rafmacalaba/data-use-mentions-extended huggingface dataset:
married women for the 2018 wave. In one-third of the sample, all men aged 15–49 were also eligible to be interviewed. For consistency, we also restrict the sample of men to married men. The DHS surveys provide geo-referenced information on the centroids of village/household clusters.<sup>8</sup> _Conflict data._ We use data from the Armed Conflict Location and Event Data Project (ACLED). ACLED collects data about political violence and protests from secondary sources on the date, location (GPS coordinates), actors involved, event type, and number of fatalities.<sup>9</sup> We aggregate attacks in five-year intervals to merge them with the DHS data. Therefore, we use attacks from 2008 to 2012 for the 2013 DHS wave and attacks from 2013 to 2017 for the 2018 wave.

For the analysis of jihadist attacks, we restrict the data to attacks from 2008 to 2018 in which at least one of the actors involved belonged to Boko Haram or Islamic State. The ACLED data register 2,359 violent events between 2008 and 2017 in which at least one of the actors involved belonged to Boko Haram. Forty-seven percent of events are related to battles, 30.4 percent to violence against civilians, and 17.5 percent to explosions and remote violence, while 5 percent are classified as “strategic developments.” During this period, the ACLED data register 28,001 deaths that resulted from attacks involving Boko Haram. A large percentage of all events occur in the Northeast region (Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe). For instance, 71 percent of events take place in Borno, 9.2 percent in Yobe, and 8.1 percent in Adamawa (these statistics are not displayed in any table).

We use these data to build variables that count the number of Boko Haram attacks

> attacks nor distant Fulani attacks. Results are available upon request.

> 8In order to ensure that respondent confidentiality is maintained
prior success (Machin, McNally, and Meghir 2010).

In Appendix Table A5, we cross academic readiness with socio-economic status measured by the IHA. We found that the positive effects of PROBEMS on EMS graduation rates among students with academic _readiness_ hold both for poor and non-poor households, but are statistically significant only for non-poor households. The lack of statistically significant effects among poor students that show academic _readiness_ could be explained by the relatively low number of poor students in the upper tercile of the 9th grade test score distribution.

> 13Results available upon request.

21
). This is due to two reasons. First, Statistics South Africa only has a partial map of the census enumeration areas in 1991. Therefore, part of the census data cannot be geographically referenced. Secondly, due to violent turmoil at the time, some areas could not be visited by enumerators and no data are available on a granular level. This is potentially beneficial for our analysis since we exclude areas with high racial tension, which could otherwise bias our results. So, while this reduces the number of observations and therefore the statistical power in the empirical analysis, our parameter estimates will remain consistent.

18
) Distribution of mangrove width (km) c) Distance to the coast (km)<br>on nightlights (NL)<br>Source: Authors’ calculations based on data from LECZ database.<br>Notes: In models 1–3, from panel (a) we discretize the mangrove width variable into various bins that correspond to its q-<br>quantiles and estimate the impact of hurricanes on economic activity for each bin. Model 1 uses q=2 and is colored green.<br>Model 2 uses q=3 and is colored orange. Model 3 uses q=4 and is colored purple. Panel (b) plots the distribution of mangrove<br>width for each bin, and panel (c) plots the distribution of distance to the coast for each bin. Regression coefficients are shown<br>in panel (a) and the average value for panels (b) and (c).<br><!-- End of picture text --> In general, the results show the potential value of mangroves as natural defenses against hurricanes in the Caribbean region, reflecting the importance of their conservation and restoration. These findings are consistent with other estimates of mangrove protection, which indicate that 2–7 kilometers of mangrove width would be needed to fully attenuate storm surge for the hurricanes that make up the bulk of our sample, particularly for Category 3. We find similar results when using a wide range of alternative assumptions for constructing the hurricane damage function, calculating mangrove width, and specifying models.

> 28 The calculation was as follows: [ ≈ −0.20 ∗0.2 ] *[1/12.1] 29 Following the same procedure to find the percentage, using the coefficient -0.36.

