rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Section1. Introduction**

There is a growing amount of work investigating the causes of forced displacement. In addition to the onset of conflict being associated with many forced displacement crises (e.g., UNHCR 2016), there is a large amount of empirical evidence demonstrating that displacement is strongly associated with households living in close proximity to violence (e.g., Stanley 1987; Engel and Ibanez 2007; Williams 2008; Lozano-Garcia et al. 2010; Bohra-Mishra and Massey 2011; Adhikari 2013; etc.). However, displacement decisions are complex, and both the decisions of whether and when to become displaced are affected by the type, frequency, and timing of violence. Better investigating potential causes and possible heterogeneity can help to better contextualize the escalating forced displacement crisis across the world (e.g., World Bank 2017a).

This paper investigates the relationship between violence and forced displacement during the Republic of Yemen’s current conflict. Importantly, this is a setting where the onset of conflict led to an immediate and widespread forced displacement crisis, where displacement might have been forced by either violence or the humanitarian situation caused by the conflict (e.g., TFPM 2016; OCHA 2018; etc.). Additionally, one of the reasons there has been limited investigation into the events forcing displacement is the difficulty in collecting information from displaced households before, during, and immediately following displacement. However, the World Food Programme (WFP) has been implementing a monthly monitoring survey in the Republic of Yemen that includes both cross-sectional and panel components, and the survey reached a large number of households before they later became displaced. The survey is able to track the food security and displacement status of these households in the months leading up to and following displacement, which allows us to identify the violence occurring in the district from which households were forcibly displaced (origin district) at the time of displacement.<sup>1</sup><sup>_,_2<
Strobl, E., and Sun, P. (2015). The local impact of typhoons on economic activity in China: A view from outer space. _Journal of Urban Economics_ , 88:50–66.

- Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., and Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. _International Journal of Remote Sensing_ , 18(6):1373–1379.

- Emanuel, K. (2011). Global warming effects on us hurricane damage. _Weather, Climate, and Society_ , 3:261–268.

- Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., and Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. _Global Ecology and Biogeography_ , 20(1):154–159.

- Holland, G. J. (1980). An analytic model of the wind and pressure profiles in hurricanes. _Monthly Weather Review_ , 108(8):1212–1218.

15
# **IV. Data** Description **and** Sample Profiles

# **Samplo** Distribution by **Gender,** Cohort. **and Residenc.15/**

The sample for the analysis consists of 5,644 females and 5,241 males aged 20-59 drawn from the Peruvian Living Stanlards Survey (PLSS). The PLSS was fielded from June 1985 through July 1986 by Peru's Instituto Nacional de Estadistica (INE). The survey questionnaire was developed by the World Bank in collaboration with IKE and the Central Bank of Peru. For details about the PLSS sample and survey design, see Grootaert and Arriagada (1986). A sample of adults was chosen to allow us to observe completed schooling levels. Yet, since 232 of those aged 20-24 were still enrolled in school when the data were collected, we do not fully capture the final education levels of more highly educated persons.

A breakdown of the sample by current place of residence and by birth year is displayed in Table 1. The individuals in the sample are, on average, nearly equally distributed across Lima, other urban and rural areas, with a slightly larger proportion residing in rural areas. The spatial distribution of the sample differs, however, by cohort. Over 40 percent of the oldest cohorts live in rural areas, compared to about one-third of the youngest cohorts.

15/ We ,o not estimate the models separately by current place of residence, i.e. Lima, other urban, and rural areas, because current place of residence could differ from placs of residence at the time of schooling due to migration. Rather, place of residence is included as an explanatory variable in the regressions.

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MODIS platform over the period October to March (Jain et al., 2017a,b). We aggregate the underlying 1-km pixel-level data to district level to determine the area of each district planted in winter crops in each year in the sample.

## **3.3 Meteorological data**

We collect weather re-analysis data from ERA5. ERA5 is a weather re-analysis product produced by the European Commission’s Copernicus Climate Change Service.<sup>7</sup> We use data from the ERA5 Land hourly product. This provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution. We use data on temperature and precipitation over the full sample from 2003 to 2014. We aggregate these weather variables to the district level by averaging over all grid points that fall within a district boundary.

