rafmacalaba/data-use-mentions-extended huggingface dataset:
|-|Mode of transportation to school|48|27|
|---|---|---|---|
|Reaso|ns for not consultinghealth care|||
|-|Distance to the facility|89|54|
|-|Lack of transportation|19|18|
|Inform|ation on health care utilization|||
|-|Distance/time to health care facility|24|16|
|-|Transportation to health care facility|17|12|
|Health|expenditure on transportation|39|30|
|Transp|ort related externalities - Road Safety|||
|-|Road crash fatality|12|10|
|-|Road crash injury|45|36|
|Owner|shipof vehicles|||
|-|Car/truck|295|111|
|-|Motorcycle/scooter|261|100|
|-|Bicycle|233|93|
|-|Boat|130|74|
|-|Animal-drawn cart|111|68|

The Demographic and Health Survey (DHS) is the flagship program of the United States Agency for International Development (USAID) mandated to produce household survey data on topics such as population, health, and nutrition. This program operates in more than 90 countries and emphasizes the statistical capacity building of NSOs in the design and implementation of surveys, as well as data analysis. In the most recent phase of DHS surveys, the household questionnaire collects the information on household ownership of different types of vehicles including car or truck, motorcycle or motor scooter, bicycle, boat with a motor, and animal-drawn cart.

In addition, information of road crash fatalities and injuries are collected in many DHS surveys (e.g., Ethiopia, Cambodia, etc.) and the health module of national population censuses and other surveys. Traffic injuries are part of broader transport issues, as lack of access to safe transport is faced by many LMICs, which
finds the head’s sister and his niece. This could be a two cells or a three cells household, depending on the relationship between these two extra members. If the niece of the household head is also the daughter of his sister, then the two constitute a third cell, while if they are not related, both will be in the head’s cell. To sum up, we are able to identify four kinds of households in the ESPS sample (and of course also in the PSF): (1) single person households, (2) one cell households, (3) more than one cell households with only “nuclear” family members and their offspring (head, spouse(s), children, grand-children), (4) other households. For reasons just explained, some truly one cell households may be misclassified as “other” households, but the opposite cannot happen.

We apply this classification rule to both the PSF and ESPS samples and compare the estimated average household per capita food and total consumption for each category of households and for the entire samples. In order to make meaningful comparisons, we need to account for any sampling frame variation and for the fact that about one year separates the ESPS and PSF surveys. Simple unweighted comparison of surveyed household locations shows that PSF oversampled the Dakar region compared to ESPS (37.5% in PSF versus 11.8% in ESPS). Since households in Dakar are on average wealthier than those in other regions of the country, a simple comparison of average consumption levels is unlikely to reflect solely differences in survey design. Using the survey weights helps to correct imbalances but not completely. The weighted proportion of households in Dakar is still 3% higher in PSF than in ESPS (29.5% for PSF versus 26.6% for ESPS) and very large imbalances appear between the Matam (PSF: 12.8%; ESPS: 3.4%) and Thies (PSF: 6.4%
previous census. One can update the population using population growth at some aggregate level. If the population is updated at a more granular level than administrative units, changes in the population distribution across areas can at least partly resolve problems due to outdated data. However, any adjustments will not change the PSU’s probability of being selected in the first stage if the adjustments are made at the administrative units or more aggregate level, such as regions. The reason is that the PPS is specific across administrative units, and any monotonic transformation of PSU size within the administrative units cannot change the PSU’s selection probability. Therefore, the population and number of households should be adjusted at a level more granular than administrative units.

# **2.2 Electoral Precincts**

The data on electoral precincts is publicly available and generally trustworthy as the official general election committee publishes the data. There are two advantages to using this dataset as a sampling frame for household surveys. First, the data is regularly updated and current. Second, there are 1,992 electoral precincts, more than the 1,037 settlements in the 2011 Census settlement data. Therefore, the spatial information is relatively more granular than the 2011 Census data, and problems associated with a few large-sized PSUs are less severe than those under a potential sampling frame based on census settlements.

