rafmacalaba/data-use-mentions-extended huggingface dataset:
crop products (e.g., wheat, potatoes) and the value of five livestock products (i.e., milk, egg, butter, hides, and honey) sold in the market.<sup>14</sup> Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX)<sup>15</sup> and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM).<sup>16</sup> We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED)<sup>17</sup> and the population data from the Gridded Population of the World (GPW) data set.<sup>18</sup> On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics.

# **5. Empirical strategy**

To estimate the impact of refugee inflow<sup>19</sup> on host community’s livelihood strategy choice, we use the following basic econometric model:<sup>_(1)_</sup> Where, indexes a household, ii ii<sup>+ γγXX</sup
Policy Research Working Paper 10526

# **Abstract**

This paper presents global evidence on the impact of expansion in mobile telephony and broadband Internet services on economic development at the subnational level. Leveraging two decades of satellite data on nightlights and the global expansion of 2G, 3G, and 4G mobile networks in over 34,000 subnational districts in 120 countries, it documents three main findings on the effects of mobile phones on local economic development (proxied by nightlights): I. The expansion of mobile coverage has a positive effect on economic activity. Using the GDP—nightlights elasticity from Henderson et al. (2012), the estimates suggest a GDP growth—mobile phone penetration elasticity of 0.018–0.023; II. While mobile broadband (3G & 4G) Internet connectivity is associated with economic development across all countries, 2G connectivity boosts local economic growth mainly in developing countries; III. The economic effects of expansion in mobile network connectivity are more pronounced in countries that hitherto had limited access to fixed-line telephone infrastructure, thus highlighting the importance of mobile phones to developing countries in leapfrogging the technology ladder.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 author may be contacted at jmensah2@worldbank.org.

_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
rates. Our results do not provide evidence for either hypothesis. In column 5 we control for the level of education using data on enrollment into tertiary schools from the World Development Indicators. Again, the results are not altered and higher university enrolment is not associated with faster institution building. We also control for the EU accession process, since the prospect of future EU membership might have fastened institution building, both through political incentives and through assistance from the original EU member states. Surprisingly, the dummy variable for the countries that entered the EU in 2004 does not enter significantly, contrary to theoretical predictions by Roland and Verdier (2003) (column 6). Countries suffering from civil war during the 1990s were not more or less likely to experience institution building (column 7). Repressed inflation during the pre-transition era also does not alter the results (column 8). We control for price distortions by using the increase in deflated wages minus the change in real GDP over the period 1987 to 1990 (De Melo et al. 1996). However, initial price distortions are not significantly associated with institution building and controlling for them does not affect our results. The initial state of price and trade liberalization and importance of the private sector does not seem to be associated with institution building, and neither does the speed with which countries liberalized prices, open the trade regime and privatized (columns 9 and 10). To assess the sensitivity of our results to controlling for macroeconomic reforms we use indicators developed by de Melo et al. (2001) for price, trade and ownership liberalization, with annual variables for the period 1990-97. We use the values for 1990 to proxy for the initial condition of price, trade and ownership liberalization in transition economies and calculate a principal component indicator of these three indices. In column 10, we control for a principal component indicator of changes in price, trade and ownership liberalization, thus controlling for the speed of

> 20 This is also confirmed with a simple t-test between Institutional Development in FSU
. The majority of formal sector employees who do separate from their jobs are eligible for severance. Few of these employees, however, collect their full entitlement to severance pay.

Actual practice departs substantially from the statutory regulations; the costs of severance pay are much lower than those implied by the severance statutes. As a result,

> 5 Similarly, voluntary separations form the majority of responses in the IFLS (2007) survey. Involuntary job separations accounted for 21.8 percent of all separations and these respondents cited two main reasons: company closed/relocated/restructured; fired for other reason.
explored associations between diets and undernutrition, with less attention given to the full range of malnutrition outcomes. While the nutrition transition is often more advanced in middle-income countries, the pace of change and the impact in low-income countries cannot be overlooked (Popkin, Corvalan, and Grummer-Strawn 2020). Food consumption patterns vary widely across geographic regions, with big differences between regions in terms of insufficient intakes of healthy foods and excess intakes of unhealthy foods (Miller et al. 2022). Spatial variation in dietary patterns (Zhao et al. 2022) and dietary diversity (Alemu et al. 2022) also occur within the same country. Therefore, it is important for all countries to have evidence based on their local context.

Our scoping review also revealed the polarization in evidence in LMICs. One group of studies focused on the foods that matter for achieving nutrient adequate diets in environments where there are still widespread nutrient inadequacies and undernutrition, especially among nutritionally vulnerable individuals like young children and pregnant women. Another group of studies explored the foods that matter for preventing the rapid rise of overweight/obesity and NCDs with a focus on adult population. For the latter group, although all the SRM reviewed included at least one LMIC, the evidence was heavily weighted toward research conducted in highincome countries. The results of our review show the gaps in evidence for foods and dietary measures that can monitor multiple subconstructs of healthy diets in low-income country contexts, including nutrient adequacy, diversity, balance and moderation (Verger et al. 2023). This is an area of ongoing work to develop, validate and use more widely healthy diet metrics that concurrently capture these two sides of the DBM and are feasible to operationalize in LMICs taking a full life cycle approach.

