rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Denmark_

Denmark is one of the countries with a strong government initiative for electronic invoice. It has made electronic invoice compulsory in B2G since February 2005 so that all public-sector institutions in the country accept only e-invoices from business suppliers. Small vendors without the capacity to issue e-invoice can send paper invoices to the “read-in bureau,” which transforms paper invoices into electronic invoices and transfers them to public institutions without charging a service fee. Business suppliers can also use private invoice portals that provide invoicing service or use their own accounting or invoicing system to transmit invoices directly to the public institutions (Agency for Modernisation Ministry of Finance). Approximately 15 million transactions are now invoiced electronically, giving the government and the public sector savings of € 100 million in three years (Gartner, 2009). According to the Eurostat, 39 percent of enterprises with at least 10 employees in Denmark sent or received e-invoices in 2010.<sup>22</sup>

# _France_

Three methods of ensuring the authenticity and integrity of e-invoices are allowed. The first is any technical solutions establishing a reliable audit trail between the invoice issued or received and the underlying goods or services, and the second a procedure defined as an advanced electronic signature. The third is a form of a structured message in a standard agreed between the parties (Schmandt & EngelFlechsig, 2013). According to the Eurostat, 36 percent of enterprises with at least 10 employees in France sent or received e-invoices in 2010.<sup>23</sup>

# _Germany_

All transmission methods including an e-mail, a PDF file, a text file, a computerized facsimile, and a facsimile server have been allowed as a valid e-invoice format subject to the authenticity of the origin and the integrity of the invoice contents since new rules on e-invoice took effect on July 1, 2011, in compliance with the EU VAT Directive (KPMG, Germany: VAT essentials). A reliable audit trail ensuring a link between an invoice and the underlying performance may also be established through a manual matching between
9

# **4 Poverty Simulation Methodology**

# **4.1 Basic Approach**

This section briefly sketches the approach used in the main part of the analysis. A fuller treatment is given in Appendix 1 of Datt and Walker (2002) and in Datt et al. (2003). The general approach is to simulate changes in consumption by applying annual GDP per capita growth rates to unit-record household survey data. For this application, consumption is defined in per adult equivalent terms. Allowance can be made for varying population and GDP growth rates at the sectoral/regional level. The simplest version assumes that growth is distribution neutral, i.e. that inequality is unchanged. Alternatively, we can incorporate estimates of changes in inequality levels in the simulation.

The discussion presented here is done going forwards from the 1991/92 survey data. It is also possible to project the simulations backwards from the 2000/01 survey. Except for one case included in the appendix, the simulations conducted for this paper are done going forwards.

# _Notation_

Define the following terms for a sample of _n_ households:

- <sup>_c_</sup> _i_ , _t_<sup>is per adult equivalent consumption for sample household</sup><sup>_i_in year</sup><sup>_t_, where</sup><sup>_t=0_</sup> represents the survey year

- <sup>isthe individual weight for household</sup><sup>_i_in year</sup><sup>_t._These weights are the product of the</sup>

- <sup>_w_</sup> _i_ , _t_ household sampling weights (the inverse of the probability of selection of the household in the survey) and household size.

- the sector (or, alternatively, region) of household _i_ in the survey year is given by<sup>_s_</sup> _i_ , _t_<sup>_._This is<
living in slightly different localities in terms of the local density of forcibly displaced persons and looks for differences in their perceptions regarding service delivery.

Our evidence on perceptions about service provision is limited by data. We find that in both Lebanon and the Amman governorate sample in Jordan, a sizeable share of host community children attends private school (which is arguably unaffordable for many refugee children), and we find no relationship between changes in the perceptions of education quality and local Syrian refugee prevalence. In KRI – where almost all host community children attend public school – residents of districts with a higher local influx of the forcibly displaced are more likely to report that education quality has worsened.

These findings should not be interpreted as evidence of absence of strain on public service systems. Rather, local competition and dissatisfaction among the host community concerning refugees is mitigated by hosts’ use of alternate systems and refugees’ limited usage of facilities and services due to a range of constraints. Across the three territories, enrollment rates of school-age (ages 5-15 years) refugee children are low, ranging from two-thirds in the Jordan sample to slightly more than half in Lebanon. The most prominent reason is not lack of access to public schools but economic hardship, either because children need to work to support the family, or because the expenses associated with going to school are unaffordable. Similarly, out-of-pocket expenditures for nonpreventive health care can be significant, and are associated with risky coping strategies.

The remainder of the paper is structured as follows. Section 2 describes the data and methodology used to measure the impact of the Syrian refugee influx on the delivery of public services and public perceptions. Sections 3, 4, and 5 present the results for infrastructure, health, and education services, respectively. Finally, section 6 concludes.

