rafmacalaba/data-use-mentions-extended huggingface dataset:
0.4|
|**Albania**<sup>**c**</sup>|71|12|1.51|1.08|0.88|0.2|
|**Brazil**<sup>**c**</sup>|88|8.4|1.39|0.99|0.53|0.2|
|**Mexico**<sup>**c,d**</sup>|80|29|0.86|1.07|0.69|0.1|
|**Peru**<sup>**c**</sup>|63|9.4|1.08|0.78|0.36|0.2|

_Source:_ U.S. EIA (2011) for U.S. prices, Reuters for FOB prices in the North Sea and Saudi Aramco contract prices, and authors’ calculations. _Notes_ : FOB = free on board. FOB prices are taken from Mont Belvieu propane prices in Texas, United States for Guatemala, Brazil, Mexico and Peru; the average of propane and butane prices from the North Sea for Albania; and the average of propane and butane Saudi Aramco contract prices for India, Indonesia, Kenya, Pakistan, and Sri Lanka.

a. Local retail prices of LPG converted to U.S. dollars using the market exchange rate at the time of the survey.

b. National average retail prices of LPG for residential consumers in the United States at the time of the survey. c. Quantities are estimated using national average prices.

d. The shares of households using LPG are based on the statistics for those using LPG as the primary cooking fuel.

Comparison of local and international prices provides clear evidence of price subsidies in India, Indonesia, and Mexico. The combination of subsidies and relatively high income made LPG most
-<br>sion of T, the reg-<br>ulator would require<br>real values for I, q<br>and c for each pe-<br>riod.|The regulator must be able to calculate<br>the real I and real net Beneft B for each<br>period.<br>The government does not need<br>to observe individual q and c values di-<br>rectly in every period, even though doing<br>so may reduce opportunistic behavior by<br>the incumbent. If the regulator faces in-<br>formation restrictions, it can audit the in-<br>cumbent on a random basis.|

# **7 Simulating alternative guarantee mechanisms in a real life highway concession**

In this section, we investigate the behavior of the proposed mechanisms using data from CCR NovaDutra, a highway franchise taken place in Brazil from 1996 to 2020. We test target functions from Subsections 5.2 and 5.3 with profit functions constructed under a broad array of different exchange rate scenarios.<sup>34</sup> For that, we suppose the same project have taken in eight developing countries characterized by limited currency market interventions between 1996 and 2020: Brazil, Mexico, Indonesia, Mozambique, Chile, South Africa, Peru and the Philippines. Table 5 compares selected indicators among these countries and shows they are diverse in terms of income, export composition, population and private investment in infrastructure.

> 34 For detailed information about the project, see Appendix B. Due to lack of data regarding prices and demand, it was not possible to simulate the target function discussed in Subsection 5.1 nor propose a test function based on notional values that would be consistent with the project’s cost structure.

34
The main specification measures IPV as dummy variables, taking the value of one if the woman has ever faced i) physical, ii) sexual or iii) any form of violence from her partner. As an alternative, the study aggregates information from the underlying questions on specific acts of IPV into summary indices, since this aggregation can improve statistical power to detect effects that go in the same direction within a domain (Kling et al. 2007). The study firsts convert answers to individual physical violence and sexual violence questions to z-scores by subtracting the country-specific mean and dividing by the country-specific standard deviation, for each of the individual questions on physical and sexual violence. The study then averages over these z-scores to create an index for i) physical violence, ii) sexual violence, as in Erten and Keskin (2018). Finally, it creates an index for iii) any violence, which is the arithmetic mean of the physical and sexual violence indices. Results in Table S1.5 show that the results are robust to using indices of violence, and gain statistical significance when considering sexual violence (Panel B), which now becomes statistically significant. Overall, the results are qualitatively similar and confirm the main analysis.

