rafmacalaba/data-use-mentions-extended huggingface dataset:
youths was it possible to directly interview their provider.<sup>22</sup> In accordance with the interests of the research, our analysis required focusing on the youths that cover the following criteria:

- Belonged to a household that in 1997 was eligible or that through the period of the panel

- had received benefits from PROSPERA.

- Were between 18 and 35 years of age. The range starts at the legal age to enroll in high

- school and concludes at the maximum age of the selected sample for ENCEL 2017.

- Were employed at the time of the study, since a study like this necessarily requires that

- individuals under study have entered the labor market.

> 19 See World Bank (2018a and 2018c) for a description of the design of ENCEL 2017 and of the collected information. 20 In the household and members databases, new households were included (not interviewed in the baseline), which were created in recent years by some selected youths (World Bank, 2018a).

> 21 The age of 14 years old is commonly used by intergenerational mobility studies to define characteristics of social origin, because it is considered to possibly represent an important moment in the formation of the individual. 22 According to the World Bank (2018d), youths who were interviewed directly in the survey, in contrast with those who were in the sampling frame but not interviewed, come from households that, on average, are reasonably similar. This, in terms of age and schooling of the head of the household, as well as in the proportion in which the latter were employed as day laborers or workmen, the access to social security through the employment of the head of the household, the percentile of the poverty index and the size of the household. Nonetheless, it is possible to differentiate them by the lower degree of marginalization of the communities of origin exhibited by those who were interviewed, even though these differences were small in magnitude (World Bank, 2018d).

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loyal personnel have a larger degree of discretion over decisions in the board of CDR.

In a third hypothesis, we test whether the “quality” of a political connection helps to reduce bargaining costs. Firms would place higher trust in the ability of elites to honor intertemporal transactions that are “embedded” in the institutional framework and exert discretion over decisions via loyal personnel. We expect that PCF1 elites have lower bargaining costs to broker deals and therefore make overspending and overpricing more likely.

**H3** : Only PCF1 connections can succeed in overpricing or overspending contracts.

# **_Enforcing_**

A central issue of corrupt exchanges is deferred reciprocity, making enforcement costly. Many deals require that mutual promises are honored with a time-lag, as not all resources are available at the same time (for example, promises for upcoming projects can only be kept once these projects are implemented). PCF1 elites, then, should face lower costs to enforce deals than other (PCF2) elites with direct discretion over decision making and are able to provide kickbacks in future contracts.

- **H4:** PCF1 connected consultants involved in the cartel are compensated with inflated contracts in intertemporal transactions.

# **3. Data and methods**

We leverage two sources of data. First, we analyze a data set of all 394 infrastructure procurement contracts awarded by CDR between January 11, 2008, and March 12, 2018. The data set contains the name of the contract and winning firm, the initially awarded contract value, the sources of funding, the project location(s), the sector, and other identifying information about each contract. We obtained the data from CDR with a formal request pursuing the access to information law.

8
<!-- Start of picture text --> year-to-year GDP growth total unemployment<br>10.0% 3,000,000<br>8.0%<br>6.0% 2,500,000<br>4.0%<br>2,000,000<br>2.0%<br>0.0%<br>1,500,000<br>-2.0%<br>-4.0%<br>1,000,000<br>-6.0%<br>-8.0%<br>500,000<br>-10.0%<br>-12.0% -<br>I 2007 II 2007 III 2007 IV 2007 I 2008 II 2008 III 2008 IV 2008 I 2009 II 2009 III 2009 IV 2009 I 2010 II 2010 III 2010 IV 2010<br>number of people<br>annual percentage growth<br><!-- End of picture text --> _Source:_ Own calculations using data from the National Institute of Statistics and Geography ( _Instituto Nacional de Estadística y Geografía_ — INEGI), the Economic Data Bank ( _Banco de Informacion Economica_ —BIE), and the Mexican Occupation and Employment Survey ( _Encuesta Nacional de Ocupación y Empleo_ —ENOE), 4th quarter 2010.

By March 2011 total employment had recovered, but unemployment and informality rates still compared unfavorably with the situation during the worse of the crisis. Even though the Mexican labor market has shown signs of an irregular recovery during the last months, as of early 2011 it remained in worse condition than before the crisis. The unemployment rate, which reached the almost record high of 6.41 percent in September 2009, declined every month (with the exception of the seasonal peak of January) to 4.81 percent in March 2010 but then rose to above 5 percent for most of 2010 (see figure 2.
- **Step 2.** We assign each settlement pixel to one of the five considered flood hazard categories defined in line with risk to lives and livelihoods (table 1). We repeat this step for all the world’s settlements and each year.

