rafmacalaba/data-use-mentions-extended huggingface dataset:
0.42|0|1|453161|
|Non-Farm Self-Employed|0.08|0.28|0|1|453161|
|Non-Farm Casual Work|0.06|0.24|0|1|453161|
|Non-Farm Salaried Work|0.06|0.23|0|1|453161|
|Farm|0.75|0.43|0|1|453161|
|Farm Worker|0.32|0.47|0|1|453161|
|Manufacturing|0.07|0.26|0|1|453161|
|Trade|0.04|0.2|0|1|453161|
|Construction|0.03|0.18|0|1|453161|
|Services|0.08|0.27|0|1|453161|

_Notes_ : This table presents summary statistics for rural individuals using the four rounds of the NSS data. Panel A restricts the sample to all rural individuals aged between 15 and 65; panel B restricts the sample to rural individuals aged 15-65 who participate in the labour force.

40
>.6 .6<br>.4 .4<br>.2 .2<br>Density 0 Density 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text -->

## _c. Dry months_

Notes: Histograms show the distribution of commune observations for each climate zone. Abnormal months are measured by the deviation of the current month’s value from the 30 years mean being greater than 1.5 standard deviations. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

To measure the number of wet, dry, hot and cold months, this study calculates the current deviation from the long‐term mean for each month (Figure A.5). Wet conditions are defined as positive deviation from 15
, and sector of work than unobserved features of their skill set or external environment.

In this paper, using data from nationally representative micro-level household surveys from almost 100 countries, we examine the characteristics of the self-employed throughout much of the developing world. Building on our profile of the self-employed, we use two admittedly coarse but nonetheless meaningful measures to classify the self-employed as successful: whether a self-employed worker is an employer as opposed to an own-account worker, and whether the self-employed worker lives in a non-poor household. Given data limitations, the analysis is unable to isolate which characteristics or factors cause some self-employed to be successful along these measures. Nonetheless, we can characterize the extent to which the currently unsuccessful self-employed possess basic traits that are correlated with success, which may lead them to have greater potential to become successful.

We first examine the characteristics associated with agricultural workers, and of nonagricultural employers, own account workers, non-paid employees and wage and salary
**|**553,456**|2,754,611|**398**|**387**|**5174**|**5031**|

Note: HH = household. Each cluster includes 13 households

# <u>Sampling Rohingya displaced households</u>

For camps, high-resolution satellite imagery, together with population and household count data generated by the humanitarian agencies (UNHCR and IOM), provided the base for selection of EAs and the refugee sample. The sampling frame used for Rohingya displaced in camps was publicly available population counts from the IOM’s Needs and Population Monitoring (NPM) Round 12 Site Assessment, which provided the most recently updated refugee population data at the time of survey design.<sup>17</sup> The data available identified 34

> 16 Walkable roads and streets were mapped using OpenStreetMap (OSM) road networks. Three hours’ walking time roughly equals 15 kilometers.

> 17 NPM Round 12 was completed in October 2018, and the sampling strategy for this survey was finalized in December 2018.

10
Data is grouped by month (j = 20) and subdistrict (i = 52). Not all subdistricts are represented in all months, but all are represented in at least two months.<sup>11</sup> There are 19 districts represented and the average number of months of data per subdistrict is just under 10 with a median of 9. To model the mediation effects we use the mediation package (Tingley et al. (2014)). This technique fits mediator and outcome models, then generates mediator predictors under control and treatment conditions. The outcome model is used to make predictions and calculate the quantities of interest outlined below. The outcome is a binary indicator of whether there is or is not return in a given month: Here the outcome Y is return from the OCHA data, the treatment T is violent events in the preceding month from the ACLED data, the mediator M is discussion of violence in the previous month on social media, and the covariates _Xi_ are log population, agriculture and district. In addition, we include an interaction term between the mediator and the treatment. We would expect the treatment to interact with the mediator in that a complete absence of violent events in a location would make it unlikely that there is any news about such events. The mediator is a continuous variable so we use a linear model for the mediator model with the same variables as above: In our outcome we have indirect or ‘causal mediation’ effects, ACME ( _δt_ ), the effect of changing the mediator status on the outcome given the same treatment (Imai et al. (2011)). In addition to this, we have the Average Direct Effect (ADE, _ξi_ ), the effect of changing the treatment status while maintaining the same mediator value, and the overall Average Treatment Effect (ATE).<sup>12</sup>

# **5 Results**

In this section, we present our findings on differences in content sharing in return and non-return
Policy Research Working Paper 10055

