rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table S1.6: Robustness - Using an ordinal probit regression model**

||DP<br>(1)|FDC<br>(2)|NRM<br>(3)|UPC<br>(4)|
|---|---|---|---|---|
|Enumerator gender (male)|-1.211***|-0.850***|-0.786***|-0.966***|
||(0.344)|(0.316)|(0.187)|(0.226)|
|Enumerator age|0.011|0.025|0.005|-0.043|
||(0.039)|(0.036)|(0.050)|(0.043)|
|Enumerator education|0.078|0.328|-0.916*|0.022|
||(0.297)|(0.385)|(0.503)|(0.317)|
|Enumerator from urban locality|0.985**|1.153***|-0.199|0.904***|
||(0.423)|(0.415)|(0.256)|(0.306)|
|Observations|6,245|6,333|6,553|6,320|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents regression results for equation (1) obtained using ordinal probit. For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic
better investment opportunities need to be available to farmers in Uganda, and further research on the interventions needed to ensure this is warranted.

This paper fits into the literature that uses household panel data to understand the drivers of income growth and welfare improvements (as in Deininger and Okidi 2003, Dercon 2004, Gunning et al. 2000, Jalan and Ravallion 1998, 2004). The focus of the analysis is on the exogenously determined factors. These are both the factors we can better identify the impact of, and, as it turns out, the factors that seem to explain much of the income changes observed. The findings are consistent with the results of Dercon (2004) and Gunning et al. (2000), in which weather shocks are found to be important determinants of household income growth.

# II. Data

The analysis in this paper uses data from 2,356 households across Uganda, present in all the waves of the nationally representative Uganda National Panel Survey (UNPS) from 2005/6 to 2011/12. This survey is one of the LSMS-ISA surveys and has data on household characteristics, household consumption and income from a variety of sources. It also contains a rich agricultural module, a module on shocks, and is accompanied by a community survey. Many of the modules in the survey are comparable to the modules used in the nationally representative Uganda National Household Survey (UNHS), which is a cross-section survey and is used to compute the official poverty numbers. The consumption module is identical across the two surveys.

Four rounds of the UNPS are used in this analysis comprising data collected in 2005/6, 2009/10, 2010/11 and 2011/12 (henceforth 2006, 2010, 2011 and 2012). Households can be matched across rounds using a unique household identifier, and, in addition, a sample of households that split from the original household during this period are also followed. The attrition in the UNPS was quite substantial between 2006 and 2010, but it has been moderate
the most responsibility for the family’s welfare in practice.</mark><sup>7</sup> <mark>Social norms can also affect whether female respondents self‐identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as</mark> _<mark>de jure</mark>_ <mark>female headed (single mothers, widows, divorcees, separated women) reported being male ‐headed. Other Eritrean female returnees who would be considered</mark> _<mark>de facto</mark>_ <mark>heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used</mark> by UNHCR.

<mark>We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care‐givers, single persons or living in non‐traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and</mark>

> 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures.

> 7 Even when traditional household survey data are gathered at the individual level, the information is often
component explains a decreasing proportion of variance. The scree plot shown in Figure 1 displays the proportion of variance explained by each principal component; on the x-axis are the components and the y-axis depicts the eigenvalues of each component.

**Figure 1: Scree-plot for results of principal component using household variable in**

**1999 Azerbaijan census household questionnaire.**

<!-- Start of picture text --> 0 5 10 15<br>Number<br>5<br>4<br>3<br>Eigenvalues 2<br>1<br>0<br><!-- End of picture text --> In calculating the household index, only the factor score (eigenvectors) of the first principal component were considered. Table 3 column (4) shows the results of the principal component on the correlation matrix of the 15 variable considered.

The asset score (A) for the jth household considering N variables is given by: where<sup>_f_</sup> 1<sup>is the eigenvector for the first asset as determined by the procedure,</sup> _a j_ 1 is the jth household’s value for the first asset and<sup>_a_</sup> 1<sup>and</sup><sup>_s_</sup> 1<sup>are the mean and standard deviation of the first</sup> asset variable over all households.

The mean value of the index is zero by construction. The standard deviation in this case is

1.90 since all asset variables (except “number of room”, and “total living area”) take only the

62
effects from the SC expansion but rather heterogeneous gains and losses–as we indeed find.