> 6Data accessed from: http://cgwb.gov.in/GW-data-access.html

> 7Data available here: https://cds.climate.copernicus.eu/cdsapp!/dataset/reanalysis-era5-land?tab=overview

6
, average preschool enrollment rates among children aged 3- 6 per district between 2003 and 2014 in Indonesia is 15.6 percent; the 99th percentile is 49.8 percent.<sup>22</sup> As such, we may not expect preschools in Indonesia to have reached the saturation point yet.

Table 13 provides suggestive evidence on the heterogeneous effects of preschool availability on maternal employment by preschool enrollment rates using DDD and DDD-FE strategies. The sample is restricted to PODES years with preschool enrollment data,<sup>23</sup> which leads to a total of 37,547 individual-year observations (less than half of the sample in Table 2). Each strategy divides the sample into three groups: those subjected to less than 20 percent (N = 23,076),

> 22 Data obtained from National Socioeconomic Surveys (SUSENAS) in 2003, 2005, 2011, and 2014.

> 23 Preschool enrollment data coincide with PODES years in 2003, 2005, 2011, and 2014.

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- For the government and infrastructure sectors, the FAI data for urban areas have been used excluding the category “other” investment.

_Estimates for the period 1981 to 2002_ For the period prior to 2003, two different methodologies have been adopted. Between 1981 and 2002, sectoral FAI data have been used as a proxy for gross fixed capital formation without any adjustment, including for the industrial sector. Between 1952 and 1980, data for new capital construction have been used. The growth rate for this series has been used to generate a hypothetical FAI series prior to 1981, as the level of the capital construction series and that for FAI in each sector were different in 1981.

One problem with the data prior to 2003 is that the sectoral definitions used in the published data do not correspond exactly with the definitions used after 2002. Data for FAI in telecommunications are not available prior to 2003. They are included in the transport and telecommunications sector. However, the definition of infrastructure used here excludes telecom investment. A series for FAI in transport has been created by assuming that the share of telecoms in FAI is the same as in capital construction.

In the published official data for the period prior to 1981, data for the capital construction in the public administration sector (government, parties and social organizations) are amalgamated with the sector “other”. According to Chao (1974), the sector “other” refers to all of the “non-productive” investment by state-owned enterprises. Such outlays cover residential and dormitory accommodation, schools, health clinics and hospitals provided by state-owned enterprises. For the period 1959 to 1980, it has been assumed that the capital construction in the public administration sector was half the aggregate amount, based on the share in the first year for which separate data were available. For the 1950s, 30
# Forecasting Migrant Remittances during the Global Financial Crisis

## 1. Introduction

The global financial crisis which started in August 2008 with the collapse of Lehman Brothers led to concerns among policymakers that it would result in precipitous declines in external resource flows to developing countries, which could adversely impact the sustained economic growth and reduction in poverty during the previous decade (World Bank 2009).<sup>1</sup> Given the size and increasing importance of migrant remittances for developing countries, which had reached more than $325 billion in 2008, there were similar concerns that a decline in remittances would affect the poorest countries and households that were heavily dependent on remittances. Policymakers needed forwardlooking analysis of the impact of the global economic crisis on remittance flows to developing countries.

Migrant remittances have usually been counter-cyclical with respect to downturns and crises in origin countries (see discussion below). However, unlike past emerging market crises that had started in emerging markets—Mexico in 1994-95, East Asia in 1997-98—the current crisis started in the rich countries and spread to developing countries (Ratha, Mohapatra and Xu 2008). Although a number of studies have documented the importance of both host and home country factors in determining remittance flows (see section 3), it was not clear _a priori_ how remittances would behave in response to a deep economic downturn in the host countries. To our knowledge, there had been no previous models for forecasting remittances, a necessary tool for analyzing the impact of the global financial crisis on remittance flows.

This note describes a first attempt to develop a methodology to forecast remittances. It exploits an existing bilateral remittance matrix for over 200 countries developed by Ratha and Shaw (2007) to generate country level forecasts. The framework

> 1 The World Bank’s Global Development Finance 2009 report estimated that net private capital inflows to developing countries fell to $707 billion in 2008 (from a peak of $1.2 trillion in 2007) and were expected to fall by further by 50 percent by end of
Policy Research Working Paper 10589

# **Abstract**

The mobility restrictions and health measures imposed during the COVID-19 pandemic have had highly adverse impacts on small-scale cross-border trade. One coping strategy that traders have pursued is to engage in group trade, that is to combine their loads and cross the border using a larger cart or vehicle. This paper uses a cross-sectional data set derived from a survey of traders at the borders between the Democratic Republic of Congo and Burundi and Rwanda to assess the determinants of participation in group trade. The findings from the econometric analysis point to association membership, business registration, and motorized transport as being important factors for traders’ participation in new cooperative trade arrangements. Moreover, successful group traders have been in a position to increase their incomes by reaching new clients and obtaining higher prices. These results suggest that policy efforts to promote group trade could usefully focus on enhancing the integration of small-scale traders into regional supply chains. However, group trade has mainly benefitted the better-off segments of the trader population, so that any assistance projects to enhance group trade risk further increasing the income gap in border communities.