# **2. Data and Methodology**

This report uses data from 2015-2016 household- and individual-level surveys. The principal data source is the Syrian Refugee and Host Community Surveys (SRHCS), implemented in Jordan, Lebanon, and
10 in this latter role have been the other major aspect of political economy failure. Here the key is not so much technical deficiencies - though these are often present - but enforcement in the face of political obstacles. Weakness in this dimension has been widely identified as a contributory problem in East Asia. Certainly, comparative assessments gave the affected Asian countries relatively low scores on the quality of the regulatory environment prior to the crisis (Caprio, 1998), such as relatively weak definitions of capital, easy loan classification and provisioning standards, low liquidity requirements and little foreign penetration of the banking sector. Furthermore, there is a fairly clear correlation between the severity of recent banking crises and expert assessments of the policy enviromnent on dimensions such as legal protection of property rights, corruption and law enforcement. Developing countries tend to score low on these, as do the East Asia "miracle" economies, at least on the dimension of corruption (Figures 3, 4).

Enforcement of prudential regulation is especially likely to be complicated by political considerations where there is concentration of ownership and control of firns, as this typically entails concentration of political power. (In addition, financial distress can propagate through ownership links to affect the economy to an extent unfamiliar in larger developed economies.) As shown by a recent detailed study of over 3000 quoted firms in nine Asian countries found that in Hong Kong, Indonesia, Korea, Malaysia, and Thailand, individual farnilies have control over the majority of corporations, with the use of pyramid structures, cross-shareholdings and other devices enhancing the control of even modest block shareholdings (Claessens, Djankov, Fan and Lang, 1999, see also LaPorta, Lopez de Silanes, and Shleifer, 1998). There are not a few other countries
.28|0.13|0.03|0.04|0.10|
|Utility and financial waivers|0.07|0.07|0.19|0.27|0.16|
|Unemployment/out-of-work<br>income support|0.00|0.01|0.11|0.38|0.15|
|Job protection measures<br>_of which_|0.00|0.07|0.59|1.92|0.79|
|Social insurance contributions|0.00|0.01|0.21|0.21|0.13|
|waivers||||||
|Wage subsidies|0.00|0.05|0.35|1.55|0.60|
|Numberofcountries|19|40|38|45|142|

_Note_ : see table 1 for the classification of social protection into policy focus areas (income protection and job protection). UCT = Unconditional Cash Transfers. _Source_ : Social protection budget data is from the authors’ dataset. Stimulus budget and health response data from IMF (2021).

32
the household welfare indicators have decreased on average between 2006 and 2010. This trend shows that living conditions have deteriorated in these three provinces of Burundi during that period. Meanwhile, household size increased during the same period. Table 1 suggests that attriting households purchased more, had fewer stocks, were smaller and were more affected by the conflict. Attrition is discussed in more detail at the end of this section.

The dummies _Ri_<sup>_FNL_</sup> and _R_<sup>_CNDD_</sup> _i_<sup>+</sup> are equal to one for _returnee_ households. This category includes all households that declared having one member with ties to the factions, whether or not they benefited from DDR allowances.<sup>16</sup> The dummies _Di_<sup>_FNL_</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+</sup> refer to demobilized households, that is, _returnee_ households with a member who benefited from the reinsertion grants. We constructed these dummies according to three definitions, provided in table 2 along with the corresponding number of excombatants falling into that category. In the first definition, demobilized ex-combatants are those who declared ties to a faction and were registered in the official demobilization registers ( _Di_<sup>_FNL_1</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+1</sup> ). In order to minimize misreporting, the second definition adds house-

> 15Conversion factors used are the following: cattle (0.50), sheep and goats (0.10), pigs (0.20), poultry and rabbits (0.01) (Harvest Choice, 2011).

> 16 _Returnees_ include _demobilized_ households, but also “gardien de la paix”, “adultes associés”, “militant combatant”, and people without any status but that declared themselves as members of a rebel faction.

12
br>(0.15)||
|Distance-discounted commercial bank branches (global; t-1)|||3.36***<br>(0.72)|
|Observations|2,000|2,021|2,023|
|R-squared|0.99|0.99|0.99|
|Kleibergen-Paap rk LM statistic|24.47***|25.67***|20.89***|
|Cragg-Donald Wald F statistic|29.29|35.16|22.02|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). The data are for the period 2004-18. Commercial bank branches are per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See sections 2 and 3 for detailed data descriptions and empirical methods. Data are between 2004 and 2018.