19
|FDI upstream service×Top firms|(0.059)|-0.036*<br>(0.017)|||
|---|---|---|---|---|
|Sector-by-year trend<br>Year FE|Yes<br>Yes|Yes<br>Yes|Sector-by-year trend<br>Year FE|Yes<br>Yes|
|Firm FE|Yes|Yes|Firm FE|Yes|
|Observations|1477|1477|Observations|707|
|Firms|322|322|Firms|153|

Note: The table reports the estimates of linear regressions. Standard errors clustered at the sector level are reported in parenthesis. Significant at *10%, **5%, ***1%. Results refer to the entire period. All independent variables are lagged one period. Distance from the frontier is time invariant and is measured by the difference between the average productivity of a firm in the first 2 years, and the average productivity of firms in the top quantile of the distribution. Frontier firms are those in the top 2 quintiles of the distribution of average TFP in the first 2 years.

Finally, we observe that the effect on productivity possibly materializes through an increase in innovative activities. For the subsample of firms that have invested in intangible assets, our proxy for innovation, at least once over the period (innovators in column 3 of Table 12) we find that FDI in upstream services is positively associated with innovative investment. Hence, this evidence is suggestive of the link between upstream FDI in services and downstream firms’ productivity operating through the mechanism of increased investment in innovation. The existence of knowledge spillovers from FDI in services has been recognized in the literature (Blind and Jungmittag 2004). Foreign-owned firms tend to enjoy some technological advantages that make their engagement with the domestic market profitable. In the service sectors, these largely involve intangible assets, which, given
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _26_ There is also a benefit to relying on CPI inflation alone. Macro-economic frameworks and monetary policies are centered around general consumer prices. For instance, central banks often focus on long-term inflation metrics less impacted by short-term price volatility, and consider a “core CPI” for this that excludes items such as food, shelter, energy, and used cars and trucks. In other cases, an index of Personal Consumption Expenditures (PCE) is preferred which similarly excludes volatile commodities. This suggests also that monetary policies aimed at stabilizing core CPI or PCE can in essence be reached without normalizing food prices and so it is an important question whether restoring the CPI inflation rate is sufficient to improve global food insecurity conditions. The simple specification of the model made investigating that through counterfactual predictions much simpler. The counterfactual analysis revealed that stabilizing domestic inflation in the ECA region would be sufficient to revert hungry populations back to prepandemic levels there, but not in other regions. This is a potentially important finding that would be worth investigating further, particularly given the result that international prices need to come down to keep development assistance needs to manageable levels in these regions.

A second possible shortcoming is that the percentage of forest land cover provides only a meager description of climate factors. The first challenge to improving this is that most climate data sets describe phenomena that are inherently spatial and not at all straightforward to summarize at a national level, particularly for larger countries. For instance, rainfall and NDVI (Normalized Difference Vegetation Index) data easily capture area-specific droughts and floods, and have shown to be highly predictive of local severe food insecurity conditions in some of the work that has been cited earlier. However, the average rainfall level measured over the entire area of countries like Brazil, the Russian Federation or China would hide any and all local climatic shocks. Recent work has focused on mapping subnational agricultural GDP (Blankespoor et al., 2022) and such approaches could in
being the other activity, since the trend flips in Table 12 (B2). On reported time, however, since activities in the recall sample could be conducted together for a minimum of 15 minutes, the recall arm often reveals significantly higher time conducted together in simultaneous activities – though, the magnitude of the differences is quite small given the substantially lower participation rates, and, in the case of men, often not significant. An understudied area, however, that the smartphone data provides some initial insights on, is the prevalence of passive care, where children are present with adults but not directly engaged in their activities (Folbre et al., 2005). Through the _TimeTracker_ pop-up data on whom activities were conducted with (Table 13), we find that about 16 percent of women’s daily time, or about 144 minutes daily, is spent with young children (aged 0-9 years) in activities that are not actively reported as care or sleep (and thus over and above the direct time reported earlier in childcare). Most of these activities are in other areas of unpaid work, transport, or leisure, and while performing these activities, women spend roughly twice as much time with young children compared to men. Gender differences in the extent of time devoted to activities in the presence of older children (aged 10-17 years), however, are narrower, reflecting important differences in the way we need to consider passive care in time use data collection. Although the _TimeTracker_ pop-ups provide an imperfect view of passive care, the data shed some light on a significantly less-understood dimension of childcare that may not be wellcaptured by simply asking individuals whether they were involved in childcare provision.