This paper is a product of the Macroeconomics, Trade and Investment 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 jkeyser@worldbank.org, ckunaka@worldbank.org, and pwalkenhorst@aup.edu.

_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, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They
2. Following a policy or exogenous shock, generate the growth rate in per capita consumption of the representative household in the macro model, up to the end of the simulation horizon (say, period _t+N_ ).

3. Apply this growth rate to the consumption expenditure data for each household in the survey. This gives new consumption levels for each household, for periods _t_ +1,... _t_ + _N_ .

4. Update the poverty line in the survey for periods _t_ +1,... _t_ + _N_ by using the growth rate of the composite price index generated by the macro model. This assumes implicitly that the poverty line is constant in real terms.

5. Using the new data on nominal consumption per household and the poverty line, calculate “post-shock” poverty indicators. Compare with initial indicators to assess the poverty effect of the shock.

In this approach, and given the assumption of only one representative household in the model, changes in inequality cannot be accounted for endogenously. Moreover, distribution among the households contained in the survey is assumed not to change following any shock. Growth (in consumption) is thus implicitly assumed to be distribution neutral.

Ignoring the inequality component of changes in poverty can be justified if the available data on changes in inequality are not deemed reliable (which is not quite the same as saying that they don’t matter), if somehow past evidence suggests that income distribution does not change much following certain types of policy shocks, or if the emphasis is on growth as being both necessary and sufficient to reduce poverty in low-income countries (see Agénor (2004 _d_ )). The caveat, of course, is that to the impact of foreign debt on private investment could also be assumed to be nonlinear, to reflect greater perceptions of confiscation risk beyond a certain level of debt.

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---|---|---|
||**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|
|**1**|2.0|3.7|2.8|7.5|6.9|7.1|
|**2**|2.9|3.7|3.5|5.9|5.6|5.6|
|**3**|2.9|3.1|3.1|5.1|4.7|4.8|
|**4**|2.6|2.7|2.7|4.6|4.1|4.1|
|**5**|2.4|1.9|1.9|3.9|3.1|3.2|
|**All**|2.5|2.8|2.7|5.6|4.4|4.6|

_Source:_ Authors’ calculations.

# **Peru (ENAHO 2009)**

The data are taken from the 2009 National Household Survey on Living Conditions and Poverty (ENAHO) administered by the National Institute of Statistics and Information (INEI) in Peru. The fieldwork for the survey was carried out between January 2009 and December 2009.

The value of all non-purchased items was about 33 percent of total purchased expenditure, while non-purchased food accounted for about 53 percent of all non-purchased items. These ratios tended to decline at the upper quintile levels. All food expenditures were deflated using adjustment factors provided in the survey data set.

The survey asked households to report the primary energy source for cooking. LPG was the primary cooking fuel for more than half of the households surveyed, followed
The results of the SAE estimates are presented graphically in the following section. Separate modeling exercises are conducted for each welfare indicator. The model variables that are part of the _X_ vector in the estimation procedure were chosen to maximize the ratio of explained variance to the total variance, as captured by the adjusted R<sup>2</sup> .<sup>4</sup> There is no pre-set group of variables that are guaranteed to achieve that objective. Instead, automated variable selection using the stepwise approach was used. Sensitivity analysis comparing the results to alternative model development approaches (including several variations of the lasso approach) are described in annex I.

