supportive communities online. This paper tests this hypothesis using data from Twitter. The collected data comprises 451 hashtags on a broad range of (non-mutually exclusive) topics: social, gender, racial, LGBTQ, religion, youth, education, economic, health, COVID, climate, political, security, entertainment and lifestyle, and the Middle East and Northern Africa. The empirical results indicate that women are more likely to participate when the debate(s) feature female influential voices. This finding supports the potential role of mutual support in bolstering women’s participation in important debates.

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 rvanderweide@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
: Response of Core Inflation to Domestic Food Price Inflation Shocks**

||Core inf<br>|lation<br>|
|---|---|---|
|Horizon|percentage<br>points|alternative<br>measure|
|1|-3.8|-25.3|
|3|-2.0|-8.9|
|6|0.7|2.8|
|12|1.8|7.4|
|18|-0.5|-2.1|
|24|-1.6|-6.8|

# _Robustness Checks_

The basic results were slightly altered when the estimations were conducted using alternative measures of international supply shocks (the FAO food index). Tables 7 and 8 report these results and confirm that pass-through to CPI is low, estimated at 8% after 6 months. However, shocks from international food price inflation still have a stronger effect on domestic food inflation than on CPI inflation. However, this effect is slightly weaker than when the IMF food index is used. In fact, pass-through to food prices is estimated at 22% after 6 months. More importantly, it subsides over time. Also, the spill-over to core 17
# **3.2 Measures and Empirical Model**

Measures of domestic violence and women’s status used in the analysis come from the survey module administered to the wife of the household head (or the head if the household is female-headed). Because of the sensitive nature of questions about a woman’s status within the household and domestic violence, we used female enumerators for this module and instructed them to politely inform other household members, including the husband, that these questions should be answered in private. Furthermore, we asked enumerators to record if, despite this request, the husband insisted on being present in the interview, which occurred in 17 percent of interviews. There was no statistically significant difference, however, in reported domestic violence in interviews in which the husband was present.

The key outcome, domestic violence, came from the woman’s response to the question “Has your husband ever beaten you?” In response, 63.6 percent of women responded “no”, 8.8 responded “once”, 26.9 percent responded “more than once”, and 0.8 percent responded “regularly”. I convert the response to a binary outcome that is equal to one if the wife has ever been beaten, which 35.4 percent were. This rate is somewhat lower than the rates found in other areas of Bangladesh,<sup>7</sup> although it is difficult to know whether this difference is due to the specific population and the fact that the data are more recent than in most previous studies or due to underreporting.

The most direct test of the theories in section 2 that relate work opportunities and domestic violence would be whether domestic violence is currently occurring, given the current work status of the woman, rather than the cumulative measure of exposure that I am able to use. Of course, these two measures are correlated, but since the only measure of domestic violence I have is a cumulative measure of lifelong exposure, in section
at the time of selection and must be willing to commit to meeting the program conditionalities. The _Listahanan_ targets beneficiaries through a household assessment and application of a Proxy Means Test (PMT) methodology to predict income of households based on characteristics such as household composition, education, housing conditions, access to basic services, ownership of assets, etc. Predicted incomes are then compared with poverty thresholds at the provincial level to identify households below (poor) or above (non-poor) these thresholds. Households that are below (above) the threshold are assigned a beneficiary (non-beneficiary) status. We exploit this variation in eligibility to identify a causal effect of the CCT program on women’s, i.e., the primary grantees’, exposure to gender-based violence.

# 2.2 Gender Based Violence in the Philippines

GBV in the Philippines, like in several other countries, has surfaced as one of the most pervasive social problems. According to the 2017 National Demographic and Health Survey, 15 percent of evermarried/partnered women aged 15-49 years old reported to have ever experienced some form of physical or sexual violence by their intimate partner, and 7 percent reported to have experienced such violence in the past 12 months.<sup>6</sup> Further, 20 percent of the respondents reported to have experienced some form of

> 5 _Listahanan_ is an information management system developed by DSWD, which seeks to establish a database of poor households to serve as a basis for identifying beneficiaries of various social protection programs and services rolled out in the country. 6 Physical violence includes slapping, pushing, kicking, punching, being threatened with a knife/gun or some other weapon. Sexual violence includes being forced or threatened into unwanted sex or performing an unwanted sexual act.

5
regulations, education and awareness campaigns on the impacts of destructive fishing gear, fishing gear exchange programs, and provision of alternative income generating activities (AIGA), have an influence on household use of destructive fishing gear? Do MPA restrictions on access negatively impact households dependent on fishing? These are the questions this paper seeks to answer.

The empirical analysis is based on purposely collected household survey data from a sample of villages located along the coastal areas of mainland Tanzania and Zanzibar.<sup>3</sup>

> 3 Fishing is not longer included as a separate economic activity in national population censuses and surveys. Although some data on the number of fishers is collected and published by the MNRT in their annual statistics,

4
stratified the experiment by age group and gender. Table A.1 of the Appendix compares sociodemographic characteristics of the survey respondents with those of the Bogot ́a population (as collected by the population census conducted by the Colombian Statistics Agency in 2018). As the table illustrates, our survey respondents were comparable to the rest of the population in terms of age distribution and income (measured as economic strata). Yet, our respondents were more educated than the average inhabitant of Bogot ́a. This aligns with our expectations as Facebook access requires respondents to read, write, and proficiently use digital apps and surveys.

Figure A.1 illustrates our recruiting ads. Without mentioning migrants or the COVID-19 pandemic, these ads invited users to answer questions about the current situation in Bogot ́a. In total, 47,376 individuals in Bogot ́a clicked on the ads and 34,034 went to the first page of the survey. Of these, 5,908 began the survey and 4,333 finished it. These numbers imply a success rate of 12.7 percent of the population exposed to the ads. The actual number

> 7See Ib ́a ̃nez et al., 2022 for details on the impacts of the amnesty offered in 2018.

6
have higher turnover (and thus vacancies) than others. Second, online job postings mostly target more skilled occupations, often published by formal firms looking for skilled workers who are comfortable using online sites for job search. Third, low-skilled work in South-East Asia frequently consists of selfemployed jobs that would not appear on online job search websites (Cunningham 2018).

The selectivity of vacancies is not an issue for the purpose of our study as long as there are a sufficient number of vacancies for a given occupation to construct a skills profile. Since we are not analyzing dynamics of vacancies but only using them to define skills requirements, the relative magnitude of the number of vacancies by occupation is irrelevant. Instead, we only need enough information to capture a representative picture of the skills profile for each occupation.<sup>12</sup>

# _Building skills categories_

We categorize the 8,221 skills in the World Bank-Burning Glass Malaysia data set into our three main skills categories of interest (digital, cognitive, and socioemotional) and nine subsets of the skills categories. Skills that do not fit these three categories are defined as “other technical and language skills.” We categorize the skills manually by assigning the 633 skills clusters to each skills category. When all skills within a cluster belong to one category, that cluster is assigned to the respective category. If the individual skills within a skill cluster cannot be assigned to one category, we assign each skill within the cluster to the appropriate category.<sup>13</sup> Skill clusters that are left unclassified by Burning Glass, as is the case for about a third of skills (2,592), are assigned manually, facilitated by a maximum likelihood method to estimate most likely skills clusters and cluster family.<sup>14</sup> About a third of the total skills in the Burning Glass Malaysia data are digital, cognitive, or socioemotional according to our classification. The 20 most frequently cited skills are a mix of cognitive (6
The first breakdown is by region. The top panel of Figure 6 shows the level of real expenditure per capita, by month, from January 2007 through June 2010, by region. As expected, real levels of spending are markedly higher in Bangkok than elsewhere, and they are the lowest in the Northeast. The most remarkable feature of this graph is that it does not show any evident <!-- Start of picture text --> 120<br>110<br>100<br>90<br>80<br>70<br>60<br>2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1 2010m7<br>Consumer price index, all Thailand<br><!-- End of picture text -->

**Figure 5.1  Consumer Price Index, Thailand, 2007:M1 – 2010:M12** Source: Reported by Bank of Thailand.  Shaded area marks period of recession.

<!-- Start of picture text -->
1.2<br>1.1<br>1<br>0.9<br>0.8<br>Bottom decile<br>Top decile<br>0.7<br>0.6<br>2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1 2010m7<br>Price index<br><!-- End of picture text -->

**Figure 5.2  Prices Indexes for households in the top and bottom deciles (of  real per capita income over 2007-2010), Thailand, 2007:M1 – 2010:M12.** Source:  Computed by the authors using Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010, and consumer price index reported by the Bank of Thailand.  Shaded area marks period of recession.

Losers and Winners in Recession: Thailand 2008-09 11
This is based on the above mentioned ILO definition. Estimates from other west-African countries range from 27-50% in Nigeria to 54% in Cote d' Ivoire (Kanbur and Grootaert, 1995).

Since the child labor force participation is high in Ghana, we would like to understand the characteristics of the child labor. As seen in Table 12, most of the child labor is concentrated in the rural areas (almost 85%). Almost 28 percent do not have any education ( an increase from 16% in 1987), while almost 52% have primary level education. This also explains the dropout rate from primary to secondary being high. On gender basis, boys contribute more to the child labor pool than females. With regards to occupation and type of industry of work, it is not surprising to find that most of the child labor is engaged in agriculture related activities, and small portion in trade and commerce.

# **3.3 Turnover rates**

An issue related to the absorption of labor is the stability of the labor force. This can be seen from the data on turnover rates for different years estimated from the GLSS data sets. We find that the quit rates have increased during this period, perhaps an indication that the labor market is very dynamic and active. However, the labor market is more stable in 1991, compared to 1987 and 1988. Perhaps the quit rates were kept low by the high wages in the formal sector, while the economic reform program undertaken in the recent years have resulted in a large movement of workers from the formal to informal sector during 1992, as seen in Figure 2. Also, the quit rates are lower for those with higher levels of education (Table 13) and highest for those with no education.

**Fig 2: The estimated quit rates among wage workers in Ghana.**

<!-- Start of picture text --> 12 _ .---------<br>4)
exploring potential causes of bias will require household data, our data can help in investigating whether, since LARP ended, the bias had eased in locations that had been exposed to systematic registration earlier. Table A4 shows this was not the case; the estimated coefficient on (NASpP # SyD) is insignificant.

In addition to providing substantive insights, Table 6 illustrates the danger that improper specification— either by lumping sporadic and systematic processes together or by omitting relevant policy reforms— will lead to faulty conclusions. Panel A col. 3 shows that, even if policy reforms are properly specified, aggregating systematic and sporadic initial-registration modes results in biased coefficients; in our case, the estimated impacts of policy reform are biased upward and those of registration downward, resulting in a highly significant negative point estimate of the coefficient on initial registration. The fact that omission of the 2010 reforms would bias estimated effects of 2006 reforms upward, mistakenly attributing the impact of the former to the latter, reinforces the need for specifications to be informed by implementation details.

# **5. Implications for Research and Policy**

Administrative data allows us to analyze the short- and medium-term effects of a program in Lesotho that combined systematic initial registration of rights (titling) with reforms of regulatory and institutional policies. Based on descriptive evidence that a structural break in the cost of registration coincided with the reforms, as was expected given much of the literature, we were able to document that titling had significant social empowerment effects on female co-ownership and investment. We add to the literature by showing that policy reforms affected operation of land sales and mortgage markets, especially in the medium term and for female co-owners, thus ensuring registry sustainability and translating social into economic empowerment. Beyond the focus on all property in a city rather than just informal settlements or rural areas, a major difference from previous land titling studies is our use of administrative data for a relatively long period both before and after project interventions and changes in policy. Using administrative data as a frame for household
# **2.2 Contextual data**

A novelty of our analysis is the distinction of gender disparities in firms’ financial access, performance, and capital misallocation across countries that differ in their gender and cultural norms. We classify countries by their level of traditionality based on social perceptions about the role of women using data from the World Value Survey (WVS). To construct our measure of social perceptions towards women, we experimented with several variables from WVS, but narrowed down to the variable answered by countries that overlap with the coverage in the WBES dataset. This variable measures the level of agreement with the statement “When jobs are scarce, men should have more right to a job than women”. The variable is also used in other studies such as Li (2021) to measure gender norms.<sup>7</sup> For each country, the share of adults who agree or strongly agree with the statement in the initial year of WVS is computed. Traditional countries are defined as those having this share of adults above the cross-country median value. For robustness checks, we experiment with different thresholds such as terciles as well as an alternative outcome-based measure, that is, female labor force participation (see Section 3.3).

# **2.3 Sample cleaning**

Before proceeding with the analysis, we make the following adjustments to the sample. _First_ , we restrict our firm sample to countries with available complementary data on societal attitudes toward women and female labor outcomes noted in Section 2.2. _Second_ , we restrict the sample to firms with fewer than 1,000 full-time employees who report positive values for sales, labor, intermediate inputs, and capital.<sup>8</sup> Our final sample includes 61 countries and 21,411 observations after dropping observations with missing values in our variables of interest.<sup>9</sup> In our sample of 61 countries, 28 are classified as more traditional and 33 as less traditional, when
sorting variable. On this point, our study differs from those using the ILO definition (Grimm et al, 2011; ILO, 2011; Nguyen et al, 2011).

The core analysis is based on nationally representative household survey data from the following countries: Burkina Faso, Cameroon, Republic of Congo (urban only), Ghana, Mozambique, Rwanda, Tanzania, and Uganda.<sup>4</sup> Survey instruments differed by country but we have developed consistent variables for the economic activities of household members over the age of 15 to the extent possible. Variables include; type of activity, and for non-farm activities, time spent in the activity (any unit), and earnings per time period worked. The respondent‟s stated primary activity is used for the employment analysis, while both primary and secondary information is used for the analysis of all household activities (livelihoods) and the analysis of HE owners. For Ghana, Rwanda, Tanzania, and Uganda, the nationally representative household survey data was supplemented by qualitative and quantitative data collected from field interviews with HE and ME owners. These data are not nationally representative. The fieldwork focused on HE owners‟ motivations, perceptions about their work, their opportunities, and the constraints they faced.

> 4 See Annex Table 1 for a list of countries and data sources.

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