Finally, the study investigated the association of domestic violence with the (potentially endogenous) _individual_ unemployment status of the woman and her partner, using questions in the DHS that ask whether the individual worked in the last 12 months. This acts as a consistency check on the main analysis. A caveat is that male unemployment is measured poorly in the DHS data – it is collected in only 18 of our 31 countries<sup>34</sup> , and even where it is collected, it is missing in around 67% of cases. Still, the pattern of results for individual unemployment mirrors the pattern for unemployment rates (Table S1.6). Holding constant the unemployment status of their spouse, female unemployment in the last 12 months is associated with lower incidence of spousal violence. Male
**Figure 2. Nutrition Outcomes over Time**

<!-- Start of picture text --> 25<br>23<br>20<br>20<br>15<br>15<br>2003/4<br>12<br>10 2006/7<br>10<br>2011<br>5<br>3<br>2<br>0<br>Stunted Underweight Wasted Iodized salt<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, MICS 2006/07, and ENPSF 2011.

- 4.1.3 Cognitive, Emotional, and Social Development

Moroccan children face a number of challenges in terms of their cognitive, emotional, and social development; relatively little progress has been made over time (Figure 3). In 2006/07, approximately 51 percent of children aged five received early childhood care and education (ECCE). By 2012, this rate had risen to 58 percent. However, over a similar period, the percentage of children engaged in developmental activities fell from 48 percent (in 2006/07) to 34 percent (in 2011).<sup>23</sup> The low level of engagement in developmental activities is of particular concern, as it means that two-thirds of the children are missing out on these important opportunities.

Most concerning are the high chances of violent discipline, with 90 percent of children experiencing violent discipline in the past month, substantially endangering their development. However, once again, we lack the data to examine trends among this proportion of children over time. Work or domestic work done by children aged 5 is also a potential problem, with 20 percent of children engaged in such work. This may make the transition to school more difficult and could be potentially hazardous to children’s well-being. Here too, available data do not allow for comparisons over
2006; Chen et al., 2008; Cerra and Saxena, 2008), while in other cases negative consequences are found to persist over longer periods (Blattman and Miguel, 2010).

The increase in the availability of household survey data in the last decade has generated some recent micro-level studies which examine the effects of violent conflict on welfare and economic outcomes of affected populations (e.g., Akresh and de Walque, 2008; Bundervoet et al., 2008; Akresh et al., 2011; Chamarbagwala and Moran, 2011; Merrouche, 2011; Shemyakina, 2011; Akresh et al., 2012; Leon, 2012; Mansour and Rees, 2012;Akbulut-Yuksel, 2014; Grimard and Laszlo, 2014; Valente, 2014; Pivovarova and Swee, 2015; Brück et al., 2019; Martin-Shields and Stojetz, 2019).

The micro studies that have focused on African conflicts have typically examined delayed outcomes observed many years following conflict. For example, Akresh (2008), and Kraehnert et al. (2019) examine the longer-term impacts of the Rwandan genocide on schooling and fertility outcomes.<sup>4</sup> Minoiu and Shemyakina (2012) find strong negative impacts of the 2002-2007 Côte d’Ivoire civil conflict on children’s health status in subsequent years.<sup>5</sup> Similarly, studies of the impacts of the 1998-2000 Ethiopia-Eritrea border conflict find evidence of negative impacts on child health and schooling outcomes in subsequent years (Akresh et al., 2012; Weldeegzie, 2017). In northern Nigeria, Adelaja and George (2019) find that increased intensity of Boko Haram attacks reduced agricultural productivity.<sup>6</sup> Most micro-level studies evaluating the impact of violent conflict suffer from two major data-related limitations. First, because the outbreak of violent conflict disrupts traditional data collection efforts, evaluating the immediate impacts of an active violent conflict proves difficult. Second, tracking the trajectory of outcomes associated with violent conflict may require highfrequency data
# **5.7 Evidence of Pro-Local Policies**

## **5.7.1 Improvement in Local Educational Achievement**

We now present evidence that the counties led by YRD-affiliated cadres indeed adopted pro-local policies that facilitated local development. Education, a key component of human capital, can be an important source of local growth. Table 17 regresses the log changes in the average years of schooling for individuals born in 1950-1972 from those born prior to 1930, as observed in the 1990 Census, on the county’s YRD affiliation, Guerrilla presence and their interactions. As before, we control for the initial conditions and geography. Note that those born prior to 1930 would have been 22 years old in 1952, and would mostly have finished their formal schooling by then. Similarly, cohorts born between 1950 and 1972 would be at least 18 by 1990 and would have completed their formal schooling by then. We use the log differences in the average schooling of the 1950-1972 cohorts and the cohorts prior to 1930 as the proxy for the improvement in local educational improvement.