# **Abstract**

What are the preferred policy responses of host country residents to large migration inflows, and to what extent are these preferences driven by contact with migrants as well as values such as humanitarianism? This paper addresses these questions using new data on preferences of Colombians for responding to the large inflow of Venezuelans into their country. In a conjoint survey experiment, respondents selected and rated different policy packages comprising variations in six policy dimensions: (1) labour market access, (2) location restrictions, (3) public service access, (4) family reunification, (5) numerical limits, and (6) length of residency. The results suggest support for the options of conditional access to the labour market (i.e., only in certain occupations) or full free access to the alternative of no access. There is support for unrestricted location choices and access to public services, as well as conditional rights to family reunification (i.e., only if able to support dependants). Respondents also support the use of numerical limits and limiting the length of the residency permit. The results show that those who have less contact with Venezuelans, those who put more weight on economic priorities, and those who see the situation in Venezuela as mainly an economic problem, tend to support policies that are more restrictive..

This paper is a product of the Social Sustainability and Inclusion 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/prwp. The authors may be contacted at isabel.ruiz@bsg.ox.ac.uk .

_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,
First, we find no clear evidence that different obfuscation methods have an impact on estimates of agricultural production. At this time, publicly available remote sensing weather products are too coarse a resolution for any of the ten obfuscation methods tested to make a substantial difference in which pixel a household ends up in. Second, we find mixed evidence regarding how different metrics used to quantify precipitation and temperature impact estimates of production. Of the 22 metrics (14 rainfall and eight temperature) that we test, only six performed consistently well across different models and countries. These tended to be simpler metrics, such as total seasonal rainfall or mean daily temperature instead of more complicated metrics, such as deviations in rainfall from the long run average or growing degree days (GDD). That said, some metrics performed particularly well in specific circumstances, such as longest dry spell in Niger and the variance of daily temperature in Tanzania. Lastly, we find substantial evidence of variation in how data drawn from different remote sensing products correlates with agricultural production. Remote sensing precipitation products that merge gauge and satellite data, such as ARC2, TAMSAT, and CHIRPS, all perform consistently well across a wide variety of settings and tend to produce results similar to each other.<sup>1</sup> Precipitation products that rely on assimilation models, such as ERA5 and MERRA-2, tend to report much higher volumes of precipitation compared to the other remote sensing products. In some cases, data drawn from these different products result in opposite signs for coefficients on rainfall metrics, meaning that precipitation as measured by ARC2, TAMSAT, and CHIRPS purportedly increases crop output while the same metrics drawn from ERA5 and MERRA-2 purportedly reduces crop output. The starkly different results, between precipitation products that merge gauge and satellite data and those that rely on assimilation models, are not present in interpolated gauge data products or in any of the temperature products.

That measurement error exists in remote sensing data is important. There is a large body of literature that relies on remote sensing weather
communities and for women in their thirties and forties, who are less likely to live with young children requiring full-time care.

A thematically related paper, focusing as we do on children’s school attendance in Ghana, is Lavy (1996), which is based on data from the Ghana Living Standards Survey in 19871988. The focus was here more on schooling costs, assumed to be a positive function of distance to the nearest public school. Greater distance to the school is found to reduce school attendance; more in secondary than in primary school.

Other related studies have dealt with social impacts of improved access to other types of public goods. Banerjee et al. (2009) considered the impact of railroads on wages in China. Akee (2006) estimated the effects of road construction on wage employment and agricultural employment in the Republic of Palau.

5
.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8<br>0<br>.6<br>CCDF -.05<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C6. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Possession of assets indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C6.1. FOSD: Counting Vector C6.2. Dominance Analysis 53
</sup>|0.60|0.91|0.47|0.54|0.47|0.58|-0.110|(0.067)*|

_Note_ :<sup>+</sup> Maximum age is 14 in Burundi and Myanmar.<sup>#</sup> Survey includes vision, hearing, speech, and physical disabilities. ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels respectively. Standard errors in parentheses.