Table 14 also presents treatment effects, using the same structure as Equation (1), and looking at the recall arm’s effect on whether (1) activities were conducted together under different combinations, and (2) shared minutes across those activities. The estimates are presented for the total, women, and men samples,
within a single province which would allow for a detailed examination of the nature and sources of variation in service delivery. This allows for fixing macro-level economic, social, and institutional characteristics than can confound the interpretation of any correlation between political characteristics and service delivery.

Two villages, or barangays as they are called in the Philippines, were selected within each municipality on the basis of population and poverty, to ensure representation of the poorer households in the municipality (who are the primary recipients of the key public services provided by the municipality, especially in health). Very small barangays were excluded from potential selection to avoid the likelihood of greater political control over household responses to the survey in closely linked small settlements. In the first stage, the poorest half was selected from among all barangays in the municipality on the basis of small area poverty estimates.<sup>16</sup> Second, the largest 2 barangays (in terms of rank of population) was selected from among this poorest half.

Households with young children were targeted by the survey to measure access to one of the key public services that municipalities in the Philippines are charged with—maternal and child health programs. From each of the barangays, a population list was obtained by the survey team leaders of all households having at least one young child (under 6 years of age).<sup>17</sup> From this list, the survey leaders made a random selection of 20 households in each village. Within each household, the survey targeted the mother of the young child (or children) as the key respondent. All the respondents were women, with the average age in the sample being 31 years. Thus, the total household sample for this study is 1200, drawn from a total of 60 villages across 30 municipalities within the same province.

_Political access to a pro-poor policy instrument_ The Local Government Code of 1991 made municipal governments critical agents in the delivery of health programs such as immunization, reproductive and child health, and nutrition
**|
||||(1% price|(6% price|||(1% price|(6% price|
||||<br>decrease)|<br>decrease)|||<br>decrease)|<br>decrease)|
|_Residence_|||||||||
|Urban|0.01|0.13|0.12|0.70|0.22|0.10|−0.12|−0.73|
|Rural|0.07|0.28|0.20|1.21|0.33|0.18|−0.15|−0.88|
|_Region_|||||||||
|North|0.07|0.26|0.19|1.14|0.29|0.16|−0.13|−0.74|
|Central|0.09|0.29|0.20|1.17|0.35|0.19|−0.17|−0.98|
|South|0.06|0.27|0.21|1.25|0.28|0.16|−0.12|−0.69|
|**Malawi**|0.06|0.24|0.18|1.09|0.32|0.17|−0.15|−0.88|

Source: Authors’ calculations based on IHS4 data. NBR is estimated based on equation 11. Self-sufficient households would neither gain nor loss due to change in maize prices.

26
# A. IPV Often Index

|p=.039 / b=-0.482<br>Boys, had sex||
|---|---|
|p=.030 / b=-0.378<br>Goal, had sex||
|p=.338<br>Boys, not had sex||
|p=.088 / b=-0.133<br>Goal, not had sex||
|-1<br>-0.5<br>0<br>0.5|1|

# B. IPV in last year Index

|p=.107 / b=-0.282<br>Boys, had sex|
|---|
|p=.023 / b=-0.444<br>Goal, had sex|
|p=.368<br>Boys, not had sex|
|p=.624<br>Goal, not had sex|
|-1<br>-0.5<br>0<br>0.5<br>1|

**Figure 6** Impact of Treatments on IPV, Heterogeneity by ever had sex at baseline

_Notes._ This figure presents estimates of _β_ 1 and _β_ 2 and from equation 1. Panel A presents estimates for the IPV often index and Panel B presents estimates for the IPV in last year index. The indices are centered on females in ELA only communities who were sexually active at baseline and who were not assigned to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker. _Source._ Female baseline and endline data, balanced panel. The sample in the top half is restricted to females who had ever had sex at baseline. The sample in the bottom half is restricted to females who had not ever had sex at baseline
internal rates of return. Additionally, we examine how part-time work, taxes and tuition fees influence the return to education in order to understand which factors are economically important for the individual in her choice of re-entering the education system or not. We use earnings and education data from six years of household surveys, 1995-2000, and combine it with institutional data. The paper contributes with new knowledge, since a limited number of similar exercises exist for high-income countries, but few for low and middle-income countries.