- **Step 3.** On a yearly basis, we compute the total settlement area per flood hazard category for each subnational administrative unit (such as state or province) and calculate its share relative to the corresponding overall settlement area. To address possible limitations of settlement over/under-estimation due to poor Landsat-5 data availability before 2000 in some parts of the world, we apply SPM (see above). To obtain wider trends, we further aggregate exposure estimates to national and regional levels and compute the long-term trends of settlement exposure to flood hazards.

**Table 1.** Flood hazard categorization applied in this study

|**Category**|**Hazard**<br>**class**|**Flood depth**<br>**(meters)**|**Description**|
|---|---|---|---|
|0|None|0|Unaffected during a 1-in-100 year flood|
|1|Low|0 – 0.15|No significant risk to life or economic activity|
|2<br>3<br>4|Moderate<br>High<br>Very high|0.15 – 0.5<br>0.5 – 1.5<br>Over 1.5|Disruptions to livelihoods and economic activity; some risk to life for<br>select populations, especially vulnerable groups such as children and<br>disabled people (including through water-borne diseases)<br>A significant share of the affected population is expected to face risk<br>to life, especially if flood waters have a current; major disruptions to<br>
Specifically, the World Bank’s Logistics Performance Index (LPI) is based on surveys of global freight operators and express carriers on customs, logistics and transport infrastructure, international shipments, logistics competence, tracking and tracing, and delays. UNCTAD’s Liner Shipping Connectivity Index (LSCI) is derived from the number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country’s ports. The choice of variables in the panel is informed by Arvis et al. (2013), but also by findings from the stylized facts presented in the main text. Full details of data and sources are presented in table 2.<sup>9</sup> Since trade costs data are obtained as bilateral geometric averages, trade facilitation indicators available at individual country level are transformed into bilateral measures by taking the geometric average of each country pair direction. Therefore, the unit of analysis is each individual country pair. Time fixed effects η t are included in the estimation to control for global trends. As the measures of trade costs already net out multilateral resistance components, in line with Novy (2013), the estimation does not include additional fixed effects.<sup>10</sup> Instead, to control for possible correlation of error terms, clustered standard errors by country pairs are used.

Two models are estimated: a general model for the determinants of trade costs in all sectors of the economy, and a sectoral model for the determinants of trade costs in the manufacturing sector. The two models follow the specification presented in equation 1, but trade costs and tariff rates are sector specific. Table 3 shows results from the estimations.

# Results

All estimated coefficients have signs and magnitudes in line with prior expectations based on the literature. Geographical distance and high bilateral tariff rates are positively associated with trade costs. In contrast, adjacency, common language, and membership in a common regional trade agreement tend to reduce trade costs. Policies
of these key groups in the campaign’s audience. Third, practitioners should be mindful of the tradeoff between maximizing on-platform engagement and concentrating on vulnerable populations that are most likely to benefit from the campaign. Although the cost to reach and engage these at-risk users may be higher, the potential for significant developmental impact per individual reached is also greater.

# **7 Cost-effectiveness**

In this section, we evaluate the cost-effectiveness of the investment in advertising. To determine the range of overall effectiveness in terms of malaria cases avoided per million per month, as _recorded_ in health facility records, we use the most conservative impact estimates from Table 5. These include monthly reductions in overall national incidence (3.12 cases) and urban incidence (6.10 cases). Considering that these impacts represent the average during the treatment and after-treatment period from September 2020 to May 2021—a span of 9 months—we multiply the monthly reduction by 9. This calculation suggests a total estimated reduction in reported malaria incidence of approximately 28.08 and 54.90 avoided cases per million people in the three study states.

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|84.7|81.1|68|72.9|966|1,418|10,641|13,025|

_Source:_ Own elaboration based on Somali HFS 2017-18 _. Note_ : IDP stands for internally displaced persons. Observations correspond to the number of individuals in the dataset.

# **_6.1. Are female-headed IDP households poorer than male-headed IDP households and maleand female-headed non-IDP households?_**

The incidence of poverty is higher for male-headed households than female-headed ones (73 versus 67 percent) (Table 3). This result is driven by the large gender gap in poverty (against maleheaded households) among IDPs (11-14 percentage points), as well as the smaller but significant difference in poverty between male- and female-headed non-IDP households (3 percentage points). Even when distinguishing between _de jure_ and _de facto_ female-headed IDP households poverty remains lower than male-headed households. For non-IDPs there is no significant 17
Unfortunately, comparable data are not available to carry out similar calculations for IDPs. The definition of a “protracted situation” for IDPs is also complex, considering that there is no clear definition of what would constitute a measurable end-point for their ordeal. Case studies may provide more useful insights than aggregate global figures.

These numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete situations. Time-series also show that the perceptions of a crisis spinning out of control are not supported by the available evidence. And let us never forget that this is not about numbers but about people and about an immense amount of human suffering.

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make transactions with those agents located in their same area. As in the case of the Democratic Republic of Congo, there is no gender differential effect, and this is reflected in the fact that the coefficient associated with the binary variable that indicates that both the client and the agent are women is not statistically significant. Since the data from Senegal do not include the age of clients or agents, we cannot include the difference in age as a regressor.

In Table 11, we run the analysis of the transaction amount, analogous to that in Columns 1 and 3 of Table 6. In particular, we run OLS regressions where the dependent variable is the log of the transaction amount (in US dollars of January 2015) against an indicator for female agents, female clients and their interaction. Again, standard errors are clustered at the client level and, due to collinearity issues, we do not include market nor time fixed effects. In Column 1 of Table 10 the estimates suggest that female agents on average perform transactions 72% or 70$USD smaller than male agents when dealing with male customers, but that difference vanishes when the female clients transact with them (p-value of the test that

> 9 When we use the sample of matched agents in Column 10, the marginal effects are 12.8 and 12.5 percentage points for the specification in columns 1 and 3, respectively (results not shown).

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The relationship between the length of paid mandated maternity leave and the presence of female relative to total workers in the formal private sector is explored. Using firm-level survey data for a large cross-section of mostly developing countries, a large positive relationship is found between maternity leave and the proportion of workers at a firm that are females. The relationship survives several robustness checks. The empirical results suggest that this relationship is far from uniform, being much stronger in countries where maternity leave is entirely funded by the government compared to the employer.

Several issues remain to be explored. We provide a few examples here. First, there is little in the theoretical literature on how the relationship between maternity leave and female employment depends on firm and country characteristics. Understanding the theoretical basis for this heterogeneity in the maternity leave and female employment relationship can help better understand the impact of maternity leave and may ultimately lead to better policy design. Second, due to data limitations, we were unable to say anything on how other outcome measures may be influenced by maternity leave. Examples of outcome measures include wages of women vs. men, occupational segregation of men and women workers, etc. Future work along the lines in this paper can help shed light on how such outcomes are related to maternity leave and if the relationship is the same or varies with firm and country characteristics such as firm-size, age of the firm, GDP per capita, etc. Last, implications of maternity leave on income equality in the country is another important area for future research.

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5.0|2.5|3.9|
|Wholesale and trade|3.8|5.0|1.3|3.3|
|Transport, storage and communications|6.2|7.5|3.4|5.9|
|Banking|6.9|8.3|6.1|5.3|
|Public administration and defense|4.6|4.5|5.6|4.5|
|Other services|3.4|4.2|2.4|2.9|
|Total GDP growth|3.9|4.9|2.3|3.5|

_Source:_ Author’s calculations based on data from Banco de Guatemala. a/ Information for 2002 (2003) is preliminary (estimated).

The current human capital base is essentially a product of past agricultural growth and eminently anti-distributional policies. The World Bank (2003a) and UNDP (2002) document that insufficient cheap labor, in particular for coffee, was the main barrier for the expansion of export crops during earlier periods. Hence, in order to create a low-wage labor force, the campesino and indigenous society was excluded from education. The plantation economy that resulted provided little incentives to accumulate human capital. Historically, the low level of schooling is also an outcome

> 7 During a very brief episode in the early 1990s, inflation increased and fiscal discipline eroded. More recently, the World Bank (2003b) presents arguments suggesting that short-run growth _may_ be related to the cyclical stance of fiscal and monetary policies.

-6-
multiple components of the stimulus, we aggregate the three programs into a single binary participation variable for the analysis.<sup>3</sup> Because our participation variable is imputed based on eligibility criteria rather than measured directly – the SES did not ask about participation in these programs – the effects we measure in the analysis are more of an intent to treat rather than an actual impact of participation. We are therefore examining the potential effect of the stimulus as if everyone who was eligible also received benefits under the program.<sup>4</sup> Aggregate statistics do show, however, that most eligible households participated in the stimulus.

Within some rounds, the SES data also collected information on households‘ perceptions of the economy. In the first six months of 2008 and 2009, for example, households were asked in the SES whether they believed the economy was in a worse situation compared to a year ago. Figures 4 and 5 show that just prior to the onset of the financial crisis in late 2008, urban and rural households across the board believed that the economy was worsening compared to the same point in 2007. By June 2008, less than 15 percent of rural and urban households both felt that the economy was doing better compared to 2007, a drop from about 25 percent in January of 2008. Rising oil and commodity prices helped fuel these sentiments, as indicated by Table 2, which shows that in 2008 price control was among the most important concern for households. By 2009, a greater share of households (about 35 percent) felt that the economy was doing better compared to in 2008, although this share slipped somewhat and flattened by mid-2009.