Of course this variability combines both actual prevalence and differences in survey techniques. However, in the countries with more than one survey (Cambodia and Jamaica) the survey with the more extensive definition of a person with a disability does not always result in the larger prevalence. The SES in Cambodia in 1999 characterizes 1.51 percent of the population as having a disability with an extensive definition, whereas the DHS in 2000 characterizes 1.57 percent of the population as having a disability with a definition restricted to physical disabilities. In Jamaica the more extensive definition in 2000 characterizes 2.77 percent as having a disability—more than the 2.09 percent identified in 1998 with a more limited definition. Clearly there is substantial variation 6
gives the ratio of</mark> **<mark>Actual Evapotranspiration</mark>** <mark>to</mark> **<mark>Total Water Requirement</mark>** <mark>(i.e. A/B in the above) for a season. ‐ The</mark> **<mark>Actual Evapotranspiration</mark>** <mark>represents the actual amount of water withdrawn from the soil water reservoir and is calculated indirectly using rainfall data within a model of soil water balance. It requires inputs of time‐variable rainfall data and soil and crop‐specific data (soil water holding capacity and crop‐ specific maximum allowable water depletion);</mark> <mark>‐ The</mark> **<mark>Total Water Requirement</mark>** <mark>is calculated as (Potential Evapotranspiration x Crop Coefficient). It requires empirically‐derived crop coefficients to relate the reference evapotranspiration to the relevant crop, and time‐variable climate data inputs of;</mark> <mark>`o` Solar radiation (sunshine);</mark> <mark>`o` Air temperature;</mark> <mark>`o` Humidity; and</mark> `o` Wind speed.

<mark>The reference evapotranspiration expresses the evaporating power of the atmosphere at a specific location and time of the year. Both average and recorded values are suitable for use in the methodology described above, with recorded (actual) values preferred.</mark> <mark>Sources: (Heofsloot & Calmanti, 2012), (Frère, M, and Popov, G, 1979), (Frère, M, and Popov, G, 1986), (Abraha, M, 2013),</mark> <u><mark>(Doorenbos, J. & Kassam, A, 1979).</mark
then rely on their general beliefs about their contextual circumstances in their search for answers to survey questions (Beegle et al. 2012). In the setting of sub-Saharan Africa, if the work of girls at the farm is more variable and has a less predictable pattern, we would expect to find that selfand proxy-reporting of child labor would be particularly prone to discrepancies. On the other hand, if farm work for boys is typically more frequent and regular, it would be easier for both proxy and boy respondents to rely on recall-and-count strategies, and thus we would expect to find less self/proxy reporting discrepancies for boys.

The seasonality of coffee production in the agricultural calendar not only affects the share of children who are working but it could also affect the extent of self/proxy reporting discrepancies. If a particular agricultural season involves child work activity with routinely steps and predictable timing, or involve the complementarity of child/adult work due to the physical strength requirements of farm activities, we would expect to observe smaller discrepancies in self/proxy reporting of child labor relative to other agricultural seasons where salient work activities do not require physical strength and, thus, can be done by children in their own. Likewise, when the labor demand is at its highest (so that the heads of households are actively busy), or at its lowest (so that the heads of households have to allocate time to outside off-farm activities to supplement income), we would expect to observe higher self/proxy discrepancies in child labor statistics as monitoring of child activities becomes more costly.

We find statistically significant gender variation in self/proxy discrepancies in child labor statistics in rural Ethiopia regardless of the age of the child or whether one uses 30-day or 7-day recall interviews. Girls report higher farm labor participation relative to proxy respondents within and across agricultural seasons. For boys, on the other hand, we do not find self/proxy information gaps across all seasons. These discrepancies in child labor statistics show variation across seasons 5
that should not be affected by the program (sex and age of head of the household). This clearly invalidates the underlying assumption necessary to perform a regression discontinuity design given that individuals would be different in some observables around the threshold.

Summary statistics for the final matched dataset (electoral census + SIFA + SISBEN) are reported in Table 1. Panel A reports statistics for the sample at the individual level, which is used for the analysis on registration to vote. Approximately 46% of the individuals in the sample are eligible to benefit from FA but around 42% of the eligible (19.6%) actually participate in the program.<sup>10</sup> 93.2% of individuals are registered to vote in the elections, which is similar to the rate calculated using a political survey done in Colombia, _LAPOP_ , 90%. 28.3% people in our sample registered to vote after the onset of FA on the municipality. The average person is 38 years old and has seven years of education at the moment they were interviewed for the _SISBEN_ ; more than half (58.8%) of individuals in the sample are women. Panel B, reports individual level statistics but restricting the data to females due to the fact that mothers are the direct recipients of the transfer. In total we have over 2 million women, with 25.5% eligible for FA and 95.5% registered to vote. Overall, the characteristics of this group are similar to those reported in Panel A for the complete sample.