Our key finding is that the rate of return to education in Colombia remains profitable for the individual for all levels of education up till at least the age of 40, hence half of life. Therefore, there seem to be important gains for adult workers to formal lifelong learning. Further, we find that (i) foregone income critically influences returns; (ii) the length of studies through its impact on foregone income strongly affects returns; (iii) tuition fees reduce returns, in particular for primary and secondary education; and (iv) tax-incentives seem to play only a marginal role for returns to education. The findings suggest that lifelong learning is economically highly rewarding and should in particular target the agecohorts between 20 and 40 years old.

This paper is organized as follows: section II introduces the basic information on school attainment, labor market income, and adult school enrolment to situate the subsequent analysis within its national context. Section III describes the model and methodology of the internal rate of return. Section IV presents and interprets results, while the last section summarizes. A description of the data and detailed estimation results can be found in the annexes.

3
on both primary and secondary education (grades 4 and 8). In 2007, the fourth survey covered grades 4 and 8 and more than 66 educational systems. In 2011, the survey covered 77 educational systems across grades 4 and 8. The last round was performed in 2015 and covered 63 countries/areas. The precise content of the questionnaires varies but remains systematic across countries.

_PIRLS_ **.** The Progress in International Reading Literacy Study (PIRLS) survey is also conducted by the IEA. The PIRLS tests pupils in primary schools in grade 4 in reading proficiency. Four rounds of PIRLS have been held to date in 2001, 2006, 2011 and 2016.

In 2006, PIRLS included 41 countries/areas, two of which were African countries (Morocco and South Africa), 4 lower middle-income countries (Georgia, Indonesia, Moldova, Morocco) and 8 upper middle-income countries (Bulgaria, Islamic Republic of Iran, Lithuania, Macedonia, Federal Yugoslavian Republic, Romania, Russian Federation, South Africa). The 2011 round of PIRLS was carried out alongside TIMSS and included 60 countries/areas. The newest round of PIRLS in 2016 includes 50 countries.

_PISA_ **.** The Organization for Economic Co-operation and Development (OECD) launched the Programme for International Student Assessment (PISA) in 1997 to provide comparable data on student performance. Since 2000, PISA has assessed the skills of 15-year-old pupils every three years. PISA concentrates on three subjects: mathematics, science and literacy. The framework for evaluation remains the same across time to ensure comparability. In 2009, 75 countries/areas participated; in 2012, 65 countries/areas participated and in 2015, 72 countries/areas participated. An important distinction between PISA and IEA surveys is that PISA assesses 15-year-old pupils, regardless of grade level, while IEA assessments assess grade 4 and 8.

# _B. Regional Standardized Achievement Tests (RSATs)_

In addition to the above international assessments, a series of regional assessments have been conducted in Africa
In addition, variables on resource rents are also used to proxy for commodity windfalls. The data on natural resource rents come from the Changing Wealth of Nations data set of the World Bank (2011) available from the World Bank’s World Development Indicators (WDI). Natural resource rents are defined as the difference between the unit price of resources and their unit cost of extraction, multiplied by the volume of resources extracted. Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. The data have been widely utilized in the literature (Klomp and de Haan, 2016; Arezki and Gylfason, 2013). Summary statistics for the sample of analysis are provided in Table A1.