In this context, we can examine the coping strategies households adopt in a crisis. Table 2 presents summary data on how households expected to cope during a crisis, and shows that during the first half 2008 they were overwhelmingly inclined to reduce spending and limit their borrowing if

> 3 As reflected in Table 1, households might also be participating
is significantly higher among persons with any difficulty in six countries but lower in eight countries while the EPR was lower for persons with disabilities in almost all countries. Persons with functional difficulties may disproportionately drop out of the labor force by stopping to search for work and hence are not captured in the unemployment rate.

# **Poverty**

Table 4.3 gives poverty headcounts using international poverty lines of $1.90, $3.20, $5.50. It also gives the headcount using $1.90 but applying the household consumption expenditures per capita net of health expenditures. Poverty headcounts were calculated based on daily household consumption aggregates divided by household size to obtain local currency and nominal daily per capita consumption expenditures (PCE). PCE were adjusted using PPP (Purchasing Power Parity) conversation rates of the International Comparison Program (2011) for each country. Poverty headcounts are close to published poverty headcounts, except for Uganda where our estimates are higher at more than 60%.<sup>6</sup> Using the international poverty lines of $1.90, $3.20, $5.50, poverty headcounts are significantly higher among persons with functional difficulties in some countries (Ethiopia, Namibia, Nigeria, Tanzania, Uganda). However, there are variations depending on the threshold for the poverty line

> 5 For a recent review of the literature, see United Nations (2019).

> 6 For Afghanistan, the international poverty rate is not available. But our computation is larger compared to the national poverty rate.

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Basically, the average rate of progress of rural HOIs more than doubled the urban annual rate (1.4 versus 0.5 points) and the aggregate rural HOIs for basic housing services for 9-15 and 0-4 year olds increased by 42 and 41 points, while the same urban HOIs increased by 11 and 15 points respectively. Although telephone access was by far the opportunity with the largest improvement in both urban and rural areas, the annual rates of improvement were faster in rural areas (6.3 versus 5.1). Similar comparisons, in terms of annual rates of improvement, apply to opportunities such as prenatal care, assisted birth delivery, cooking energy source, and overcrowding. Three opportunities that remained stagnant in urban areas showed moderate improvement in rural areas, namely, access to water and completion of primary and secondary education (respectively by 14, 7, and 7 points). In only three cases – completion of postsecondary education, sanitation, and immunization vaccines – urban improvement was marginally

> 25 Only three of the urban HOI indicators – lighting energy source, water, and cooking energy source – approached universal coverage and had reached 100, 97, and 94 points, respectively by 2000. Three other stagnant urban HOIs were completion of primary education, sanitation and immunization-vaccines. A major cause of concern is the deterioration in the HOIs for nutrition non-wasting and non-stunting by 14 and 6 points, respectivel, and the HOIs for primary and secondary education by 4 and 7 points, respectively.

14
of skill content, but also in the bottom, where jobs are intensive in manual, non-routine tasks. Meanwhile, the occupations in the middle of the skill distribution lost relative weight. Goos, Manning, and Salomons (2009, 2011) show that these trends are not unique to the United States. High-paying and lowpaying occupations expanded relative to middle-wage occupations in the 1990s and 2000s in 16 OECD countries.

Computers and information and communications technologies have certainly reached the most dynamic firms in developing countries (World Bank 2016). However, the relative abundance of low skill labor and different barriers to the diffusion of technology can slow down the process of technological change (Messina 2017). Are there signs of job polarization in Latin America?

An important challenge for the study of occupation polarization in the region is that most household surveys change the classification of occupations over time, limiting the possibility to make detailed comparisons over long periods. Figure 6 shows changes in the employment 12
education levels of members, by household composition, etc. Different characteristics will predict a different probability to suffer an unemployment shock, or for those who gain employment their different characteristics will predict different new incomes.

<mark>The third source of heterogeneity is the sector of employment, as job losses by sector vary, whether according to macroeconomic projections or actual available data. Each country faces different sectoral GDP projections or estimates. Workers in four sectors are protected from any shock:</mark> Public Administration, Utilities, Health, and Extraterritorial Agencies.

<mark>This macro-micro simulation model provides several advantages over other methodologies. First, it allows estimating distributional impacts and not only poverty impacts. Second, the use of actual employment data when available (as opposed to using projected employment levels based on past elasticities) partially addresses the potential concern of using past data to predict outcomes, especially during an unprecedented crisis with unique elements, such as the implementation of mobility restrictions.</mark> However, the model also faces some limitations. First, because of the unprecedented nature of the crisis, the estimation of employment probabilities based on pre-crisis household and individual characteristics may have limited predictive power. Second, the model is focused on average annual impacts to ensure consistency between the macro and micro projections. Consequently, it is

> 15 This procedure is not applied in South Africa where there is no detailed information on sector of employment.

16