Panel C of Table 1 in turn presents descriptive statistics for the booth-level sample used to estimate the effects of FA on voter turnout and choice. People registered to vote in urban centers were assigned to 103,367 voting booths, each of them with an average of 420 individuals. Turnout rates at the booth level for both Presidential elections (first round and runoff) were close to 60 percent.<sup>11</sup> Finally, the
.2 Estimated location premia by municipality type_

In line with the steep rise of population density along the urban gradient (Table 2), the denser the municipality, the further to the right is its distribution (Figure 3) and the higher its average location premia (Table 4). This is expected as economic agents benefit from stronger agglomeration economies and better learning externalities and market access in municipalities with higher population density. However, the rural-urban differences in average location premia at the end of the 2010s are small in most cases (last two columns of Table 4). Rather than a clear rural-urban-metropolitan divide, there is a gradation in labor and place productivity premia by type of municipality.

The location premia density charts, displayed in Figure 3, overlap in all cases to different extents. In Mexico, the highest location premia are not observed in the largest metropolitan areas, but in a few smaller metropolitan areas, large urban centers, and even some rural municipalities. Still, because the variation of location premia in larger metropolitan/urban areas is smaller, on average the location premia in less dense rural municipalities are lower than those in denser metropolitan areas.

In Brazil, the overlap of the rural and urban density plots is much less pronounced. There is a clear double-hump rural-urban pattern of lotion effects, which shows that the majority of rural municipalities have lower productivity premia than the majority of urban or metropolitan municipalities. In Colombia, the overlap in location premia by type of municipality is akin to the one observed in Mexico.<sup>17</sup> In Chile and the Dominican Republic, except for a few urban municipalities

> 17 We do not have data to estimate location premia after sorting by type of settlement in Colombia because SEDLAC data do not offer household information below the 1<sup>st</sup> administrative region.

15
mathematics, and whether this is due to the selection of high ability children in school. In Figure 20, we show that at the baseline, schooled children have higher score levels and progression over their age compared to not schooled children. We see no differences between treated and control villages. This relationship holds in the endline. However, at the time of the endline, we observe that the unschooled children in the treated group have slightly lower levels of mathematics compared to control group. Visually, this suggests that perhaps it is the lowest ability children which were kept unschooled in the treated areas, or put another way, that the treatment effects on increasing enrollment with cash transfers were more focused on higher ability children. The regression results presented in Table 8 do not, however, confirm this suggestion.

# **IV. Results on Vaccines**

One of the program’s objectives was to increase vaccination rates. Vaccinations are free and can be done at primary health centers when there is supply. Data on vaccination of infants shows that in 2012, 78 percent of children aged 12 months in Guinea had a vaccination card. This percentage had been increasing steadily. We used the 2012 ELEP household survey that is representative of the population, and we find that vaccination rates increased from 70 to 78 percent of children between 2007 and 2012. This is shown in Figure 21, which we created from the 2012 ELEP household survey.

13
money are likely to offer more credit to customers and also demand more credit, while male owned-firms do not. One potential explanation is that mobile money builds trust between womenowned businesses and suppliers and customers, providing opportunities to expand and invest. Alternatively, mobile money may provide liquidity to female-owned firms, allowing them to lend to customers. The provision of customer credit is more under the control of businesses than supplier credit, which is ultimately a decision by the supplying firm. Gosavi (2018) uncovered a positive relationship between mobile money and access to credit for a smaller sample of economies in East Africa.

This study builds on the emerging literature exploring mobile money adoption by businesses, and its benefits. This literature has typically focused on a single or a handful of economies, mainly in East Africa. Gosavi (2015) investigates the characteristics of businesses that use mobile money in Kenya, Tanzania, Uganda, and Zambia and explores the relationship between mobile money and access to finance for the same sample (Gosavi, 2018). A precursor to this study, Islam et al, (2018) find a positive relationship between mobile and investment for firms in Kenya, Uganda, and Tanzania. Beck et al (2018) model the relationship between the adoption of mobile money, entrepreneurial growth, and finance, calibrating their model to match firm-level data from Kenya. Bastian et al. (2018) experimentally explore the relationship between mobile money savings and the growth of female-owned micro-businesses in Tanzania and Indonesia. This study is the first to exhaustively explore the relationship between mobile money and investment for formal firms in 16 economies across Sub-Saharan Africa. It also provides important descriptive information for policy makers on adoption rates and the reasons for mobile money use by firms across Sub-Saharan Africa.

This study also relates to the literature on access to finance for women entrepreneurs, a topic of great interest given that financing obstacles hold back firms (Beck et al., 2005 and Ayyagari et al., 2008) and that discrimination in access to finance limits growth