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

Satellite-recorded nighttime light data are used extensively as a proxy for economic activity. However, surprisingly little economic analysis employs data from the Visible Infrared Imaging Radiometer Suite (VIIRS).<sup>1</sup> These new nighttime light data, with a better resolution and a higher frequency than the previous generation of data, have the potential to facilitate our understanding of rapid and spatially heterogeneous economic changes, such as those during the COVID-19 pandemic.<sup>2</sup> One reason for the hesitancy to use these data in the economic literature may be the difficulty of converting changes in nighttime light intensity into changes in economic activity. To the best of our knowledge, no properly estimated and widely accepted quarterly elasticity between VIIRS nighttime lights and economic activity exists to date.<sup>3</sup> In this paper, we attempt to fill this gap.

VIIRS nighttime light data are an imprecise measure of man-made lights. Even after aggregating the data to the country level and to quarterly frequency, substantial statistical noise remains. It mainly stems from atmospheric conditions like cloud cover that impact the effective number of observations. For instance, there are only five effective observations at the pixel level on average each month for a median developing country. Equally important, missing observations in the summer months and occasional satellite sensor recalibrations contribute to the noise as well.

In this paper, we provide a novel framework to estimate the elasticity between nighttime light intensity and gross domestic product (GDP) at quarterly frequency that takes measurement errors of nighttime lights explicitly into account. The elasticity can be identified because countries exhibit varying noise in their nighttime light growth. Using information from the average number of effective observations, we provide a regression equation that estimates the elasticity precisely. In emerging markets and developing economies (EMDEs), a 1 percent change in GDP is associated with a 1.55 percent change in nighttime lights. While the elasticity varies somewhat with a country’s income status and
rst order effect of trade distortions, the simpli fi ed TRI is informative:<sup>15</sup>

> 12 We found similar patterns when regressing on the median elasticity by country. The coefficient on GDP is 0.03 and on GDP per capita - 0.02 and both are statistically signi fi cant at the 1 percent leve.

> 13 Data sources for tariff data are United Nations’ Comtrade and the Integrated Database of the WTO. In this paper, we abstract from measuring the trade restrictiveness of non-tariff barriers, as well as the role of tariff preferences in eroding trade restrictiveness. For an attempt to do so, see Kee, Nicita and Olarreaga (2004b).

> 14 If NTB measures are to be considered, trade economist often use simple or import-weigthed coverage ratios of NTBs.

> 15 See Equation (3.5) in Feenstra (1995). Note that given our setup, the derivation in Feenstra (1995) is equivalent to deriving the TRI that would keep GDP at its maximum level given the existing tariff structure.

18
decreased the probability of delivery at a health center by a skilled health professional.

We use a careful empirical strategy employing a quasi-experimental methodology to explore these conflict-related impacts. To estimate the effect of the BH insurgency on IPV we spatially link geo-referenced data on conflict events from the Armed Conflict Location and Event Database (ACLED) with survey data from two rounds of the Domestic Violence (DV) module of the Nigerian Demographic and Health Survey (NDHS) collected in the period before and during the BH insurgency, and apply a difference in difference approach.

The remainder of the paper is structured as follows. Section 2 provides some background on the IPV prevalence in Nigeria and the Boko Haram insurgency. Section 3 discusses the conceptual framework. Section 4 presents the data and empirical model specification. Results are presented in section 5 and section 6 concludes.

# 2. Background: IPV prevalence in Nigeria and the Boko Haram insurgency

The most recent estimate of IPV in Nigeria, based on data from the 2013 NDHS, suggests that 16 percent of women have ever experienced physical or sexual IPV (NPC 2014), a rate considerably lower than lifetime prevalence of IPV among ever-partnered women for Africa -37% (World Health Organization, 2013). However, IPV rates vary considerably across regions, reaching 28 percent in the South-South region (Table 1). Analysis of the NDHS data finds that there are significant ethnic and geographical differences in the likelihood of experiencing IPV (Lino et al. 2013; <mark>Nwabunike and Tenkorang 2015</mark> ) and qualitative and smaller scale quantitative studies find 3
examine the presence of offsetting effects on either side of the mean treatment effect we compare the distribution of the unadjusted (original) percentile rank statistic to a uniform distribution. In Section 5, we apply both mean-adjusted and unadjusted heterogeneity tests to all of our outcome variables, using the results from our benchmark specification of the SCM.