# _2.3 Tax Data_

Tax data are obtained from the UNU-WIDER ICTD government revenue data set (Prichard et al, 2014). The data set combines several sources of tax data compiled from IMF Article IV reports, thereby ensuring extensive coverage. These include IMF Government Finance Statistics (GFS), World Bank World Development Indicators (WDI), OECD Tax Statistics, OECD Revenue Statistics in Latin America dataset, CEPAL Tax Statistics, and the AEO African Fiscal Performance. The data set includes a separate category for resource tax revenues, in addition to several other tax breakdowns. Data are available from 1980 to 2017. Summary statistics for the sample of analysis are provided in Table A1.

# **3. Estimation Strategy**

In this section we present our empirical strategy.

To explore the effect of commodity price shocks on billionaire net worth, we estimate the following equation: L 0<sup>+ ββ</sup> 1<sup>D</sup> (2) ii,tt<sup>= αα</sup> cc,tt<sup>+ γγ</sup> ZZ<sup>DDLLIICCDDLLDDCC</sup> cc,tt<sup>+ ττ</sup> tt
Education levels are strongly related to poverty. That is, having incomplete or some complete education is important in determining the likelihood of being poor. In Mexico, the P0 is 45 percent for household heads that have received no education in dispersed rural areas and 28 percent in semi-urban areas in 2002.

<!-- Start of picture text --> Figure 4.1: Poverty headcount (P0) and education level attained,<br>rural Mexico (<15,000 people), 2002<br>60.00<br>No education<br>50.00 Primary incomplete<br>Primary complete<br>Secondary complete<br>Higher education<br>40.00<br>30.00<br>20.00<br>10.00<br>-<br>Total Norte Capital Golfo Pacifico Sur C-norte Centro<br>Source: Calculations based on ENIGH 2002.<br><!-- End of picture text --> There appears to be a relatively large difference in extreme poverty between household heads with no education (45 percent) and household heads with incomplete primary education (36 percent) in dispersed rural areas. Surprisingly, there seems to be very little difference in extreme poverty between household heads with incomplete primary education and household heads with complete primary education (36 percent), the latter up from 31 percent in 1992. Nevertheless, household heads that have completed secondary education are much better off (18 percent are poor) than those with only primary education. Extreme poverty for high-school graduates decreased rapidly during 1992-2002, down from 22 percent in 1992. Of the household heads with some or complete tertiary education 13 percent were extremely poor in 2002, up from 1 percent in 1992. These findings indicate that education is a very important key to poverty reduction in rural Mexico. It is not a silver bullet, however, as the more educated are increasingly experiencing poverty, something that was rare a decade ago.It is
business environment. In general we rely on objective measures to avoid endogeneity of firm responses. We avoid using the so-called subjective measures, which record a firm’s perceived obstacles to doing business. Only for questions related to crime do we use the subjective measures due to its importance in less developed countries and the lack of alternative data sources.

For indicators of the business environment from WBES, we do not directly use individual answers because we believe they are endogenous. We thus use the local average of the business environment as a proxy for the local business environment (Dollar et al. 2005; HallwardDreimeier et al. 2006; Aterido et al. forthcoming; Xu 2011). In particular, we opt to rely on a city-industry-size cell as the basic unit for measuring the local business environment. In computing the mean for a firm, the observation for the firm itself is excluded to avoid endogeneity. The business environment has been shown to differ vastly across regions (Almeida and Carneiro 2009; Hallward-Driemeier and Pritchett 2010; Xu 2011) so we allow for a cityspecific dimension. The literature also suggests that firms of various sizes face different business environments. In particular, small firms are particularly vulnerable to expropriation (Beck et al. 2005; Cull and Xu 2005), so we allow the business environment to differ by size. To implement the empirical strategy, we define a firm to be small (large) if the firm has fewer (more) number of employees than the median-sized firm of the city-industry cell.<sup>9</sup> We also rely on cross-country data to capture important elements of the business and political environment that the Enterprise Surveys do not measure, including elements such as geography, infrastructure, the political and institutional environment, and macroeconomic policies. Aiming to be comprehensive, we combine measures of the business environment from

> 9 When the city-industry-size cell has fewer than 5 observations, we replace the cell mean with the city-industry mean as a proxy of the local business environment.

7