However, like the “settlements” in the 2011 Census data, employing electoral precincts as PSUs also poses a problem associated with large-sized PSUs. It is also worth mentioning that there are also some small-sized electoral precincts, which are less problematic. As stated in Pettersson (2023), the size of voting point areas in Armenia ranges from 7 addresses to 1,200 addresses. The small voting point (VP) areas can be combined with a neighboring area or neighboring areas, and large VP areas can be divided into smaller segments. The procedure for merging multiple VP areas should be straightforward; however, segmenting large areas could be costly as it requires some spatial analysis and
|**Tool name:**|**123PRSP**|
|---|---|
|**What is it?**|123PRSP (one country, two sectors, and three goods) is a static CGE model.|
|**What can it be used for?**|123PRSP can be used to analyze the impact of macroeconomic policy and external shocks on income<br>distribution, employment, and poverty.|
|**What does it tell you?**|It allows for a forecast of welfare measures and poverty outcomes that is consistent with a set of<br>macroeconomic policies in the context of a very simple general equilibrium model. For a given set of<br>macroeconomic policies, 123PRSP generates a set of wages, sector-specific profits, and relative prices<br>that are mutually consistent. The projected changes in prices, wages, and profits are then inputted into<br>household data on wages, profits, and commodity demand among representative groups or segments of<br>the distribution. In principle, 123PRSP can calculate the policy impact on each household in the sample<br>so as to capture the effect on the entire distribution of income. For a given poverty line, 123PRSP can<br>also compute the effect of different poverty measures.|
|**Complementary tools:**|Analysis of impacts on income distribution could be complemented by social impact analysis and<br>institutional analysis, which look at variables that would affect household participation in growth.<br>Scenario analysis, which helps policy makers assess the effects of major discontinuities on economic<br>projections,could complement CGE models operatingon a long-time horizon.|
|**Key elements:**|123PRSP can be viewed as a middle ground between consistency models such as RMSM-X and more<br>sophisticated approaches such as disaggregated CGE models. The former are simple to estimate and<br>use,
only properly reproduce the pre-treatment outcomes averaged over all twelve treatment forests, but also those for each of the treatment forests separately (see Figure C.2 in Appendix C). And this holds not only for the case of forest fires that are detected with a 30% confidence level, but also when using the 50% criterion; see Figure C.3 in Appendix C. Our results are thus robust to changes in forest fire detection confidence levels. And our results are also robust to excluding those non-treatment forests from the analysis that are contiguous to treatment forests; see Table C.2 in Appendix C. This is important because it allows us to rule out the possibility of treatment spillovers affecting the post-intervention outcomes in non-treatment forests, which would have resulted in a violation of the SUTVA assumption (Pearl, 2009).

Regarding the effectiveness of the intervention in reducing the number of forest fire occurrences, Figure 4 shows that the post-intervention differences between the treatment forests and their synthetic controls tend to be small in all months except for, possibly, November – and then only for the first three or four years. This is confirmed by the SCM _K_ treatment estimates _αt_ = _K_<sup><u>1</u></sup> � _i_ =1<sup>_αit_</sup> presented in Table 1. Columns (1) and (2) of � � this table present the estimated impact on fire occurrences with a minimum detection confidence of 30% and 50%, respectively. Note that the associated _p_ -values are presented in brackets, and that those with _p <_ 0 _._ 10 are printed in bold.

As already suggested by the graphical evidence presented in Figure 4, Table 1 documents that the FIP’s intervention only managed to significantly reduce forest fires in November, the post-harvest month. In the first year of the intervention, the number of days the average grid cell was on fire in that month decreased by 0.073 compared to the
.0|63.6|36.3|66.2|33.8|0.0|
|***United States 2001**|69.3|19.1|11.6|83.0|15.5|1.5|10.0|8.0|82.0|15.7|67.6|16.8|
|**_*Argentina 2001_**|_59.7_|_40.3_|_0.0_|_50.5_|_49.5_|_0.0_|_54.4_|_45.6_|_0.0_|_94.6_|_5.4_|_0.0_|
|**_*India 1999_**|_62.6_|_37.4_|_0.0_|_100.0_|_0.0_|_0.0_|_14.9_|_85.1_|_0.0_|_41.5_|_58.5_|_0.0_|
|**_China 1999_**|_45.0_|_55.0_|_0.0_|_24.4_|_75.6_|_0.0_|_0.0_|_100.0_|_0.0_|_55.7_|_44.3_|_0.0_|
|**_Indonesia 1999_**|_97.1_|_2.9_|_0.0_|_100.0_|_0.0_|_0.0_|_74.9_|_25.1_|_0.0_|_95.9_|_4.1_|_0.0_|