23
**Table 3.2: Characteristics Included in Bias Adjustment**

|**Characteristics**|**Ethiopia**|**Malawi**|**Nigeria**|**Uganda**|
|---|---|---|---|---|
|Residence(Urban/Rural)|X||X||
|Age of the household Head(age and age squared)|X|||X|
|Gender of the household head|X|X|X||
|Household Size|X|X|X|X|
|Marital Status(Married=Yes)|X||||
|Literacyof the household head||X|X||
|Language spoken byhousehold head||X|||
|Education of the household head|X||X|X|
|Highestyears of education amonghousehold members||X|||
|Consumptionquintile(Q1-Q5)|X||X|X|
|Wealthquintile(Q1-Q5)||X|||
|Food consumption score||X|||
|Household received remittance(domestic or international)|X||||
|Household received cash transfer fromgovernment||X|||
|Anyhousehold member has a financial account||X|||
|Anyhousehold member had access to credit||||X|
|Household owns dwelling|X||||
|Number ofpeopleper room|X||||
|Employment status of the household head|X||||
|Household head engaged in agriculture|X||||
|Household head engaged in casual labor|X||||
|Sector of household head employment||||X|
|Share of household members engaged in wage employment||X|||
|Share of household members engaged in informal (ganyu)<br>employment||X|||
|Copingstrategies index
have fostered domestic reforms in EMDEs and generated momentum for greater liberalization and expansion of trade opportunities (Baccini and Urpelainen 2014a, 2014b; Baldwin and Jaimovich 2010).

A multitude of costs are imposed on trade by administrative border and customs procedures. Documentation and other customs compliance requirements, lengthy administrative procedures, and other delays have been estimated to increase transaction costs by 2-24 percent of the value of traded goods. In some countries, government revenue losses from inefficient border procedures may exceed 5 percent of GDP (Moïsé and Le Bris 2013).

The WTO Agreement on Trade Facilitation (WTO TFA), adopted in 2014 and ratified by more than 90 percent of WTO members, provides a framework to streamline inefficient control and clearance procedures of border authorities, reduce unnecessary border formalities, and cut opaque administrative costs. 72 percent of the commitments made under the agreement have been implemented to date, but progress has been uneven, with less than 40 percent of commitments implemented in low-income countries. In West Africa, an initiative is underway to cut trade costs by electronically sharing customs transit data (World Bank 2021d). Guatemala and Honduras have reduced the time taken by traders to cross the border from 10 hours to 7 minutes by integrating
women’s access to finance. The influence of gender norms on intra-household dynamics such as access and control of income and expenditure may also play a role. As a result, women may exhibit – voluntarily or involuntarily – a lower demand for financial services than men (Johnson, 2004). Research in rural Paraguay shows that women are more knowledgeable about financial institutions and loan requirements when they control a larger share of family assets and when their husbands do not oppose them taking out loans (Fletschner and Mesbah, 2011).

While a growing literature examines household finance and especially households’ borrowing and savings decisions,<sup>2</sup> systematic data on household and individual use of financial services remains limited. The absence of such data contributes to the scarcity of empirical literature investigating the link between access to finance and gender at the individual level. One exception is the paper by Aterido, Beck, and Iacovone (2011) that uses individual-level survey data from FinMark Trust (Finscope) for nine countries in Sub-Saharan Africa to examine the gender gap in financial services. They find that the lower use of formal financial services by women in nine Sub-Saharan Africa countries can be explained by gender differences in education and income levels, formal employment, and being the head of household. The evidence on a gender gap in the use of informal financial services is more mixed; the study finds that while in some countries women are more likely to use informal services, the reverse is true in other countries. One shortcoming of the FinMark data, however, is that surveys were carried out over a span of six years, 2004 to 2009, and may not be appropriate for cross-country comparisons.

Much of the literature on the gender gap in access to financial services has focused on access to credit in the context of financing entrepreneurial activities, rather than on household

> 2 For a detailed literature review, see Beck, et al. (2008a) and references therein. Campbell (2006) also provides an overview of the household finance
Granger, C. (1981): “Some Properties of Time Series Data and their use in Econometric Model Specification,” _Journal of Econometrics_ , 16, 121–130.

- Ismet, M., A. P. Barkley, and R. V. Llewelyn (1998): “Government intervention and market integration in Indonesian rice markets,” _Agricultural Economics_ , 19(3), 283–295.

- Johansen, S. (1988): “Statistical analysis of cointegration vectors,” _Journal of Economic Dynamics and Control_ , 12(3), 231–254.

- Rapsomanikis, G., D. Hallam, and P. Conforti (2004): _Market Integration and price transmission in selected food and cash crop markets of developing countries: review and applications_ . Chap. 8 in Commodity Market Review 2003-2004, FAO (ed), pp. 187–215. FAO, Rome, Italy.

- Rashid, S. (2004): “Spatial Integration of Maize Markets in Post-liberalised Uganda,” _Journal of African Economies_ , 13(1), 102–133.

- Ravallion, M. (1986): “Testing Market Integration,” _American Journal of Agricultural Economics_ , 68(1), 102–109.