By relying on meta-analyses for effect size estimates, our review also did not capture emerging approaches to assessment of unhealthy diets and associated outcomes that may be well-suited to monitor the rapid nutrition transition faced by several countries. For example, two relatively new metrics, the Global Dietary Recommendations Score (Herforth et
World. Estimation was conducted on the monthly trade data using a fixed effects estimator that accounts for cross-country-product heterogeneities. Robust standard errors are reported in parentheses. Asterisk indicate the level of *** ** * significance, _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

Figure A6 reveals that imports were more affected by strict lockdown measures than exports. Exports to countries that had stringent COVID-19 policies fell by an average of around 12% between the first and third months of the lockdown where the coefficients are statistically significant. This result is contrary to Kenya where lockdowns reduced exports 28
Policy Research Working Paper 10896

# **Abstract**

This paper contributes to the discussion on Africa’s pathways to economic transformation by examining the roles of trade patterns—specifically, South-South and NorthSouth trade—focusing on intermediate and capital goods sourced from both the Global North and the Global South. The paper relies on a panel dataset comprising 44 African countries from 2000 to 2022. To address endogeneity concerns, it uses a two-stage least squares method, employing instrumental variables that leverage exogenous changes in trading partner conditions. Findings from the analysis indicate that imported capital and intermediates significantly predict economic transformation in Africa. However, the impact of imports from the Global North and the Global South varies depending on the specific channel of economic transformation. Imports from the Global South are more influential in driving structural change, while those from the Global North are more effective in facilitating productivity convergence. This divergence highlights the distinct roles that North-South and South-South trade play in Africa’s economic transformation agenda. The findings underscore the importance of a nuanced trade policy that leverages the strengths of both regional and global trade partners to advance Africa’s economic transformation.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 wkassa1@worldbank.org.

_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
|
|<br> <br>H-VS- PALE|3%|8%|4%|10%|17%|19%|
|<br> <br>_Source:_Authors’ estimates based on data from<br>_Note_: The table shows the percentage of pairs<br>consumption, monetary poverty rate (HM),mu<br>for each scenario, baseline and preferred. For<br>rate below 50%, on the one hand, and those w<br>the disparity between compared states.|NLSS 2019<br>of states whose c<br>ltidimensional po<br>each scenario, re<br>ith a monetary p|omparison is rever<br>verty rate (H) and p<br>strictions are applie<br>overty rate above 5|sed between two dif<br>overty-adjusted life-e<br>d in order to consid<br>0%, on the other. Th|ferent well-being<br>xpectancy (PALE). <br>er only states with<br>e aim of these res|indicators: average<br>Results are presented<br>a monetary poverty<br>trictions is to reduce|<br> <br>|

Second, when comparing monetary poverty and (baseline) PALE, the fraction of re-rankings is 7 percent. This fraction increases to 17 percent (12 percent) when restricting comparisons to the subset of states that have less (more) than 50 percent monetary poverty. Those frequencies of re-rankings are arguably substantial given that PALE accounts for monetary poverty. The table suggests that, in the baseline scenario, moving from monetary poverty to multidimensional poverty generates more re-rankings than moving from multidimensional poverty to PALE. This reflects the fact that (1) mortality is less heterogeneous across states than other-dimensions poverty and, to a lesser extent, (2) mortality is more correlated with monetary poverty than security deprivations.

Third, the
household expenditures, and bottom quintile per capita household expenditures and explore the interactions in the context of multiple spatial time series in Indonesia over the period 1999-2014. We seek to distinguish between the effects of average household growth and bottom household growth on pollution and see if there is differential in potential impacts of pollution on the two different income groups.

## **5.1 Data**

Our analysis relies on two longitudinal data sets. As a proxy for air pollution, we use the global estimates of fine particulate matter developed by van van Donkelaar et al. (2016). We 22
**Table 2. ICT intensity by sector**

||Mean|Standard Deviation|
|---|---|---|
|Agriculture|0.007||
|Mining and Quarrying|0.079||
|Manufacturing|0.060||
|Electricity and Water|0.241||
|Construction|0.017||
|Wholesale and retail|0.049||
|Trasportation and acccomodation|0.037||
|Information and communication|0.348||
|Finance|0.300||
|Real Estate and personal services|0.139||
|Arts and other services|0.013||
|Education|0.018||
|Health|0.018||
|Total|0.102|0.114|

Note: ICT intensity come from EUKLEMS data for the United States in 2007, and it is define as ICT capital services per hour worked, divided by 100. Public administration workers are excluded from the estimation sample.

16
6 monopoly on major exports (Sachs and Warner, 1995: 22). As shown in Figure 1, the subperiods with the highest percentage of "reformed" economies were the 1950s and the 1990s - one hundred percent of the countries were reformed after 1990. It is noteworthy that while Sachs and Warner were attempting to apply a somewhat "objective" criteria for classifying countries as "open" or "closed," criteria (c) - (e) are actually related with macroeconomic policy and the role of the state in production. Moreover, as mentioned earlier, trade liberalization in LAC has been implemented simultaneously with a broad set -of reforms, and thus the periods of "openness" can also be interpreted as being periods of "reform." Our main source of data is the data base on physical capital stocks, working-age population (aged 15 to 64) and gross domestic product (GDP) constructed by Nehru and Dareshwar (1993). This data base covers the period 1950-1990, but we updated it until 1995 using the World Bank's data base on World Development Indicators - the specific procedures that were used in this updating are described in the Appendix. The data for capital stocks was constructed by Nehru and Dareshwar (1993) using the perpetual inventory method applied to gross domestic fixed investment series. This data set has been used previously in studies of growth and openness by Harrison (1996) and Edwards (1997).

# **III. Results**

Table 1 reports growth decompositions for the 18 countries of our sample, using 0.4 as the capital share in output. On average, in the period 1950-1995, these countries

> **6** This is also the procedure adopted in the studies of income convergence across countries that use panel data. In these studies, as in the present paper, the motivation is that of purging long-run growth from the
- 29 -

2.36 Goverrnment policies to stimulate increased cereal production through improvements in infrastructure, subsidized inputs, stable producer prices, and a host of institutional supports have been largely responsible for the rapid rates of growth in production. With budgetary pressures and low international prices of cereals, these policies will probably be rolled back to some degree, thus forcing marginal land out of cereal production. These lands, marginal for cereal cultivation, may be better suited to alternative farming systems.

# Demand Factors

2.37 Three main factors determine the level of rice consumption: tastes, incomes and the price of rice relative to the price of substitutes. These three factors are interactive, and they are difficult to separate using aggregate data on consumption. Tastes of urban people are generally distinct from those of rural people because of differences in life-style and level of physical activity. The tastes of one ethnic group within a country may differ from those of other groups. Changes in the price of rice will have an effect on consumption because rice becomes more or less expensive relative to other alternatives. If rice absorbs a large fraction of total expenditures, a change in its price has substantial effects on real incomes. Absolute income levels of consumers also influence consumption patterns.6 2.38 In most countries, place of residence, whether rural or urban, is an important factor affecting patterns of consumption. Since rural people usually have lower incomes than those of urbanites, they are more likely to eat meals at home and less likely to purchase prepared foods made from wheat flour if they are more expensive. Preparation of rice in the household tends to be a time-consuming activity. As urbanization proceeds and women enter the labor force, the opportunity cost of time in the household tends to rise, and cooking rice has to compete with the 6/ This section is drawn extensively from Chapter 11 of Randolph Barker, Robert W. Herdt, with Beth Rose, The Rice Economy of Asia (Washington, D.C.: Resources for the Future,
awareness to establish a community-based play activity, and it was expected to impact three sets of outcomes: (1) community factors including discussion of school readiness at community meetings and the establishment of CPBAs, (2) participation in CPBAs and caregiver-child activities at home, and (3) children’s school readiness. The effects of the school readiness intervention on each of these outcomes, Y��, are estimated by We are interested in how the school readiness intervention affects children from disadvantaged socio-economic groups. One indicator of this is mother’s education. The report of the 2014 TEHCI found that children of mothers without high-school education had lower school readiness measures (Brinkman & Vu: 31). More broadly, mother’s socioeconomic status has been linked with better educational outcomes (Bjorklund, Lindahl & Plug 2006), health and child development including obesity (Currie 2009), health for young adults (Lundborg, Nilsson & Rooth 2014), age of marriage and fertility (Breierova & Duflo 2004), among others. For a child’s participation in a CPBA, caregiver-child interaction, and school readiness, the effects of the school readiness intervention are estimated for different sub-populations defined by gender and whether the child had a mother with a high-school education or not. Children of mothers without a high-school education comprise 30 percent of our sample. The effects by subpopulation are estimated with the following model: where f is a binary variable for being a girl, and h is a binary variable for having a mother who completed high-school.

# _Effects of the school readiness intervention_

Table 3 presents the effect of the school readiness intervention on the measures of community support for school readiness collected in mid-2017. These are estimated with model (1). Data were collected previously, but this table is shown because it is the last round of data collected 16
Policy Research Working Paper 7890

# **Abstract**

Private investment can be an important engine of economic growth in East African countries, which, despite recent growth rates, are still plagued with adverse economic conditions. Against this backdrop, there has been substantial penetration of mobile money, moving beyond simple person-to-person exchanges toward adoption by private firms. This study explores whether there is a relationship between firm adoption of mobile money and firm investment. Using firm-level data that are nationally representative of the private sector in three East African countries—Kenya, Tanzania, and Uganda—a positive relationship is found between mobile money use and the probability of a firm’s purchase of fixed assets. This relationship is attributed to reduced transaction costs, increased liquidity, and increased credit worthiness associated with the use of mobile phone financial services.

This paper is a product of the Global Indicators Group, Development Economics. 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://econ.worldbank.org. The authors may be contacted at aislam@worldbank.org.

_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