We exploit the local supply of higher education programs in Colombia as a source of exogenous variation for instruments. We use estimated propensity scores of enrollment choices to estimate the share of expansion and diversion compliers under a behavioral assumption (partial monotonicity, as in Mountjoy (2022) and Mogstad et al. (2021)) that allows us to isolate the effect of changes in the local supply of HEIs specialized in offering SC programs while holding the supply of other HEIs constant. To improve over standard two-stage least squares (TSLS) methods and identify the parameters of interest, we posit a multinomial model of enrollment choices, multiple IVs, and essential heterogeneity.<sup>2</sup> We use administrative information for the universe of Colombia’s high school graduates in 2005. We observe their high school exit exam ( _Saber 11_ ) scores, higher education enrollment choices, labor market outcomes between 2008 and 2013 (employment status and wages), and socioeconomic information. We link this data to municipality-level information on higher education supply, and focus on whether there is at least one HEI specialized in SC programs within the 10-kilometer radius of the student’s high school municipality. Hence, we compare the enrollment choices of high school students in municipalities with and without access to at least one specialized institution offering SC programs. Moreover, we examine whether the existence of such HEIs has different effects depending on the local supply of HEIs offering bachelor’s programs.

Our findings show that the presence of SC-specialized HEIs raises SC enrollment by 3 _._ 7 _−_ 4 _._ 5 percentage points (pp). This is a sizable increase, representing 40-50% of the sample enrollment rate in SC programs.<sup>3</sup> At least 70% of this increase is due to students who would have otherwise attended a bachelor’s program (the diversion margin), while the remaining students react along
# **Table 1: Number of neonatal and infant death analyzed from different Bangladesh** **<u>Demographic Health Survey</u>**

||||**DH**|**S year**|||
|---|---|---|---|---|---|---|
||**2000**|**2004**|**2007**|**2011**|**2014**|**Combined**|
|Total number of live birth 5 years prior to interview year|31925|33475|30527|45844|43695|185466|
|Total number of deaths 5 years prior to interview year|||||||
|Neonatal (died within 2 months after birth)|2549|2421|2005|2688|2390|12053|
|Infant (died within 1 year after birth)|3529|3288|2664|3481|3053|16015|
|Number of live birth 3 years prior to interview year|4114|4233|3751|5191|4899|22188|
|Number of death 3 years prior to interview year|||||||
|Neonatal|215|211|168|213|188|995|
|Infant|253|267|203|247|213|1183|
|Number of child deaths 3 years prior to interview year<br>from the households with tuebwell as the primary source<br>of drinking water|||||||
|Neonatal|190|170|153|190|159|862|
|Infant|224|214|182|222|180|1022|

21
use two outcome variables: decline in farm income and decline in non-farm income.<sup>22</sup> First, _Yhpc_ takes the value 1 if the household ( _h)_ in province p within country _c_ reported a decline in farm income since the pandemic started and 0 if it did not. Second, _Yhpc_ takes the value 1 if the household ( _h)_ in province _p_ within country _c_ reported a decline in non-farm income since the pandemic started and 0 if it did not. The vectors _Ghpc_ , _Xhpc,_ and _Ipc_ are defined as in Equation (1).

We estimate this model in two samples. First, we use the household business definition of urban household enterprise to estimate how farm and non-farm household enterprise income changes by poverty status of the household. We restrict the sample to households owning a farm business when modeling declines in farm income, and similarly to households owning a nonfarm business when modeling declines in nonfarm income. This model does not include the X vector control variables. We then use the household head definition of urban household enterprise and include the X vector to explore how farm and non-farm income changes among the self-employed who

> 19 <mark>We use the survey-to-survey imputation method. Using household surveys with individual level data, we run a model for each country where the welfare measure is the dependent variable (per capita consumption) controlling for demographic characteristics. Then we use the coefficients of the regression to predict the welfare level in the HFPS. The variables used to impute the poverty proxy are not included in the control variables in the regression.</mark>

> 20 The survey asks for location at the 1st level administrative unit. We would have preferred to control for fixed effects at the city level to allow for differences between capital and secondary cities, but the data do not include a city variable.

> 21 <mark>The exercise only observes change in enterprise earnings so we cannot rule out that factors other than the pandemic affected enterprise income. However
# **Woreda estimates of undernutrition**

Before presenting the SAE results, Figure 3 shows stunting and underweight at the regional level based on EMDHS. Figure 3 shows that the urban-rural divide is an important indicator of undernutrition levels. Child stunting is 16 percentage points higher in urban (27.0%) than rural (42.6%) areas. The urban divide is also reflected in regional variation with urban-dominated regions as Addis Ababa, Dire Dawa, Harari, and Gambella (regions sorted by share of population living in urban areas) having significantly lower rates of stunting, compared to other regions. For underweight, regional differences are smaller, though the south and eastern regions Afar and Somali have significantly higher underweight rates, while capital Addis Ababa has significantly lower rates (Figure 3). Though stunting and underweight levels vary across regions, many are not significantly different from each other, as observed by the 95 percent confidence intervals (Figure 3).

**Fig 3. Share of children stunted and underweight with 95 percent confidence intervals by region.**

<!-- Start of picture text --> Upper 95/Lower 95 Mean stunting rate Upper 95/Lower 95 Mean underweight rate<br>GambelaAddis AbabaDire DawaHarariSomaliBenishangul GumuzOromiya AmharaSNNPTigray AfarUrban avgRural avg Addis AbabaHarariGambelaDire DawaOromiyaBenishangul GumuzSNNP AmharaTigraySomali AfarUrban avgRural avg<br>.6 .6<br>d 95 CI<br>.5 .5<br>.4 an an .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 erweight mend 0<br>U<br><!-- End of picture text --> A first observation, based on the SAE-obtained woreda level estimates, is that only a few regions are cohesive with similar levels of undernutrition across woredas (Figure 4). Most regions are not cohesive and have large variations in levels of underweight and stunting across woredas. This is illustrated in Figure 4 by the wide
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Air transport

## **Achievements**

The Benin air transport market is characterized by significant airline competition. The presence of three local airline companies and several large foreign operators in this medium-sized market seems sufficient to ensure a certain level of competition, even though the carriers share routes rather than compete directly with each other (World Bank 2007b). The relatively high level of competition may be attributable to the regional implementation of the Yamoussoukro decision, which liberalizes international regional air traffic.

Competition in the market has enabled Beninese air transport traffic and connectivity to recover since the global decline in 2004. Total traffic (excluding domestic) surpassed the baseline of 572,433 in 2001, reaching 603,436 in 2009 despite the global recession. Figures show a strong increase from 2007 to 2009, with solid growth in traffic between other countries in Sub-Saharan Africa, in traffic between North Africa and Benin, and in intercontinental traffic (figure 10a). Five of the airlines serving Benin established their routes between 2004 and 2009, including Kenya Airways, increasing the number of international city pairs from 16 to 18. New airlines took over the capacity left by Air Afrique after its collapse in 2001. The total number of city pairs as of 2009 was 19, recovering to precrisis levels (figure 10b).

**Figure 10. Evolution of seats and city pairs in Benin**

<!-- Start of picture text --> a. Seats b. City pairs<br>700,000 25<br>600,000<br>20<br>500,000<br>15<br>400,000<br>300,000<br>10<br>200,000<br>5<br>100,000<br>- 0<br>2,001 2,004 2,007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total International Intercontinental Domestic<br>
able to fall back on (formal or informal) support systems to cope with it ex post, leading it to consider the event less of a shock or disaster, as it is not bearing its full costs. Such considerations, which remain largely unknown to the researcher, may lead to an underestimation of the full welfare cost of the event. Besides methodological concerns, it is typically also hard to extrapolate the findings across space and time, as subjective shock measures are usually not available outside the sample. Neither are their probability distributions, which are necessary to predict in the future.<sup>3</sup> When objective measures of natural disasters have been used instead, for example directly derived from the weather data, these measures have often been constructed only at rather aggregated levels (Christiaensen and Subbarao, 2005).

To overcome these methodological and practical concerns, this study derives measures of natural disasters and hazards at disaggregated geographical levels from primary meteorological weather station data, storm tracks and satellite observations. Second, in understanding the economic impact from disasters both direct and indirect effects need to be considered. Their most direct (and visible) impact relates to the destruction of capital (for example productive assets, property and infrastructure in case of floods and hurricanes; livestock and crop loss, but also hunger and reduced work capacity in case of droughts and famines) (Loyaza, et al., 2009). But they also have important indirect effects (Okuyama, 2009), as the effects of the initial destruction of private and public capital works its way through the economy via the product and factor markets. Ruptures in infrastructure or the electricity supply for example reduce the flow of goods and services and 2 These are typically derived from answers to a shock module incorporated in household questionnaires asking respondents whether they experienced different types of shocks over a certain period (e.g. a drought, a flood, a storm, ...).

3 The latter could in theory be obtained (Delavande, Gine
human resources, with around 15 percent lower resources than the administrations of countries in a similar category. This explains why the level of revenues collected by each customs officer is higher than the median.

**Table 1: International Comparison with Madagascar Customs**

||**Customs revenue/per**<br>**capita (US$)**|**Customs revenue/per**<br>**customs agent (US$)**|**Number of**<br>**employees**|
|---|---|---|---|
|**Madagascar**|22.7|416,600|1,182|
|**Median for Africa**|28.5|282,900|1,393|

_Note:_ Data for 31 African countries provided by World Development Indicators. Data on number of agents provided by the World Customs Organization, 2012 figures.

**Figure 1: Structure of Tax Revenues Figure 2: Structure of Customs Revenues by Major Type of Taxes**

> 8 The structure of Madagascar’s economy leads to a sizeable balance of trade deficit. In 2014, the trade deficit was 10.5 percent of GDP. Unless otherwise indicated, all statistics in the rest of this document pertain to the year 2014.

4
about 15% of the country’s population. As can be seen from Figure 1, there is a large disparity in temporal changes in the number of refugees hosted even within these major refugee-hosting regions. Considering the refugee-to-population ratio in Figure 2, the highest increase is recorded in the Gambella region, reaching roughly 60% in 2014 from about 6% in 2009, followed, respectively, by the Benishangul Gumuz and Somali regions.<sup>9</sup> Despite achieving one of the fastest rates of economic growth globally in the past decade, Ethiopia remains as one of the poorest countries, with average annual income per capita of just US$772 as of 2018. Poverty is pervasive, as 27 million people, close to one-third of the population, live on less than US$1.90 a day as of 2015 (World Bank, 2018). As a result, improving welfare and reducing the number of people living in poverty are among the top policy priorities in the country.

Given the increasing instability in the region, which remained as the major cause of forced displacement, and the open-door policy toward refugees and the pervasive poverty in the country, understanding the well-being implications of hosting refugees in Ethiopia is important. The evidence could support policy making that aims at easing refugee integration, a relevant precondition for them to rebuild their lives, without overburdening the host economies as envisioned in the Global Compact on Refugees (GCR).

# **3. Data Sources**

I use data from two sources: (1) the Ethiopian Socioeconomic Survey (ESS) data from the Central Statistical Agency of Ethiopia (CSA) and the World Bank, and (2) data on refugees in Ethiopia from the United Nations High Commissioner for Refugees (UNHCR).

## **3.1. ESS Data**

The individual- and household-level information comes from the ESS data. The ESS is a three round panel survey, at the individual and household levels. The first round (ESS1
.4|1.4|3.3|12.1|
|Number of observations|970|244|241|243|242|

<u>1 Occupied parcels are evaluated using the median prices corresponding to the respective communities. Rented-in (both under fixed</u> and sharecropping contracts) and borrowed-in parcels are excluded while computing the value of land occupied by the household. _Source:_ Own computation from 2004 WB/Makerere/FASID land tenure survey.

23
Table 4: Early Life Exposure to Coffee Mills and Test Scores: Effects by Gender

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Male|||||
|CatchmentArea X Post|0.0951***|0.1022***|0.0932***|0.0784***|0.0739***|0.0645**|0.0998***|0.1144***|0.1059***|
||(0.0317)|(0.0280)|(0.0272)|(0.0303)|(0.0271)|(0.0267)|(0.0317)|(0.0282)|(0.0274)|
||[ 0.004 ]|[ 0.001 ]|[ 0.001 ]|[ 0.012 ]|[ 0.009 ]|[ 0.009 ]|[ 0.003 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3793|0.4222|0.4222|0.3699|0.4099|0.4100|0.3574|0.4026|0.4027|
|Observations|668639|668285|668285|668639|668285|668285|668638|668284|668284|
||||||Female|||||
|CatchmentArea X Post|0.0837**
ΔlnMikUS = αi + σk + β[I(ΔChina share2017−2022 > median) × (characteristici)] + εik (5) The dependent variable is US import growth from country _i_ in product _k_ , characteristic is an exporter country characteristic, such as population, income, or distance from the US. The purpose is to explore the characteristics of the countries that displaced China in the products subject to tariffs in which China’s share declined markedly. Country- and product-fixed effects control for average exporter growth and average product growth in order to isolate which countries benefit from drops in China’s shares relative to other products and to other exporters.

In addition to income, size, and distance, we also include independent variables to reflect economic and political linkages with China or the US. Export similarity with China would promote exports if investors were eschewing China as an export hub and replacing it with otherwise similar economies. Revealed comparative advantage in a product would be important if the most competitive alternative suppliers are gaining market share. UN voting aligned with the United States would be important if US importers want to focus on friendly trade partners. Finally, the intra-industry trade index (Grubel and Lloyd, 1975) is included to reflect whether supply chain integration with China matters. We also tried including a host of other variables, including distance from China, regional trade agreement with the US, regional trade agreement with China, and historical estimates of value-added trade with China, but they were never significant.

While trade in value added with China would be a better measure of supply chain linkages, existing data only provide broad estimates of trade in value added, relying on assumptions about input-output matrices, cover only 22 goods industries that are defined differently from trade statistics, and for a limited sample of countries. The advantage of the Grubel-Lloyd index (GLI) is that it can be precisely measured using bilateral trade data. In the specification below, GLI is calculated at the 4-digit level (1,237 x sectors). A