- Van Campenhout, B. (2007): “Modelling trends in food market integration: Method and an application to Tanzanian maize markets,” _Food Policy_ , 32(1), 112–127.

23
attitudes and prevalence were significantly different between rural and urban areas.

As one would expect given the literature on the prevalence and causes of IPV worldwide, Figure 2b illustrates that the acceptability of IPV was slightly lower in urban areas than in rural areas, with the difference being between 6 and 9 percentage points lower in urban areas. But as discussed in the background section and contrary to expectations, the reported prevalence of IPV was _higher_ in urban areas than in rural areas for nine out of the 10 types of violence experienced in the past year. Although the confidence intervals of the estimates often overlap and only one of the differences is statistically different at conventional significance levels when including individual-level control variables (e.g., education, etc.), the rural-urban differences in reported IPV incidents are far different than the clear patterns presented in the acceptability of IPV in Figure 2b.<sup>25</sup> However, it is difficult to interpret whether these are actual differences in the prevalence of IPV or if they might represent differences between urban and rural areas in the willingness of victims to report the violence they experience.

# **Section 5a. Empirical Strategy**

As described in the Introduction, we estimate how men’s attitudes about IPV and how the prevalence of IPV incidents reported by women changed following the 2018 earthquake in PNG, which immediately preceded the beginning of the fourth phase of the 2016-2018 DHS. Specifically, we estimate the following difference-in-differences specification: where _Post_ denotes an indicator equaling one if the individual was interviewed after the earthquake in the fourth phase of the survey; _Affected_ denotes an indicator equaling one if the household lived in the Southern Highlands Province, which was the region that was most strongly affected by the earthquake that was able to be included in the post-earthquake phase of the survey;<sup>26</sup> _X_ denotes individual-level control variables; and _t_ denotes month-year, _r_ denotes the four broad regions in the country, and i denotes individuals.<sup>27<
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 3 Access to major urban centers is another aspect of the urban-rural gradient that captures important determinants of economic opportunities and constraints. Areas within easy commuting radius of a major urban center can for many purposes be considered urban, even if occupied by farmsteads. The edge of this commuting radius may constitute a meaningful break-point or discontinuity on the urban-rural divide, though it is not sharply delineated. At slightly larger radii, farm and nonfarm households may still enjoy some urban employment opportunities, and can profitably grow fruits and vegetables for urban markets. Perhaps there is another meaningful breakpoint, though even less sharply defined, at the von Thünen distance associated with truck farming for urban consumption. As remoteness increases to market increases still further, von Thünen teaches us that activities will systematically change<sup>1</sup> . Households will either move to production of goods with low relative transport costs, or shift towards subsistence production. There will also be a strong though not universal tendency for population density to decline as remoteness increases.

Figure 1, taken from Chomitz and Nelson (2003), illustrates the rural urban gradient in the context of Nicaragua. Based on municipio-level data it shows the strong correlation between travel time to Managua, incidence of severe rural poverty, and low population density (all measured in logs). The tightness and smoothness of the relation is remarkable. However, the graphs exclude the six municipios of Managua Department, which show a sharply lower poverty rate than would be predicted by travel time alone.

Areas towards the rural end of the density and remoteness gradient are by no means homogeneous. Rural development policies may be profoundly influenced by two important environmental factors: agroclimatic suitability for cropping, and current forest cover. These factors are important determinants of the prospects for agriculturally-led rural development, and of the potential effect of development on environmental externalities. Both factors are best viewed as continuous variables.

In summary, we propose to define urban-rural gradients along two dimensions: population density, and
.050|
|Community leader completed sec. school or above|0.252|0.384|-0.201|0.383|0.335|0.071|
|Community is connected to public electr. grid|0.898|0.887|0.024|0.944|0.908|0.097|
|# comm. members with rights to comm. land|150.456|178.026|-0.084|165.906|185.280|-0.052|
|# comm. members without rights to comm. land|209.568|168.861|0.049|298.606|202.294|0.077|
|1st/2nd livelihood: income gen. agric|0.471|0.510|-0.055|0.526|0.431|0.135|
|1st/2nd livelihood: subsistence agric|0.456|0.430|0.037|0.408|0.500|-0.131|
|1st/2nd livelihood: income gen. livestock rearing|0.471|0.523|-0.074|0.477|0.413|0.092|
|1st/2nd livelihood: labor in husbandry/forestry|0.199|0.132|0.127|0.157|0.138|0.038|
|Daily wage (pesos)|140.413|141.464|-0.015|138.868|131.367|0.116|
|PES payment per capita|6685.073|5365.926|0.118|5591.061|6030.615|-0.045|
|Obs|206|151|.|287|218|.|

> 1 The table shows means for enrolled and non-enrolled communities and normalized in means.

>