rafmacalaba/data-use-mentions-extended huggingface dataset:
both migrant (treatment) and non-migrant (control) individuals. In addition, data on pre-migration conditions are also needed (Carletto et al., 2014). While the shift from older cross-sectional studies, which were vulnerable to a wide range of potential confounders and sources of omitted variable bias, to more robust panel methods exploiting longitudinal information on household and individual movements, indeed represents a huge empirical step forward, the use of panel settings is not in itself a panacea. In fact, the issues highlighted above of self-selection, endogeneity, transmission channels, longer-run effects and persistence, reverse causality and other key empirical concerns still need to be addressed through clever research design (De Brauw and Carletto, 2012). Researchers in the field have traditionally exploited lotteries (e.g., the popular study on Saudi Arabia to Hajj Visa, Clingingsmith et al., 2009, but also the works by Gibson, McKenzie and Stillman (2011) and Gibson et al. (2018)) and other possible sets of “exogenous variations” or “natural experiments” able to mimic the hypothetical situation of a random selection of migrants. However, what they get is an estimate of the impact of migration only for the sub-group of beneficiaries (the Local Average Treatment Effect – LATE in the jargon of policy impact assessments) without “external validity”. A valid alternative, in this case, could be the use of governmental policy experiments to learn about the effectiveness of alternative policy initiatives, but the latter goes beyond the scope of this analysis.

# _3.2 Data gaps and needs_

Given the conceptual and empirical limitations of existing studies and the research priorities set out above, we have identified the main data gaps that currently prevent carrying out the proposed research agenda and possible solutions to address these data needs that can come from improvements to longitudinal, multi-topic household surveys such as the LSMS-ISA. We here start by outlining the main data gaps and needs separately for migration and weather data.

**Migration data** . Until
in the _Monga_ season, suggesting that borrowing is highly depressed in the _Monga_ season when credit demand is the highest. Credit demand is also found to respond positively to the availability of credit through credit agencies in the village. Supply of microcredit is higher in villages with higher number of MFIs working. Villages with a commercial bank network seem to have higher incidence of borrowing from informal sources, suggesting that developed villages have higher demand for credit that sometimes come from informal lenders rather than formal lenders.

The equations for borrowing (that is, microcredit, formal, informal and aggregate borrowing), the results of which are presented in Table 3a, are estimated independently, assuming that their error terms are not correlated. However, if the error terms are correlated (or ‘contemporaneously correlated’), the equations should be estimated jointly to get more efficient estimates. Such equations are called ‘seemingly unrelated regression equations’ or SURE, and the estimator for this problem is called SUR estimator, proposed by Zellner (see Zellner 1962; 1963).<sup>10</sup> Table 3b reports findings based on SUR estimator.<sup>11</sup> The effects of average seasonal income and share of actual seasonal income in average income are stronger based on SUR estimation. For example, a 10-percent increase in per capita average income raises the borrowing from microcredit lenders by almost 2 percent, and a one percentage point increase in the share of seasonal income is associated with 0.06 percent increase in microcredit borrowing. As for the independent effects of the seasons, except for microcredit borrowing, the borrowing from other sources seems to be affected by all three non-Monga seasons.

> 10 SUR estimator uses the asymptotically efficient, feasible, generalized least-squares algorithm to jointly estimate the equations. A detailed treatment of the SUR estimator can be found in Zellner (1962, and 1963).

> 11 These findings are based on random-effects (RE) estimates using panel data of 1998/99 and 2010/11. We also
# **Children and the Fiscal Space in Ethiopia**<sup>*****</sup>

**Alemayehu A. Ambel**<sup>**†**</sup> **Getachew Yirga Belete**<sup>**‡**</sup> **Oliver Fiala**<sup>**§**</sup>

**Key Words:** Child Poverty; Fiscal Incidence; Commitment to Equity for Children; Ethiopia **JEL Codes:** H22, I32, J13

> * The study is supported by the Global Tax Program. We thank Jose Antonio Cuesta Leiva and participants of conferences held in Addis Ababa, Luxemburg and Oxford for their comments on an earlier version of the paper. We also thank Olutola Jordan for her help in editing. Any remaining errors are our own.

> † Living Standards Measurement Study (LSMS), Development Data Group, World Bank

> ‡‡ Department of Economics, Bahir Dar University

> § Global Policy, Advocacy & Research Department, Save the Children UK

_._
d, but without a sustained increase in the growth rate. Box 1 in the DSA in IMF (2021) notes the key role of oil discoveries in supporting growth, containing the current account deficit and boosting FDI.

2. Oil discoveries would also raise primary fiscal surpluses and increase foreign exchange reserves, causing the exchange rate to strengthen but, once again, on a one-off basis.

Controlling for such idiosyncrasies by running “with” and “without” scenarios is important to get a clear idea of underlying public debt dynamics and is something the LIC DSF should be required to do. Indeed, d returned to an explosive path, rising from 58% in 2018 to 79% in 2020 and was projected to rise further to 87% in 2026 in the DSA in IMF (2021). The temporary salutary effects of GDP rebasing and new oil discoveries had worn off and the underlying explosive dynamics took hold again.

Nevertheless, as in 2016, the 2021 DSA rates Ghana at high risk of both external and overall debt distress but qualifies this by noting that “ <mark>public debt is sustainable provided that the authorities’ medium-term consolidation plan (reflected in the baseline) is rigorously and credibly implemented to improve the primary balance, put debt on a declining trajectory, and ensure continued market access”.</mark> _Applying the alternative approach using data from the 2021 DSA_ We now repeat the steps gone through in the context of IMF (2016) to see what information and early warning can be gauged from the alternative approach set out above. We first make the case that public deficits drive the CAD and then examine public debt dynamics. This time, we calculate three interest rates: the composite real interest rate and its domestic and foreign currency components along the lines of the numerical example in Box 1. Annex 1 derives the formulas based on the first-order difference equation for the ratio of public debt to GDP, which forms the basis for the public sector DSA in the LIC

> 8 An explanation for this
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **4. Data and methodologies**

# **A. CDR data descriptive**

The mobile penetration rate<sup>2</sup> of The Gambia was 94.2 percent in 2013 and rose to 140 percent in 2018, which was higher than the average of developed countries (“ITU ICT-Eye,” n.d.). As of 2018, 98.4 percent of households reported ownership of at least one mobile phone with limited variation across regions and between rural and urban areas. On the individual level, 85.1 percent of men and 74.1 percent of women in the age group between 15 and 49 years own a mobile phone (The Gambia Bureau of Statistics, 2019). According to PURA the four major MNOs provide services to 2.59 million subscribers as of 2020, even allowing for persons with multiple sim-cards. In this paper, we use CDR data for a three-month period between March 2, and May 31, 2020, offering a snapshot of changes in mobility during the COVID-19 lockdown.

Data was made available for two major MNOs, which cover approximately 70 percent of the market and include around 1.75 million subscribers. On average, the data comprises 18.8 million data points per day with very limited variations over the data period, 2 billion anonymized observations in total. Hence, we assume that cell phone usage in terms of transaction volumes did not change fundamentally once COVID-19 hit the country. The average number of records per subscriber per day is 10.6 where approximately 2.6 records are used for calling. Like other developing countries, multiple-SIM-card holding is common in The Gambia. We expect a certain overlap between the two-MNO subscribers, which might have resulted in over-representing the multiple-SIM-card holders. In this study the impact of the multi-SIM holding on the analysis result is considered to be limited since the two MNOs primarily market different socio-economic
estimates for gaps in growth rates using different weighting schemes are available in Appendix 2, Table A2.2.

The general pattern of a larger gap in growth in middle-income countries is reasonably consistent with the findings regarding the gap in levels. The gap in levels is increasing across the middle-income countries, suggesting that surveys also grow slower than national accounts means over this segment. For high-income countries, the point estimate suggests that growth rates of consumption in surveys is higher than that in national accounts, which contributes to explaining the narrowing gap seen for high-income countries (Figure 3). The narrowing of the gaps as countries get richer could be due to the increased integration of survey data with national accounts in richer countries, noted earlier. Notably, in US and Canada, this integration between survey and national accounts measures is not practiced to the same extent, and a larger and widening gap is seen in these countries.

In addition to the direction of bias (or gaps), we are often interested in the precision (efficiency) of growth rates of national accounts as a predictor of survey growth rates, given that national accounts data are frequently used to interpolate and extrapolate household survey consumption or income to obtain estimates of poverty in years surveys are not conducted, as done by the World Bank. The mean absolute error and the root mean square errors presented in Table 2 gives an assessment of the precision, which is relevant for using national accounts in predicting survey growth. Again, there is a clear economic gradient with the precision increasing with income, but it should be noted that high-income countries typically experience lower growth rates and the gradient of relative errors may be smaller. The mean absolute error is large, more than 2 percentage points for all sub-groups except for North America and high-income countries, highlighting the large average differences in growth rates observed between HHS and NAS data sources.

Ravallion (2003) uses an alternative way to assess the degree of correspondence of growth rates, using a simple no-constant OLS regression of survey means on national
br>2.09<br>|(1.27)<br>Total area cultivated (ha) in 2014<br>5.77<br>445|(.)<br>Number of fertilizations in 2014<br>1.58<br>|(0.54)<br>Number of days between sowing and 1st fertilization in 2014<br>36.32<br>|(21.48)<br>Self-reported yield (ton/ha) in 2014<br>2.06<br>(1.16)|_Panel C: Plot Characteristics_<br>Sand (%)<br>70.93<br>(9.96)<br>Clay (%)<br>14.71<br>(5.43)<br>Slope (degrees)<br>3.24<br>(3.43)<br>pH (1:2 water)<br>6.07<br>(0.71)|Note: This table reports summary statistics for the study sample of 678 farmers in 2014. For each variable, we report the mean a<br>all treatment coeffcients are jointly equal to zero. The data were collected using our Baseline survey conducted in February 201<br>reftab:defvars for the defnition of the variables.|

32
|Competitors 1|(0/1) if a firm faces 0 competitors in the main market|
|---|---|
|Competitors 2|(0/1) if a firm faces 1 competitor in the main market|
|Competitors 3|(0/1) if a firm faces 2 to 5 competitors in the main market|
|Competitors 4|(0/1) if a firm faces more than 5 competitors in the main market|
|International market|% of firms whose direct exports consist of more than 50% of total sales by sector (at 2-<br>digit ISIC level) and country for each survey year|
|**_Other_**||
|Materials|Log (constant US$) raw materials and intermediate goods per full-time worker|

Nominal variables such as labor productivity, R&D per worker, fixed investment, and materials are deflated using weighted GDP deflators from WDI. The GDP deflators are weighted based on the closing month of each firm’s last completed fiscal year. For example, if a firm’s last complete fiscal year spans July 2015 through June 2016, then relevant deflator corresponds to 6 6 d d d d 2015 + d d d d 2016. Since these variables are expressed in local currency (LCU), 12 12 we convert them into USD using weighted exchange rates.