_Note:_ *indicates a federal country. Data for the emerging country group are shown in italics. _Source:_ As calculated from International Monetary Fund (2002) by Martinez-Vazquez, McLure, and Vaillancourt (2006). Countries are listed in order of tax-to-GDP ratio (as based on a variety of sources and in some instances for a later year than the detailed tax data included in this
Lichter, Peichl and Siegloch (2013).** We consider different elasticities for different skill levels, as well as short- and long-term elasticities.  This takes into account that employment is more sensitive to minimum wages among unskilled workers and to larger elasticities in the long run than in the short run because employers can fully adjust either in the intensive or extensive margin.  In our baseline specification, we use the elasticity values presented in Table 2.<sup>26</sup> The interpretation of the baseline elasticity value is the following: if wages of affected skilled workers increase on average by x percent, x*0.5 percent of affected skilled workers would lose their jobs in the short run and x*0.8 percent in the long run .

**To the best of our knowledge, prior research has not estimated labor demand elasticity for Kosovo, which in turn can vary substantially from the estimated elasticities for Eastern Europe.** To address this issue, we test the sensitivity of our results to the choice of the elasticity values in the next subsection.

The micro-simulation algorithm is as follows:

1. Estimate an expected wage increase (in percentage) for each group defined _by age, education, occupation, gender and skill level_ .

> 25 <mark>Some of the negative employment effects that result from wage increases (which are cost increases) may be due to more firms closing rather than firms laying off workers, especially in the long run. Analyzing change in the distribution of firms is difficult given the lack of firm-level data. Further firm-level analysis is required to take this effect into account.</mark>

> 26 The earliest studies of the employment effects of minimum wages used only national variation in the U.S. minimum wage. They found elasticities between -0.1 and -0.3 for teens aged 16-19, and between -0.1 and -0.2 for young adults aged 16-24. Other analyses that attempt to make valid geographic comparisons
In the first stage, we regress the take-up rate on the pre-primary access indicator defined at the community-cohort level. In the second stage, we estimate the model described above but specifying the fraction of children attending the pre-primary at the cohortcommunity level as the main independent regressor and instrumenting this variable with the pre-primary access dummy.<sup>9</sup>

# **5 Results**

## **5.1 Main impacts**

We first examine if and how the opening of pre-primaries in rural communities affects primary school progression by age 12. Table 2 presents the corresponding ITT estimates of the average effect of interest. The baseline specification described above is presented in column (1). We find no statistically significant effects associated with having access to a pre-primary on enrollment rates (Panel A). However, we do find statistically significant positive effects on adequate school progression (Panel B). The results indicate that opening a pre-primary in these rural communities increased by approximately 2.4 percentage points the proportion of children in grade 6 by age 12.

In columns (2) to (4), we test the robustness of these results using alternative specifications. Column (2) presents results when controlling for department-linear time trends and column (3) when controlling for differential municipality-linear time trends. We exploit the matched Census 1994 and 2002 data and control for time-varying controls in column (4). The baseline estimates are robust to these alternative specifications.

We proceed by examining the ToT average effects of attending a pre-primary on primary school progression in Table 3. Because the average take-up rate is about 60 percent for a given cohort at age 6, the estimated effects are roughly 1.67 times the ITT estimates of Table 2. The results indicate no significant effects on enrollment rates, but show a positive statistically significant effect on adequate progression of about 4.2 percentage points. As before, the results are robust to changes in the empirical specification used.

Expanding access to pre-primary education seems to improve adequate
that the share with access to mobile phones remained high following the start of the conflict (although some households reported sharing a mobile phone),18 the geographic coverage of the survey reaches the vast majority of the country,19 and the WFP survey itself demonstrates that the number of mobile phones owned by households has largely not changed at the national and governorate levels.20 Importantly, the shocks on which this analysis focuses all affect regions where the number of mobile phones has not changed since the last nationally-representative estimate (e.g., Abyan, Al Jawf, Shabwah, etc.).21 Another potential challenge to the generalizability of results is sample selection (i.e., nonrandom non-response). For example, in random digit dialing phone surveys in the United States of political preferences, the surveys are generally good at predicting party affiliation and many other political attributes relative to traditional household surveys but over-predict the amount of civic engagement due to differences in who is most likely to respond to a phone survey (e.g., Abraham et al. 2009).

To the degree that we are able to assess this issue in such a data- and evidence-scarce environment, we validate the WFP survey’s representativeness of the broader mobile phoneusing population by demonstrating that the survey is capturing trends that are independently corroborated by other sources. We demonstrate that the mobile phone survey does in fact detect large declines in many welfare outcomes and access to basic services that are consisFurthermore, this figure remained high for the rural population (81 percent), the population living below the poverty line (77 percent), and the population of all governorates (over 60 percent for each). Authors’ calculations using the 2014 Household Budget Survey.

> 18Registration for the  World Bank’ s  cash  transfers  program  being  implemented  by   UNICEF,  which covers approximately one-quarter of the total population and is aimed at  relatively  poorer  households, demonstrates  the  vast   majority  of  households  can  be  reached  via  phone   (see   (accessed September 2018): http:/
..|-0.750*|-0.559**|..|..|..|..|
|_(Oxford tracker)_|(0.370)|(0.247)|||||(0.383)|(0.259)|||||
|Stringency Index|..|..|-0.614*|-0.476*|-0.350**|-0.546**|..|..|-0.607*|-0.470*|-0.313**|-0.530**|
|<br>_(Oxford tracker)_|||(0.335)|(0.247)|(0.141)|(0.247)|||(0.342)|(0.254)|(0.129)|(0.250)|
|_Regional Dummies_|||||||||||||
|Dummy: Sub-Saharan|3.369|5.676**|2.161|4.609*|1.463|4.503|4.338|6.701**|3.285|5.547*|1.982|5.612*|
|<br> Africa (SSA)|(3.321)|(2.564)|(3.883)|(2.782)|(1.812)|(2.912)|(3.441)|(2.688)|(3.968)|(2.857)|(1.665)|(2.956)|
|Observations|85|85|75|85|75|85|85|85|75|85|75|85|
|
>20<br>6<br>15<br>4<br>10<br>2 5<br>Tax to market income ratio (%) 0<br>Transfer to market income ratio (%)<br>0<br>6 30<br>20<br>4<br>10<br>2<br>0<br>Tax to market income ratio (%)<br>Transfer to market income ratio (%)<br>0<br><!-- End of picture text -->

**_Source:_** _Authors’ calculations; Data from ESS 2018/19._ **_Note:_** _Shaded areas represent 95% confidence intervals._

16
|4,930|
|Tobago<br>Region|1,241,177|881,107||31.4|3.7||

Sources: Sugar production and exports are in tons of raw sugar from FAOSTAT. The sugar sector’s share of national employment is calculated relative to the labor force using various sources of sugar sector field and factory employment and labor force figures from the World Bank’s Development Indicators. The share of cropland planted to sugar cane is computed from FAOSTAT data. The share of merchandise exports from sugar is from FAOSTAT. The gross national income per capita is from the Development Indicators, 2002, Tables 1.1 and 1.6. Blanks indicate that data was not available.

1 The Caribbean region in this report refer to the countries included in the World Bank’s regional classification which includes the following countries: Antigua and Barbuda, The Bahamas, Barbados, Belize, Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Suriname, Trinidad and Tobago, Saint Vincent and the Grenadines.

1
countries (Russia and Chile, which lead in GDP per capita). They also represent 15 percent of the smoking prevalence in the world.<sup>5</sup> Almost three adult men in five ages above 15 living in these countries report that they smoke. One woman in 10 in these countries smokes. On average, health expenditures account for 6.7 percent of GDP in these countries, which collect only 17 percent of GDP as tax revenue.<sup>6</sup> A back-of-the-envelope estimation under the medium-bound elasticity scenario suggests that raising the price of cigarettes by 50 percent would allow around 350 million people (50 percent of the population in these eight countries) to capture positive household income gains on top of other potential social benefits from the greater public revenue. In some countries, the entire population could potentially see modest positive income effects. Higher elasticities in the longer term and more decisive price shocks would boost the number of beneficiaries. However, some heterogeneity in the results under the lower price shocks and the lower-bound elasticity scenarios and in some countries (especially Indonesia and South Africa) call for caution and careful analysis in implementing policy interventions.

> 5 2016 data of WDI (World Development Indicators) (database), World Bank, Washington, DC, http://data.worldbank.org/products/wdi.

> 6 2016 data of WDI (World Development Indicators) (database), World Bank, Washington, DC, http://data.worldbank.org/products/wdi.

Page 22
# **1. Introduction**

Approximately 85 percent of the global refugees are hosted in developing countries (UNHCR, 2020). Sub-Saharan Africa (SSA) hosts about one-third of them (Ruiz & Vargas-Silva, 2017). UNHCR (2019) asserts that the number of refugees in the region increased threefold between 2010 and 2019. This rise has been mainly attributed to persistent conflicts in the region (Verwimp & Maystadt, 2015; Kasozi, 2017 & Ivanova et al., 2018). Protracted conflict has also led to long refugee stays; an average of 9-21 years according to Hunter (2009). Hosting refugees could have far-reaching consequences in areas already struggling to ameliorate their own economic situation (Maystadt et al., 2019).

There has been a booming literature assessing the consequences of hosting refugees (Meyer et al., 2011; Ruiz & Vargas-Silva, 2017; Maystadt et al. 2019). Although the literature highlights that refugees can have a positive effect on economic development but with likely distributional consequences, the evidence from individual studies is mixed (Ruiz and Vargas-Silva 2017; Maystadt et al. 2019; Verme and Schuettler 2021). In their review, Verme and Schuettler (2021) argue that the direction of impact depends on which economic dimension is studied. For instance, they find that beneficial impacts are less likely if the outcome of interest is host employment or wages. By contrast, it is more likely if the outcome of interest is well-being measured in terms of income, consumption or wealth (Verme & Schuettler, 2021). Furthermore, they stress that few studies have employed panel data to study the impact of hosting refugees on the host communities.

Based on panel data collected between 2009 and 2012, we assess the impact of hosting refugees in Uganda on material welfare, measured by the consumption per adult equivalent. Our main outcome variable differs from studies such as Alix-Garcia and Saah (2009), Alix-Garcia et al. (2012), and Loschmann et al. (2019),
**TABLE 5. MARGINAL PRODUCTIVITY DIFFERENTIALS AND RELATIVE WAGES FOR TRAINING BY GENDER**

|_Variable_|_Producti_<br>_Differen_|_vity_<br>_tial_|_Relati_<br>_Wag_|_ve_<br>_Reject_<br>_e_<br>_i_<br>_i_<br>_o_<br>_H_<br>λ<br>φ =<br>:|
|---|---|---|---|---|
|_Ho: coefficient=1_|||||
|Men within non-trained workers|1.97<br>(0.15)|*****|1.54<br>(0.04)|*****<br>*****|
|In-house training within women|1.14<br>(0.17)||1.23<br>(0.08)|*****|
|External formal training within women|1.25<br>(0.29)||1.23<br>(0.13)|*****|
|In-house training within men|1.28<br>(0.07)|*****|1.14<br>(0.03)|*****<br>*****|
|External formal training within men|1.55<br>(0.10)|*****|1.24<br>(0.05)|*****<br>*****|
|Adjusted R<sup>2</sup>|0.90||0.96||
|Observations|6,662||||

_Notes:_ Calculations based on ENESTYC 2001. Standard errors are shown in parenthesis. *
time, column 2 adds year fixed effects; the point estimates slightly increase and standard errors decrease, suggesting that the result is not an artifact of time trends.

Does the significance of open defecation merely reflect general economic development? Column 3 adds a control for GDP per capita; the coefficient on sanitation remains similar,

> 3For example, in India’s NFHS-3, the survey asks “What kind of toilet facility do members of your household usually use,” with the relevant answer “No facility/uses open space or field.” This importantly distinguishes latrine use from latrine ownership.

8
realities.

# **2 Data and Summary Statistics**

This paper combines data from three sources: namely data on non-farm enterprises based on a nationally representative enterprise survey; agricultural statistics and rainfall data from the district-level ICRISAT database; and household consumption data from a nationally representative household survey. Additional district-level covariates are sourced from the Census of India and representative household employment surveys.

## **2.1 Agricultural Productivity Data**

We obtain data on district-level farm productivity and rainfall from the district-level database maintained by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). This database contains extensive crop-wise data on acreage and output for 14 crops at the district-level, in addition to data on fertilizer usage, cultivable land, irrigation and monthly precipitation. While the data is available for a 26 year period between 1990 and 2015, we use 4