# **4.  Data, Summary Statistics and Implementation**

## **4.1. Data**

The raw data we employ track Serbian imports for consumption at the transaction level for the period 2010-2013. The data were provided confidentially by the Serbian national customs authority. The Serbian customs agency processes goods at the level of individual import declarations (each declaration may contain one or more transactions). The primary fields of interest used in this study are the registration time and the clearance time for each declaration (which allow us to calculate the clearance time in hours per declaration), a numeric identifier for the importing firm, a Harmonized System (HS) 10-digit commodity code for each transaction, the country of origin, a field indicating what type of inspection the declaration was subject to, and a field indicating whether the declaration was cleared under a special customs clearance procedure. This latter code which we designate as ‘special clearance’ includes transactions of goods that were cleared under the IHC program, but also includes other special clearance codes.<sup>33</sup> Our outcome variables of interest, calculated from the raw data aggregated to the firmmonth level, are a) the (log) median time necessary for the firm to clear customs for its import shipments in a month, b) the (log of the) 75<sup>th</sup> percentile of customs clearance times in a month, c) the (log of the) 90<sup>th</sup> percentile of customs clearance times in a month, d) the rate of inspections of the firm’s import declarations in a month, and e) the
ownership tree in the same way as in step 2, only if this tree exits. Any extra entities (and their BvD IDs) from these additional trees are added into the set of Corporatized SOEs.

12. Use the ORBIS interface to identify all the firms, for which the GUO is a “Public Authority” in the variable “Type of Entity”. We cross check this set of firms within the Corporatized and Noncorporatized sets of SOEs. For those extra firms that are missing in these sets, we classify them into either the Corporatized set or the Non-corporatized set based on the legal form reported in the variable “National Legal Form” in ORBIS.

13. For the extra firms identified in step 12), construct the ownership tree in the same way as in step 2 only if this tree exists. Any extra subsidiaries (and their BvD IDs) from these additional trees are added into the set of Corporatized SOEs.

14. Use the information of sector from variable “NACE Rev. 2, core code (4 digits)” in ORBIS for all the entities identified in the ORBIS Database and the information of sector from the SOE Supplementary Database for only those entities identified through the SOE Supplementary Database to eliminate all entities that fall within one of the excluded sectors described in Section 2.

15. The remaining entities in the Corporatized and Non-corporatized sets make up the registry of SOEs in the country.

> 36 The review of the legal framework in each country was conducted with the support of the WB country local teams.

> 37 It means that there are firms reporting the No-corporatized SOE as a shareholder.

22
market impacts of violence, it is subject to one of the same criticisms leveled at the accounting approach: important categories of costs are not examined—although in this case the methodology makes no claim of producing a comprehensive estimate of the costs of GBV.

Another option for estimating the socioeconomic costs associated with GBV—and one frequently employed by economists to establish the market value of non-market goods—is to estimate the willingness of individuals (and by extension society) to pay for lives free of GBV. This approach has the advantage of producing a comprehensive estimate of the cost of GBV in a specific locale. The approach, however, has been used only very infrequently to gauge the welfare loss occasioned by GBV (see Sorenson (2003) for one of the few examples), presumably for two reasons. First, as the CEDAW and Belém do Pará conventions make explicit, living lives free of violence is a human right; estimating the willingness to pay for a right—while demonstrating the importance society attaches to an issue—may itself be controversial. A second unattractive feature of willingness to pay estimates is that they are sensitive to income levels and income distribution.

A final option for estimating the socioeconomic costs of GBV it to use the metric of disability adjusted life years (DALYs) lost to GBV. DALYs have the great advantage of including years lost due not only to premature mortality, but also due to disability or illness;

> 16 The matching methodology estimates a reduced form equation for the determinants of the probability of suffering violence; it then matches women who suffered violence with those who did not, but had a similar “a priori” probability of having done so.  Determinants of violence included in this regression were age, marital status, education level, and many others.
survey or earlier.<br>b) If a patient visited a health facility other than<br>the reported visit to a hospital<br>c) The number of visits to hospitals or health<br>posts during the 15 days before the survey.|**Assumptions for Method 1:**<br><br>We assume that all reported visits occurred<br>during the 15 days before the survey.<br><br>If a respondent reported going to a hospital, we<br>assume he/she went to the hospital during the 15<br>days before the survey and assign the official<br>per-beneficiary hospital expenditure to that<br>respondent. We then aggregate that information<br>to determine total healthcare expenditures per<br>quintile.<br>**Assumptions for Method 2:**<br><br>We assume that annual government<br>expenditures on healthcare facilities are<br>distributed homogeneously across the year.<br>Therefore we divide the official information b|
|3|All information on public spending and number of<br>beneficiaries (i.e. patient visits) is annual.|y<br>24 to pro-rate for a 15-day period.<br><br>We use the household survey data to determine<br>the composition of beneficiaries by income<br>quintile.<br><br>We use these proportions to assign to each<br>quintile a share of the 15-day government<br>expenditure.<br>**Assumptions for both methods:**|
|4|The number of survey participants who reported<br>visiting a hospital during the previous 15 days is<br>almost equal to the number who reported visiting a<br>clinic. But this does not correspond with the|<br>Households‘ out-of-pocket expenditure is the same<br
subject to substantial fluctuations, however, the following analysis aims at netting out the individual-specific component in how successfully each technology is operated by a given household. Moreover, we have to allow for the possibility of both individual and social learning when the new technology is used.

Learning by doing implies that individual’s catches trend upwards after adoption as the individual learns how to use the new technology more efficiently over time. Social learning (or learning from others), on the other hand, implies that an individual can use the expertise other individuals have acquired with the new technology to become more efficient himself. Quite generally, the latter implies that the “learning curve” of an individual, that is his success as a function of time since adoption, depends on the amount of information available at the time he adopts. More specifically, we expect the learning curve of later adopters to be flatter since they start out with relatively more information at the time of adoption. With monthly sales data from 43 fishermen who switched to a fibre boat before the date of the interview, a test for individual as well as social learning is thus facilitated by the regression specification _ysit_ denotes monthly sales (in Rupees) of fisherman _i_ in month _t_ who currently operates technology _s_ , where _s_ = _C_ for catamaran and _s_ = _F_ for a fibre boat. Also consistent with the notation in the previous section, _t_<sup>_∗_</sup> _i_<sup>denotesthetimeofadoptionbyindividual</sup><sup>_i_,and</sup> _τi_ denotes time since adoption, so that _t_ = _t_<sup>_∗_</sup> _i_<sup>+</sup><sup>_τi_.Variable</sup><sup>_μsi_isanindividual-specific,</sup> technology-dependent intercept term or fixed effect, while _δt_ is a month-specific dummy that picks up aggregate fishing conditions and shocks. Finally, _usit_ is an i.i.d. error term with _E_ [ _usit_ ]
considered: the presence of an inflation targeting regime, the degree of central bank transparency, the exchange rate regime, financial openness, trade openness, and the degree of fiscal sustainability. Inflation targeting regime and fixed exchange rate regime are dummy variables for which the presence of the indicated regime equals one. Exchange rate regime is determined using Shambaugh (2004). Central bank transparency and financial openness (capital account openness) are measured using indexes produced by Dincer and Eichengreen (2014) and Chinn and Ito (2017), respectively. Trade openness is measured as imports divided by domestic demand (domestic demand is defined as gross domestic product (GDP) + imports - exports). Fiscal sustainability is measured as the ratio of gross public debt to GDP.

The empirical exercise is undertaken in four steps. First, all variables are tested in a panel setting for unit roots.<sup>5</sup> Some tests do not reject the null hypothesis of nonstationarity of trade openness and gross public debt-to-GDP ratio (Table A.4.2). Second, since some variables (including the inflation targeting dummy, fixed exchange rate regime dummy, and financial openness index) are stationary, residual series are obtained from a panel regression of sensitivity of inflation expectations on these stationary variables. Specifically, the following model is estimated: where _β i,t_ is the time-varying estimate of the country-specific estimate of the elasticity of inflation expectations to inflation shocks, as explained in the discussion of regressions with time-varying parameters. _MPi,t_ is (i) a dummy variable that takes a value of one in countries with an inflation targeting framework or (ii) a measure of central bank transparency. _Xi,t_ includes a dummy variable that takes a value of one for countries with a fixed exchange rate regime and financial openness index. _Ѳ i_ captures country-fixed effects and _φ t_ refers to time fixed effects.

Third, the existence of cointegration between the residuals from the panel regression in model (5) and the gross public debt-to-GDP
Policy Research Working Paper 10765

# **Abstract**

By 2023, more than 108 million people were forcibly displaced worldwide, with children under 18 constituting 45 percent. This study examines the human development gaps between forcibly displaced migrant and host children and adolescents, focusing on cognitive and socioemotional skills and physical and mental health. The study also explores how access to services and regularization programs are correlated with these disparities using a unique and comprehensive longitudinal data set of around 2,500 Venezuelan migrant and Colombian host children and adolescents, ages 5 to 17 and living in Medellín, Colombia. The findings reveal significant developmental delays among migrant children in physical and cognitive development, but interestingly, no significant differences in socioemotional and mental health outcomes. The research underscores how the availability of public services and engagement in regularization programs are crucial for mitigating these developmental gaps.

This paper is a product of the Development Research 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://www.worldbank.org/prwp. The authors may be contacted at sandrarozo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work
VFs where more committee members have accounting experience, and where the accounts are maintained on computers – both good indicators of managerial competence.

For on-lending VFs, those that require (rather than just allow) their members to save have higher credit ratings – see Appendix 1. VFs that would, if funds were short, cut the size of loans across the board, also had higher credit ratings. More surprising, perhaps, is the finding that when the committee has more members with some university education, the credit score is lower. This is surely related to our earlier result that links the higher education variable with lower capital availability (Table 20), holding other effects constant.

# **Outreach**

Two other measures of performance are of importance. The first looks at the proportion of individuals in a village who borrow from the VF – a measure of outreach that we refer to as “lending density.” Theoretically, we expect altruistic village funds to favor a wide outreach. The second measures the number of loans going to the “poor” (defined as households with expenditure per capita below the village median) relative to the number of loans going to the non-poor. We expect, again theoretically, loans to go to both groups, but probably with a tilt toward the poor, and indeed this is what we find, with a ratio of 1.68 (poor borrowers per non-poor borrower) in rural Thailand. This latter measure was obtained by matching the 2009 SES data with the data from the 2010 VF survey and the 2009 Community Survey, which was possible only in rural areas.

A first cut at the data may be seen in Table 23, which uses household data from the socio-economic surveys of 2009 and 2010 to map VF borrowing by expenditure per capita quintile. Poorer households are more likely to borrow, pay lower interest rates, and get somewhat smaller loans than their richer neighbors. Table 24 breaks down VF borrowing by household (financial) assets, and shows that VF borrowing is particularly
|Reweighting||survey and admin<br>data|Use admin data to adjust<br>sample weights so that<br>shares of high earners in the<br>surveys match the admin<br>data. Requires common<br>support.|Campos-<br>Vazquez and<br>Lustig (2019)|
|---|---|---|---|---|
|Replacing-<br>imputation|Item non-<br>response|survey data only|Use incomes of those who<br>do respond to impute<br>incomes for those who don't|Campos-<br>Vazquez and<br>Lustig (2019)|
|Replacing-<br>parametric<br>distribution<br>Replacing|Various<br>Various|survey data only<br>survey and admin<br>data|Estimate parameters for (for<br>example) a Pareto<br>distribution using the<br>bottom X% of the survey<br>distribution, impute earnings<br>for the top (1-x)%<br>Replace the top X% of the<br>survey distribution using<br>admin data and either<br>parametric or non-<br>parametric methods.<br>Endogenously determine a<br>merging income value,|Jenkins<br>(2017),<br>Hlasny and<br>Verme<br>(2018)<br>Alvaredo<br>and<br>Londono<br>Velez (2013),<br>Bach et al.<br>(2009),<br>Burkhauser<br>et al. (2018),<br>Czajka<br>(2017), Van<br>der Weide et<br>al. (2018)|
|Reweighting and<br>Replacing|Various
made, took place in August 2016 – i.e., before the states had begun implementation – also generated controversy. Stakeholders disagree as to whether it was reasonable to expect that states could have initiated some activities even without these funds, but all agree that the level of delay was unanticipated and that funds were expected to have been in the states much earlier.

Regarding Bank monitoring of the TA, the SOML supervision budget covered two weeks of staff time supervising the TA, though no _in‐country_ staff had a direct monitoring responsibility.<sup>25</sup> Initially, the firm was encouraged to carry out a mini household survey to check progress mid‐way through the assignment, but this did not take place.<sup>26</sup> The Bank TTL for the TA (from the Governance practice and based out of country), who acted as the contact point for ADG, evaluated their deliverables (sharing them with health sector colleagues) and managed their payments, confirms that monitoring was made additionally difficult by the fact that adaptive assistance, by its nature, is tough to report on or assess. In the absence of a reliable means of viewing progress on indicators or a reporting system that captured evidence of problems solved or capabilities enhanced, Bank staff began to question what was actually happening on the ground – but had no means of verification other than the word of the firm itself and their fairly convoluted, opaque reports.<sup>27</sup> Missions to the states (and especially rural areas) are logistically difficult<sup>28</sup> and the TA firm’s communication style has also been challenging.<sup>29</sup> As a result of growing concerns, Bank staff began to push the firm to implement solutions, specifically some solutions that it felt were technically appropriate. These solutions were dutifully implemented across the range of contexts in which ADG was operating, and as a result (as noted above) the TA
5 and 0.85. For Argentina, Lopresti _et al._ (2015) find an increase of yield of 109 kg/ha with each additional point of peak NDVI as measured by MODIS data (R<sup>2</sup> of 0.53). For10 seasons in the 2000-2013 period from South Africa’s Free State, Mashaba _et al._ (2017) find that MODIS-based NDVI values from up to 30 days before anthesis have the highest predictive power (with an R<sup>2</sup> of 0.73) with a 0.1 point change in the NDVI predicted to increase wheat yield by 1.21 t/ha (from a mean of about 2.2 t/ha).

# **2.3 Ukraine’s context and motivation**

With 41.5 million ha of highly fertile land, Ukraine has traditionally been a major source of food grains. Before the war, agriculture contributed about 10% to GDP and 42% of the county’s exports. After decollectivization in the early 2000s, when some 7 mn. land owners were provided with land shares of about 4 ha each, the agricultural sector’s value added doubled. Some 20 mn. ha of Ukraine’s agricultural land is farmed by large farms, often with links to foreign capital (Deininger _et al._ 2018); 12 mn. ha is cultivated by small and household farms; and some 9.2 mn. ha was originally under state or communal land, a sector the size of which has been reduced significantly via disposal of land. was in non-transparent ways.

The war disrupted implementation of far-reaching structural reforms relating to agricultural land in Ukraine. To increase investment, diversification and climate resilience by making property rights more secure,<sup>6</sup>

> 4 As wheat accounts for more than 90% of winter cereals, we use winter wheat and winter cereals interchangeably.

> 5 Noting that the