When the closing/starting month data of the last completed fiscal year is missing, we assume the start month to be 1 if the last completed fiscal year spans only one year (i.e., 2014) and 7 if the last completed fiscal year spans two years (i.e., 2013-2014). If the last completed fiscal year variable is missing, we assume it as survey-year minus 1 or refer to the corresponding questionnaires if they indicated the actual last completed fiscal year. We use the sampling weight according to median eligibility from WBES. To avoid biases from extreme values, we censor the
*|1.5|-4.0|
|SSC and above|6.9|8.2|8.9|1.3|2.0|0.7|
|HH receive international|2.0|0.4|1.8|-1.6**|-0.2|1.4|
|remittance=1|||||||
|Average log of HH annual|9.6|9.4|9.5|-0.1***|-0.1|0.1|
|expenditure per capita|||||||
|HH living standard index|-0.06|0.02|0.1|0.08|0.2|0.1|
|Number of children aged 6 to 14|0.8|0.7|0.5|-0.1|-0.2***|-0.1|
|years|||||||
|Number of working-age women aged<br>15–64 years|1.4|1.3|1.1|-0.1|-0.2***|-0.1*|
|Number of working-age men aged|1.4|1.1|1.1|-0.3***|-0.3***|-0.01|
|15–64 years|||||||
|Number of HH members aged 65+|0.07|0.1|0.03|0.06|-0.04**|-0.1***|
|**Regional characteristics**|||||||
|Dhaka City Corporation=1|87.3|88.5|84.2|1.2|
findings are subject to limitations from the relatively small number of observations available for these specific variables and the challenges in precisely assigning exposure levels. Indeed, the pollution exposure assignment during pregnancy is only accurate if households have not relocated too far since birth. Otherwise, the measurement error introduces a bias of the coefficient towards zero. In light of these considerations, we exercise caution as the DHS dataset may not be the optimal resource for examining the long-term effects of environmental hazards on child health. Nevertheless, we present these results transparently, aiming to offer insights and guidance for future researchers in this field.

data (Silva and Tenreyro, 2011).

9
Policy Research Working Paper 9203

# **Abstract**

Decades of conflict have contributed to high flows of internal displacement in Iraq. The incidence of these flows on the welfare of internally displaced persons is not well understood. This paper attempts to fill this gap in the literature by investigating the link between internal displacement and multidimensional poverty, using one of the most comprehensive household surveys for poverty analysis in Iraq. The results show crucial differences between internally displaced and non-displaced households with respect to multidimensional poverty. Furthermore, instrumental variable regression analysis suggests that the relationship is causal, that is, the probabilities of multidimensional and monetary poverty are higher because of internal displacement.

This paper is a product of the Poverty and Equity 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 cnoumedemtemgoua@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
.12|59.14|0.09|892.12|
|Public debt service|3.91|3.24|0|35.24|

Note: All variables as % of GDP except real GDP per capita (thousands of constant 2000 international dollars) and its growth rate and urbanization (urban population, % of total). Source: author’s analysis based on data described in the text.

training and scholarships in donor countries, remunerating experts and consultants, and financing equipment and administrative costs associated with TC primarily involve direct payments from donor governments rather than transfers of money to recipient governments. In fact, Sundberg and Gelb (2006) argue that many aspects of TC, such as finance for training programs, analytical reports and expert advice, involve resources that never even leave donor countries. For the seven countries that they study, IDD and Associates (2006, p. 23 in annex B) indicate that off-budget aid is explained, among other things, by “aid in kind e.g. TA [technical assistance] and other aid where expenditure is undertaken directly by the donor”. Similarly, Fagern ̈as and Roberts (2004b) argue that technical assistance involves donors making direct payments that are not reflected in budget documents, and Feeny (2007, p. 442) states that “the salaries of external consultants will not enter public sector accounts”. Feeny argues that a larger share of aid is off-budget in Fiji and Vanuatu compared with Papua New Guinea and the Solomon Islands because the former two countries receive a large proportion of their aid in the form of technical assistance. In addition, Fagern ̈as and Roberts (2004a) attribute discrepancies between donor 12
|**Cost utility**<br>**analysis (a subset**<br>**of CEA)**|Incremental cost<br>effectiveness ratio<br>(effect in generalized<br>units) Net<br>health/monetary<br>benefits|Costs and effects<br>from a defined<br>perspective|Marginal<br>productivity of<br>health system (k);<br>ICERs of competing<br>investments|iDSI Reference Case;<br>[2] Drummond 2015<br>[46]|
|---|---|---|---|---|
|**Cost effectiveness**<br>**analysis**|Incremental cost<br>effectiveness ratio|Costs and effects<br>from a defined<br>perspective<br>Effects in natural<br>units|ICERs of competing<br>investments (in same<br>effect units)|<br>Drummond 2015 [46]|
|**Social return on**<br>**investment**|SROI|All costs and<br>benefits converted<br>to monetized<br>form, plus wider<br>social impact|<br>Understanding of<br>relative weight or<br>importance of<br>additional social<br>benefits|Olsen 2003 [47]|

The complexity and variety of DHIs and wide range of potential decision makers necessitates that the concept of “Return on Digital Investment” utilizing the DHI economic evaluation framework does not prescribe a particular form of aggregation, however it does require that researchers and decision makers are cognizant of the options available, the additional information required for their interpretation, and the risk of creating false certainty through obfuscation of important gaps in evidence, attribution, and valuation.

The use of the Impact Inventory in addition to value
determinant of e-commerce. This suggests that competition induces organizational and management changes in the use of internet, although not in the case of e-commerce. In general, the impact of competition from informal firms is not significant, suggesting that either ICT adoption is not an option to compete against these firms, or given the significant impact of size, that ICT adopters are not the firms that face the strongest competition from informal firms.

_<u>Table 14 Base model estimations - Pooled sample</u>_

|**Pooled sample**|**(1)**<br>**Log(sales/l**<br>**)**|**(2)**<br>**Product**<br>**innovation**|**(3)**<br>**Process**<br>**innovation**|**(4)**<br>**R&D+ per**<br>**worker**|**(5)**<br>**ICT total**<br>**index**|
|---|---|---|---|---|---|
|Log(K/L)|0.1869***<br>(0.028)|||||
|Log(L)|0.3014***|0.0121|0.0229**|0.1152|0.2006***|
||(0.054)|(0.010)|(0.010)|(0.088)|(0.016)|
|Product Innovation|-0.0214|||||
||(0.121)|||||
|Process Innovation|<br>
systems, large landowners responded to the threat of land reform by either evicting tenants who could have made claims to land ownership under a possible reform program, or converting them into wage laborers. In the case of eviction, landlords reduced reliance on hired workers either by resuming extensive livestock production and ranching or -aided by significant credit subsidies- by embarking on highly mechanized self-cultivation (Binswanger et al. 1995). This did not only reduce tenant welfare but also depopulated farms and created further difficulties for redistributive land reform. A number of further difficulties of effective land reform in hacienda systems are associated with policy distortions, limitations on the functioning of the land market, and inability to provide the necessary complementary elements for land reform beneficiaries to start successful small farm enterprises.

First, the cost of carrying out land reform were often increased by the continued existence of implicit and explicit policy distortions (e.g. agricultural protection and selective credit subsidies) which drove land prices above the capitalized value of agricultural profits and often disproportionately benefited large producers. Such distortions increased the fiscal cost of land reform policies and reduced their sustainability by making it profitable for land reform beneficiaries to sell their newly acquired land back to large farmers. Indeed, despite attempts to limit beneficiary desertion through imposition of legal restrictions and the threat of punishment in case of contravention, there is considerable anecdotal evidence on land sales by reform beneficiaries in Nicaragua, Colombia, and El Salvador. In a recent Census of Brazilian land reformn settlements, only about 60% of recently established land reform beneficiaries were actually found tilling their land (El Globo 1997).

Secondly, many countries aimed to implement land reform by eliminating or restricting other forms of (rental and sales) market transactions. This completely eliminated price and other market signals, making it more difficult to select beneficiaries and land and further increased the costs of land reform implementation. It also tilted the balance in favor of a highly centralized mode of land reform implementation that has, in a number of countries, led to the