In Columns (1) and (2) of Table 17 where all the counties (with non-missing variables) are included in the analysis, we find that counties with YRD affiliations and with Guerrilla presence are associated with larger percentage increases in educational achievement, though the differences are not statistically significant. In Column (3) where we interact the YRD and the Guerrilla dummies, we find that counties with both YRD affiliation and Guerrilla presence are associated with 37.5 percentage points higher increase in average education, and the effect is statistically significant at 10% level.

In the right panel of Table 17, we trim the tail 5% of the counties from the analysis. In Column (4), we find that, relative to counties led by FD3 cadres, those led by YRD cadres had a 30.1 percentage points higher increase in average schooling levels; in Column (5), we include both YRD and Guerrilla dummies in
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Liberia’s air transport indicators benchmarked against West African neighbors**

||Benin|Cote<br>d’Ivoire|Gambia|Ghana|Guinea|Guinea-<br>Bissau|Liberia|Sierra<br>Leone|
|---|---|---|---|---|---|---|---|---|
|Total seats (millions)|0.42|1.15|0.25|1.88|0.32|0.07|0.16|<br>0.23|
|City pairs served|19|23|7|19|8|2|8|<br>8|
|Percent of seat kilometers flown in older aircraft|7|9|5|3|5|0|67|45|
|International market Herfindahl index (measure<br>of competition)|0.1|0.1|0.1|0.1|0.2|0.5|0.2|0.2|

Source: Gwillliam and others 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

# Water supply and sanitation

The civil conflict in Liberia adversely affected water and sanitation infrastructure in two ways. First, there was direct destruction and disrepair of existing infrastructure, as well as decline of the related institutions. Second, the conflict led to huge shifts of population out of rural areas into the cities and related camps and informal settlements, which put additional strain on urban utilities. The urban growth rate over the period between 1980 and 2000 was 3.8 percent annually. The population of Monrovia alone grew from 0.7 to 1.2 million people during the last decade of the conflict.

## **Achievements*
is thus essential if such an intervention is to be affordable and sustainable and greater interoperability can greatly enhance long-term benefits. At the same time, with LFS data showing that the current titling process reinforces pre-existing gender bias rather than alleviating it,<sup>28</sup> changes in procedures for women to assert and protect their property claims during land titling are essential. Unless such changes are made ahead of any program, efforts such as the National Land Titling Program may disempower women rather than facilitating their economic and social participation. Continued collection of the variables highlighted here would be important to monitor progress and ensure unintended negative impacts are avoided or, if they arise, are addressed quickly.

Our results suggest that Prindex together with other cross-national initiatives such as the World Bank’s Doing Business, is a complementary approach that can motivate debate on property but is unlikely to reach the level of granularity, detail, and continuity of national household surveys, particularly for agricultural land. If methodological issues identified here for Zambia are resolved, there are ample opportunities to

> 26 At the incremental cost (US$ 50,000, in addition to few days of long-distance support to programming and training) for administering the land tenure module in Zambia, some US$ 5 million would be needed to cover 100 countries. 27 The fact that such surveys are conducted in regular intervals (most LFSs are annual) offers opportunities to anchor land tenure in national statistics and generate data of high policy relevance that are in high demand from analysts. In Zambia’s case, data for the 2019 LFS are currently collected with an improved questionnaire that corrects some of the initial shortcomings identified here (e.g. in terms of non-response), at the NSO’s own initiative without additional funding from outside.

> 28 Our analysis shows that, even if transfers are possible, titles may make it easier to forestall female spouses’ input into transfer decisions.