# **IV – Results**

Summarizing poverty rates by region (at which the underlying surveys are themselves considered representative) yields a range of between 0 and 27.7 percent at the poverty line for lower middleincome countries ($3.2 PPP per person, per day), and between 0 and 76 percent for the line appropriate for upper middle-income countries ($5.5 PPP per person, per day). But this level of aggregation hides significant variation within regions. When disaggregated to the district level, the resulting poverty estimates for the LMIC line range from 0 percent to 70 percent, and many individual districts register nearly 100 percent poverty rates at the UMIC line, despite a lower average poverty rate in the surrounding region.<sup>5</sup> Poverty rates at the LMIC line are relatively low throughout Kazakhstan and in much of Uzbekistan. However, pockets of high poverty rates remain in the Kyrgyz Republic and in Tajikistan. The parts of the Ferghana Valley within Uzbekistan’s borders have relatively low rates of poverty but nearby districts in the Kyrgyz Republic and Tajikistan with somewhat higher rates of poverty.

Even stronger regional disparities are apparent when using the UMIC line. Poverty by this definition was still less than 10 percent in Kazakhstan in 2017, but this stands in contrast to much higher rates in the Kyrgyz Republic
Policy Research Working Paper 9602

# **Abstract**

This paper estimates trade barriers in government procurement, a market that accounts for 12 percent of world GDP. - Using data from inter-country input-output tables in a grav ity model, the paper finds that home bias in government procurement is significantly higher than in trade between firms. However, this difference has been shrinking over time.

Results also show that trade agreements with provisions on government procurement increase cross-border flows of services, whereas the effect on goods is small and not different from that in private markets. Provisions containing transparency and procedural requirements drive the liberalizing effect of trade agreements.

This paper is a product of the Development Research Group, Development Economics, the Macroeconomics, Trade and Investment Global Practice, and the Governance 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 amulabdic@worldbank.org and lorenzo.ROTUNNO@univ-amu.fr.

_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, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
, such as state funding for capital expansion programs as in Ethiopia. It is important for such expenditures to be deliberate and transparent. Where this is not the case, “hidden” costs and thus the “hidden” (but quite real) contingent fiscal liabilities can become a problem because these are not sufficiently openly and explicitly presented for policy debates and decisions. In addition to the large fiscal burden that QFDs may impose, another consequence of large QFDs is an undercapitalized sector in a state of disrepair. Frequent blackouts that result force businesses to rely on expensive diesel generators and factories to shut down, with serious negative macroeconomic consequences. South Africa is a good illustration, with rolling blackouts taking a toll on the economy and harming economic growth. The adverse macroeconomic effects of power outages are discussed in Bacon and Kojima (2016, chapter 4).

The data set built through this study provides a view on long-term trends through comparison with AICD results. Substantial improvements are apparent in several countries. QFDs in Tanzania fell sharply in 2014 following tariff increases, helped further by a steady reduction in T&D losses. The QFD in Nigeria was very large in the AICD and remains large in the present study but has fallen following privatization and continuing power sector reforms. On the other hand, some countries appear to have slipped in the financial performance of the sector. Botswana is one example where the QFD was almost zero in the AICD, but has increased due to delays in the commissioning of new power plants, forcing reliance on emergency power rentals.

The results in this paper are sensitive to oil prices and hydrology risk. One concern is whether the reference year could have been an outlier—for example whether the reference year was a year of abundant rainfall or severe drought in a country that depends significantly on hydropower. For the 23 countries with three or more years of data available, the multiyear analysis suggests that the reference years selected for each country do not appear to be outliers.

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|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|TIMMS Math Score<br>(Standardized)|I|Core<br>Respondent|N/A|3|Total score from a professionally developed<br>test of mathematics based on the Malawian<br>school curricula for the grades the target<br>population was attending. Five questions (four<br>from the Fourth Grade test and one from the<br>eighth Grade test) from Trends in Mathematics<br>and Science Study (TIMMS) 2007, which is a|
|Non-TIMMS Math Score<br>(Standardized)|I|Core<br>Respondent|N/A|3|cycle of internationally comparative<br>assessments in mathematics and science<br>carried out at the fourth and eighth grades<br>every 4 years, were added to the Math test.|
|Cognitive Test Score<br>(Standardized)|I|Core<br>Respondent|N/A|3|The cognitive test score is based on Raven’s<br>Colored Progressive Matrices.<br>Competencies represent a set of skills that<br>were anticipated to be sensitive to education|
|Competencies Score<br>(Standardized)|I|Core<br>Respondent|Yes|4|and relevant for non-formal employment. The<br>overall competencies score is a standardized<br>index of the five competencies listed<br>immediately below.|
|Fertilizer Application<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to follow<br>instructions for applying fertilizer to maize.<br>|
|Change Given<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized