rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>Africa. Projected annual growth rates in Private Consumption per capita from National Expenditure Accounts are provided by World Banks’ Macro Poverty Outlooks. Sectoral GDP growth projections in Agriculture, Industry, and Services are provided by the International Monetary Fund's World Economic Outlook Reports. Observed employment level for 2020 comes from the International Labor Organization for Brazil, Sri Lanka, and South Africa, from OECD for Türkiye and from government estimates for the Philippines. Projected changes in remittances by country are obtained from World Banks’s Migration and Remittances Data. Another input is a set of population projections for 2019 and 2020 based on the World Banks’ Macro Poverty</mark> Outlooks.

# **<mark>3.1 Methodology</mark>**

The model takes the household surveys microdata mentioned above as a starting point. The methodology then allows three main channels of transmission of the 2020 shock: through job losses, labor income changes, and non-labor (remittance) income changes.

Job losses or gains in Agriculture, Industry, and Services in 2020 are taken from observed employment levels from the ILO, OECD, or government estimates and compared to household survey data from previous years. When employment data is not available for a specific year, then job losses or gains by sector, by country, are estimated using elasticity estimates, such as those presented in Section 2. In that case, sectoral GDP growth projections (in Agriculture, Industry, and Services) are combined with the elasticities to provide employment projections.

These job losses/gains per sector then need to be imposed onto the household survey data. To match the new expected 2020 employment levels in each sector, the model has to determine which individuals in the base year household survey will lose (or gain) jobs to reach this new target. A Probit model, applied to each of the three sectors and by formal and informal workers, provides each person in each household survey a probability of employment: where _i_ is a person, _c_
**Table 3: Robustness of results to inclusion of country-specific trends**

||(1)<br>Physical<br>Violence|(2)<br>Sexual<br>Violence|(3)<br>Any Violence|
|---|---|---|---|
|ln(U<sup>f</sup>)|-0.301***|0.050|-0.249***|
||(0.071)|(0.060)|(0.063)|
|ln(U<sup>m</sup>)|0.308**|-0.070|0.254***|
||(0.135)|(0.054)|(0.085)|
|Observations|348165|268173|266517|
|P-value F-test|0.006|0.287|0.001|
|Mean Violence|0.181|0.067|0.198|
|X controls|Y|Y|Y|
|Year FE|Y|Y|Y|
|Country FE|Y|Y|Y|
|CountrySpecific Trends|Y|Y|Y|

# _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (I), sexual (II) or either (III) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse
variable name in dataset: HE030 Dummy variable equals 1 if respondent has an arrear on the payment, equals 0 if he/she does not have any such arrear.

The Global Financial Inclusion Database, (FINDEX, 2011, 2014)

- **Bank Account Ownership** (Bank_Account_p): Ownership of an account at a bank or credit union (or another financial institution, where applicable – for example, cooperatives in Latin America) Original variable name in dataset: q1a Dummy variable equals 1 if respondent has a bank account, equals 0 if he/she does not own an account.

- **Savings Rate** (Savings_p): Any saved or set aside money in the 12 months preceding the survey Original variable name in dataset: q11 Dummy variable equals 1 if respondent has saved money, equals 0 if he/she has not.

- **Savings in Bank** (Savings_Bank): Any saved or set aside money in the 12 months preceding the survey using an account at a bank, credit union (or another financial institution) or microfinance institution as supposed to saved money using an informal savings club or a person outside the family.

- Original variable name in dataset: q13a Dummy variable equals 1 if respondent has saved money using a financial institution, equals 0 if he/she has not.

- **Debit Card Ownership** (Debit_Card_p): Ownership of a debit card (sometimes called a bank card, bank book or salary card) Original variable name in dataset: q3a Dummy variable equals 1 if respondent has a debit card, equals 0 if he/she does not own a card.

23
The random forests method we use works as a combination of individual classification trees. Each of these trees focuses only on a subset of the explanatory variables to develop its nodes. That way, the classification trees are not correlated and offer different information on the underlying data generation process (Breiman 2001a; James and others 2013). Implementation of the random forests method is based on the package provided by Liaw and Wiener (2002) for the R program. As before, to compute prediction accuracy we split the sample between a training set and a validation set and repeat the exercise 10,000 times to estimate the mean and the confidence interval of the relevant metrics.

The benchmark specification under the logit regression framework performs as well as the random forests method, and the results from the two methods are highly correlated. The share of out‐of‐sample units correctly predicted when using the random forests method is 85.2 percent, or only 1 percentage point higher than when using the benchmark specification. This difference is less than the estimated standard error, which is around 1.4 percent under both the benchmark probabilistic model and the random forests method.

# **5. The rate and speed of urbanization**

The probabilistic model built from the sample of 1,277 units using subjective assessments can be used to predict the urban status of all 564,052 units in the universe of India in 2011. The key indicators for each unit are plugged into the model to compute the unit’s likelihood of being urban. If this likelihood is above 0.5, the unit is deemed urban; otherwise, it is considered rural. Combining indicators from the ground and from outer space, this procedure yields an urbanization rate that is almost identical to that in the 2011 Census of India. However, this similarity hides important discrepancies across administrative types, especially in the midrange between villages and municipal corporations. The exercise also shows that relying exclusively on one type of indicators, such as nighttime light, can yield potentially
whether the variable is correlated with other well-known indexes on corruption. Thus, comparing the WVS variable with the corruption indexes TI (Transparency International), International Country Risk Guide (ICRG) and Quality of Government (Control of Corruption) developed by Kaufmann, Kraay, and Mastruzzi (2004). The WVS corruption ratings (higher values=lower corruption) are highly correlated with the TI ( _r_ = -0.878), the ICRG ( _r_ =-0.680) and the Quality of Government rating ( _r_ =-0.827)<sup>12</sup> . Torgler‘s (2007b) results indicate that an increase in the perceived level of corruption by one unit increases the

> 12 The sign is negative because for all three ratings used (TI, ICRG and Quality of Government), a  higher score corresponds to a lower corruption.

52
8

# _Voucher Beneficiaries in Kilimanjaro_

The data used here are from the Vulnerability Household Panel conducted in the Kilimanjaro region of Tanzania in 2003, 2004 and 2009.<sup>10</sup> Kilimanjaro is a well-connected and dynamic coffee growing region located in the Northern Highlands, where maize is an important staple (in addition to bananas). It consisted of 5 districts in 2003 (one district was split up later on) and the sample was designed to be representative for all agricultural households in rural Kilimanjaro. In the first round, conducted in November-December 2003, 954 households were surveyed in 45 villages selected using the probability proportional to size procedure, or about 21 households per village. Households were revisited in NovemberDecember 2004 and 2009, with little attrition in 2004 (915 households surveyed), though a significant loss of households in 2009 (772 households interviewed).<sup>11</sup> To correct for underrepresentation due to attrition of households with certain characteristics, the sampling weights of the remaining households were adjusted, as outlined in more detail in Appendix A1.

Each round the survey comprises a comprehensive community and household survey with most of the modules identical across rounds.<sup>12</sup> The data in the third round capture the results of the 2008/9 agricultural season, which coincides with the input voucher pilot. A special module about the input voucher was added to the household questionnaire, including questions about whether households were determined as eligible, their actual uptake as well as the kinds of vouchers received. The total number of vouchers received by the village was recorded in the community survey.

> 10 See Christiaensen and Sarris (2007) for a detailed description of the survey and sampling design. 11 Some households with only one elderly were lost due to the death of the person. Some households moved out of the village, and some households were not surveyed because they were working far away on their farms. 12 The community survey collects information about the village governance structure and the
predict extreme weather events, and address their impact on an existing treaty.

Flow estimation models are already available to policy analysts, but they need improvement to increase the accuracy of the results. This paper contributes to the growing literature of methodologies with a specific application of the Basist Wetness Index (BWI), which is an index used to identify the magnitude of surface water, for the global set of international river basins. The methodology will predict natural runoff with a greater accuracy and level of significance than precipitation data alone, customary in other methodologies. The method 2
civic behaviours. Refugees report to be more interested in politics than natives, and are more likely to engage in political activism (boycott, petition, demonstration, etc.). They are also significantly more likely to participate in voluntary organisations, such as sport or cultural clubs, trade unions, or humanitarian organisations. Again, this pattern contrasts with Albanian immigrants, who are much less likely to engage in political or civic activities relative to Greek natives (Table 3). These findings are consistent with existing evidence (Blattman, 2009; Bellows and Miguel, 2009) that exposure to violence and conflicts – such as the religious persecutions Greek refugees were victim from in Turkey – can lead victims to higher political and civic mobilization and engagement (as a post-traumatic coping mechanism).<sup>25</sup> An important body of work even suggests that individual exposure to war violence may increase social cooperation at the local level, including community participation and prosocial behavior (for a review, see Bauer et al. (2016)).

One question that remains is whether Albanians will gradually assimilated over time, and in particular whether the second generation of Albanian immigrants will be equally integrated as the 1923 refugees. Although it is probably too early to say (as most Albanian arrived in Greece after 1991), the ESS provides some suggestive evidence that Albanians have not yet experienced any progress in integration outcomes. Although the ESS sample contains (almost) no second generation Albanians, I can examine the integration outcomes of first generation migrants who arrived in Greece at different ages. If second generation migrants were to integrate better than the previous generation we would expect to detect such a trend already between migrants who arrived at a young and older age. Table A.6 in the appendix shows that it is not the case: migrants who arrived at a young age ( _<_ 24) are as likely as those who arrived later in life to report Greek as the first language spoken at home, to feel discriminated, to
.00)**|4093|
||1994|-12.269|<br>(18.46)**|-5.224|<br>(9.02)**|3841|
||1995|-10.856|(17.45)**|-5.47|(9.36)**|3393|
||1996|-10.402|(17.57)**|-5.379|(9.41)**|2963|
||1997|-9.132|<br>(14.93)**|-3.378|<br>(5.63)**|2348|

Source: Elaborated from CHER database. Dep. Var. = Income satisfaction. Ordered Logit estimations. Z- stat in parenthesis. (*) significant at 5% level; (**) significant at 1% level.

27
Policy Research Working Paper 8597

# **Abstract**

Through substitution and income effects, remittances can alter an individual’s allocation of time between market activities and household production, decreasing labor supply. This paper uses propensity score matching and household surveys for 2006 and 2014 to estimate the impact of remittances on labor supply in the three countries of the Northern Triangle (El Salvador, Guatemala, and Honduras). The results show that remittances are associated with a reduction in labor force participation, particularly among women. This effect is largest for Salvadoran women (13 percentage points). A sensitivity analysis finds that the negative effect on labor force participation rates of men in El Salvador and Guatemala and women in El Salvador is robust to potential selection bias. Receiving remittances is also associated with a lower likelihood of young adults being in school or at work, with this effect being robust to selection bias for young men in Guatemala. At the same time, the evidence suggests that remittances may be supporting small enterprises and self-employment in El Salvador and Guatemala. The analysis does not find robust evidence of remittances affecting the labor supply in Honduras in 2014..

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at lsousa@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
two unemployed people is a first‐time job seeker, and nearly two out of three unemployed people have been unemployed for over a year. Thus, unemployment in Morocco is largely structural. Long‐term unemployment discourages job search and accelerates the loss of skills, making it more difficult to enter, or return to, the workforce. This is particularly worrying in a country where more than half the population is below 30 years of age. Yet, unemployment is a regional issue. According to the MENA Economic Monitor, more than 11 percent of the region’s active population is unemployed, with more than 30 percent of youth. Unemployment of the region’s educated youth is particularly worrying and questioned the concept of a “skills premium”

> 9 As discussed later, another constraining factor relates to the fact that the business environment, despite notable improvements in recent years, is still in need of progress.

11
Industry Restructuring and Regulation -Building a base for sustainable growth

# **2. Evidence on the impact of reform on growth**

The impact of reforming an infrastructure or utility industry on economic growth can be seen in two primary ways:<sup>7</sup>

- reform can provide a stimulus for the economy through:

- )> attracting foreign investment, both when privatizations occur and when new investment opportunities become available;

- cheaper (relative) prices for infrastructure services as efficiency gains accrue, due to greater incentives for operators so helping ensure the country can utilize its competitive advantages and that markets will allocate resources appropriately;

- increased 'product' competition due to greater incentives for innovation and growth;<sup>8</sup> and

- increased employment opportunities as service expansion and quality improvements are implemented;

- providing a platform for economic expansion. For example, availability of adequate 'public' electricity supplies at an acceptable quality is key to increasing output without costly self-generation each new factory, etc. Furthermore, having an adequate communications infrastructure (both transport and telecommunications) is vital for efficient operations.

From these two relationships between infrastructure and economic growth, it is easy to postulate why effective regulatory reform is key to unleashing greater economic growth. However, the problem that has continually hampered an empirical investigation of these relationships is that of data.

Two sets of data are available:

- a economy-wide studies that attempt to determine the impact of infrastructure reforms throughout the whole economy; and

- studies that focus on the impact of reforms on just one infrastructure sector.

The latter type of study is less informative than the former but is more prevalent. It is possible, however, to infer from these studies implications for the whole economy.

## **2.1 Economy wide**

There are two types of economy-wide studies available:

- those that seek to consider panel data from several countries; and

- studies that concentrate on just one country.

Both types of study are reported below
mathematics was the only one of the four sets of models where the final gender difference was no longer statistically significant. This implies that other factors, not examined in the models, contribute to the substantial residual gender difference in grade 4 science and grade 8 mathematics and science. One possibility is that selection effects could be driving the observed differences — that is, if only highachieving girls attend school or sit for assessments, but most boys do so, there is a possibility of bias, such that girls’ average achievement would appear inflated. However, as enrollment in primary and intermediate education in Saudi Arabia is almost universal among both genders, selection effects are unlikely to be playing a role in this analysis.

Given evidence from other studies, it is likely that differences in reading proficiency play a role in explaining at least part of the remaining gender differences. This is particularly so in relation to science achievement, where test items are by necessity embedded in a context that often requires a greater degree of reading comprehension. Differences in reading proficiency between boys and girls might also contribute to explaining the remaining gender differences observed here in mathematics achievement in grade 8, given that grade 8 mathematics assessments require more reading skills compared to mathematics in early grades. For example, test items assessing applied reasoning or problem-solving skills in grade 8 are more likely to be embedded in a short scenario requiring some level of reading. International assessments in recent years have consistently shown that reading achievement is, on average, substantially lower in Saudi Arabia than in many other countries, both among grade 4 students (Mullis et al. 2012; 2017) and among 15-year-old students (OECD 2019).

Moreover, in Saudi Arabia, the reading achievement gap in which girls outperform boys is among the largest gender differences in the world. Differences between boys and girls in reading proficiency have been found to exceed half a standard deviation in both the PIRLS and PISA studies (Mullis et al. 2017; OECD 2019). Previous research on TIMSS mathematics and science items has
at risk for PTSD (Perreira and Ornelas, 2013). This is true not only in Central American migrants, but in migration populations around the world. For example, one meta-analysis of 113 articles confirms that exposure to violence during migration affects mental health; the most frequent consequences include post-traumatic stress disorder (Kirmayer et al., 2011). PTSD is closely tied to anxiety (Torres, 2020). More broadly, a great deal of research suggests that fear follows experiencing crime in general (e.g. Skogan, 1987; Walklate and Mythen, 2007). Thus, one of the most important mental health consequences of experiencing violence is increased levels of anxiety.

We argue that these mental health consequences of victimization affect political engagement. Some emotions, such as anger, mobilize voters (Valentino et al., 2009, 2011). In contrast, both depression and anxiety are politically demobilizing (Weber, 2012; Young, 2019; Burden et al., 2017; Landwehr and Ojeda, 2021). For example, when triggered to consider stressful life experiences unrelated to politics, individuals without a history of participation 7
commute and expanding women’s choices for dependent care, such as on-site care centers as well as home-based supervision or childcare arrangements, which may be more cost-effective or convenient for the family.

This paper is structured as follows: Section 2 reviews the existing literature related to work from home and gender. Section 3 presents the theoretical framework used to explain women’s choices related to where and how to work. Section 4 details the data and explains the empirical methodology. Section 5 presents the results of the multinomial model for women, and we compared them with men. Section 6 briefly discusses the policy implication and concludes.

# 2. Related Literature

The increase in the use of information technology in the last decade has shaped the labor market. One example of that is teleworking. This work arrangement enables people to work from home and the possibility of combining work with home responsibilities. During the COVID-19 pandemic, the government-mandated lockdown inevitably brought negative consequences to the labor market, while having more pronounced effects on women as school closures heightened their household responsibilities. For instance, 46 percent of women report having experienced an increase in childcare responsibilities during the pandemic compared to 30 percent of men (World Bank High Frequency Household Survey, 2021). Throughout that time, telework may have been the easiest way to care for

> 12 Edwards & Field-Hendrey (2002) explain that the attractiveness is given by the possibility of combining unpaid domestic work with gainful home-based employment.

4