18
types of rigidities in the two countries, suggesting a difference between a worker with 15 years of tenure in a firm and one with 5 years of tenure in the range from 0.9 to 1.6pp. Both of these features are in line with insider-outsider considerations. This evidence appears in contrast to previous evidence for the US, where in a survey of firm managers, Campbell and Kamlani (1997) find strong support for explanations of DNWR based on efficiency wage considerations and adverse selection, but little evidence in favor of the insider-outsider theory. Interestingly, gender, education, and type of contract seem to have little impact on the probability of being subject to either type of 22
population 100,000 or more, which allows comparison with the ENEU survey. The second is rural households in areas of population of 15,000 or fewer. Out of the 105 cohort-period observations, we have 82 observations in urban areas and only 53 observations in rural areas for which 100 or more households are surveyed within the cohort.

We examine mobility in four different measures of household resources. The first is household income from the primary occupation of each member, which is the measure used in the ENEU. The second, total monetary income, includes all household cash income, including income earned from transfers, pensions, rent, interest, and from non-primary jobs. The third measure, full income, adds non-monetary sources of household income, which includes the value of all home-produced consumption and of any goods received as transfers. The fourth measure is full expenditure, which includes all monetary expenditure and homeproduced consumption items. Over the six survey rounds household primary labor earnings has a correlation of 0.91 with total monetary income, 0.83 with full income, and 0.58 with full expenditure.

Panel A of Table 5 presents the estimated slope coefficients from equation (17) for these four measures. For urban households the four measures give very similar levels of absolute mobility. The estimates of β range from 0.86 to 0.89. The rural estimates range from 0.65 (primary wage income) to 0.80 (full expenditure). The point estimates would therefore suggest that there is more absolute mobility in rural areas than in urban areas, and that rural wage income is more mobile than rural expenditure. However the limited number of rural observations results in large standard errors and we can not reject equality of the rural and urban coefficients. The coefficient on log primary wage income for urban households is 0.87 compared to 0.93 for the equivalent measure in the ENEU data. This difference is not statistically significant.

Panel B of Table 5 adds cohort fixed effects and presents the
transactions may not be posted at all, as was found during an assessment of the Malawi FMIS. [10, 18] Posting transactions into the system after they have occurred only gives the illusion of comprehensiveness and control, while integrity cannot be ensured, and controls are actually not applied.

Coverage and utilization of FMIS is important as it determines how well the system can support expenditure control and gives a measure of the integrity of expenditure reporting. What underpins coverage and utilization is the actual transactions database. Yet, actual government transactions data are rarely analyzed despite their obvious value proposition: (i) revealed preferences of the executive to meet expenditure targets, and (ii) a measure of confidence in the integrity of government expenditure data. This paper is the first in exploring governments’ expenditure transactions more closely and 6
had BRL 254 billion in credit exposure to establishments that could be operating in protected areas. This amount is equivalent to 15 percent of the corporates’ credit portfolio. The exposure could increase to BRL 437 billion (25 percent of the corporates credit portfolio) should conservation gaps close, and to BRL 664 billion (38 percent of the corporates credit portfolio) should all priority areas become protected. Figure 10, Panel B, provides more details.

# _Environmental controversies_

Brazilian firms for which environmental controversies have been recorded and balance sheet data is available held BRL 109 billion in financial debt with credit institutions as of December 31, 2019. Out of 143 Brazilian firms, environmental incidents were recorded for 11 of them. Firms involved in severe or very severe controversies had debt liabilities of BRL 65 billion, while firms with moderate controversies were exposed for BRL 44 billion.

A well-known example of a very severe environmental controversy was the flooding and widespread environmental damage due to the Fundao tailings dam collapse, which caused flooding and widespread environmental damage in the state of Minas Gerais in 2015. In an

> 31 The HHI is calculated as a sum of the squared market shares (measured as a share of total corporate credit of the banking sector) of each bank in a market. An HHI of less than 1,500 typically identifies low concentration, an HHI between 1,500 to 2,500 identifies a moderately concentrated marketplace, while an HHI of above 2,500 suggests a highly concentrated marketplace.

16
(for return migrants) and at the time of the survey in 2012 (for current migrants). This allows us to account for both forced return migration and planned return.

While conflict occurrence in host countries constitutes a strong predictor of return migration, an empirical concern would be if conflicts might have consequences on the Egyptian economy through, for instance, changes in trade flows, foreign direct investments, or remittances. To address this concern, we additionally rely on data from the World Bank Development Indicators in order to control for FDI (% of GDP), remittances (% of GDP), and the exports and imports of goods and services (% of GDP) in Egypt in equation (4). We show in the robustness checks section that our results are robust to these two checks, which provides support to the exclusion restriction of occurrence.

# **5 Empirical findings**

## **5.1 Does the legal status of migrants matter upon return?**

### **5.1.1 Hourly Wages upon Return**

In Table 4, we depart from simple OLS regressions to estimate the effect of return migration on wages in 2012 upon return. We are interested in examining whether the legal status of migrants has a differential effect on their wage premium upon return. In column (1), we focus on the full sample of non-migrants and returnees. In column (2), we focus on the subsample of documented migrants compared to nonmigrants, and in column (3), we compare undocumented migrants to non-migrants. Without accounting for any of the selection issues presented earlier, Table 4 shows, that controlling for individual and job characteristics, return migration is associated with a significant wage premium. However, when we disentangle the effects by legal status, the results show that this positive association between return migration and wages holds only for documented migrants.

Accounting for the triple selection into migration, illegality, and return using a conditional mixed process model, in Table
then identified it as unpaid work). The share of women reporting unpaid work increases from 5.8 percent to 7.2 percent. With these changes, the share of employment increases from 37.4 percent to 41.2 percent.

Table 7. Participation rates in 2015 and 2018, women in rural areas

||**2015**|**2018**|
|---|---|---|
|**Employed**|30.3|29.6|
|**Employed - follow-up question**||2.1|
|**Unpaid work**|5.8|7.2|
|**Employed but temporarily out**|1.3|2.3|
|**Employed(total)**|**37.4**|**41.2**|
|**Unemployed (1 week)**|1.6|1.4|
|**Unemployed (1 month)**||1.0|
|**In labor force**|**38.9**|**43.6**|

Source: Author’s tabulations using the Encuesta de Hogares de Propósitos Múltiples (EPHPM) 2015 and 2018. Note: The table reports the percentage of women living in rural areas who are 18 and over.

Another change is the expansion of unemployed to include those who searched for work in the past month, not just the past week. After all of these changes, the estimated labor force participation rates for rural women from the 2018 survey are only marginally higher than they had been in 2015 (from 38.9 to 43.6 percent), suggesting that they did not fully solve the issue of underreporting. A total of 24.5 percent of all rural women (accounting for 43 percent of those out of the labor force in 2018) reported that
||(9.370)|(9.242)|(9.397)|(9.319)|
|---|---|---|---|---|
|Observations|4,276|4,276|4,276|4,276|
|R-squared|0.130|0.133|0.133|0.133|
|Year x Country FE|Yes|Yes|Yes|Yes|

Note: *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses clustered at the bank level. All independent variables are lagged one year.

Figure 12 shows the estimates from the regression results reported in column 3, Table 2. The figure shows the impact on z-score of increasing capital ratios by 10% after all controls. The lines represent one standard error around the point estimates. The impact of simple leverage ratios on bank risk is both economically and statistically more significant than the impact of regulatory ratios on bank risk. A 10% increase in simple leverage would result in a bank moving from median to the 60<sup>th</sup> percentile in risk rankings after all controls and after holding regulatory capital constant.

**Figure 12: Relationship Between Bank Risk and Regulatory Capital and Simple Leverage**

<!-- Start of picture text --> Z-Score<br> 9.0<br> 8.0<br> 7.0<br> 6.0<br> 5.0<br> 4.0<br> 3.0<br> 2.0<br> 1.0<br> -<br>RC/RWA Equity/TA<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and