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

China’s economy has undergone unprecedented growth over the last twenty-five years, and this has facilitated a dramatic reduction in poverty. While average incomes have risen considerably, it is also well documented that not all regions of the country have benefited uniformly (Ravallion and Chen, 2007; Benjamin et al, 2008). Gaps exist between coastal and interior regions and between urban and rural areas of the country (Kanbur and Zhang, 1999; 2005). In addition to differences in income and consumption growth by geography (Jalan and Ravallion, 2002), there are also considerable differences across demographic groups not only in income levels (Chaudhuri and Datt, 2009), but also in the level of coverage by safety nets and in the risks of falling into poverty (Duclos et al, 2010; Jalan and Ravallion, 1999). In particular, the movement of young rural adults to urban and coastal areas for higher income earning opportunities raises the prospect that older residents remaining in rural areas are at greater risk of falling into poverty. In general, rural residents lack access to pension support when they are of “retirement age,” and must rely on either their own labor income or support from family members, even as they age into their 70s and beyond. In rural China, financial support for the elderly remains the responsibility of adult children and is even codified into laws governing the family (Marriage Law, 2001). As the population of potential care providers continues to shrink as a result of both China’s demographic transition and the availability of attractive migrant employment opportunities for the young, many observers have expressed concerns for the well-being of the rural elderly (Benjamin et al, 2000; Jiang and Zhao, 2009; Li et al, 2009; Yao, 2006).

In this paper, we use the 2005 one percent population sample to document the sources of support for older rural residents, and then present updated evidence on the rates both at which the 2
Incidence of COVID-19 and Connections with Air Pollution Exposure in accordance with country-level legislation on public health. The 355 municipalities are grouped into 25 GGD areas, each covering a population of approximately 600,000 inhabitants. The GGD borders are visible in figure 5 which visualizes the hospital admissions.

The data is combined with demographic statistics (2019) obtained from the Dutch Central Bureau of Statistics.<sup>10</sup> The data contains the official population headcount at district level, as well as a number of relevant household characteristics. A number of surveyed health statistics (2016) have been obtained as well from the RIVM (maps can be viewed in the source link).<sup>11</sup> . The data is based on a survey of 457,000 people and includes the share of population in each district with a documented long-term illness (illnesses over 6 months), the prevalence of overweight and obesity, alcohol abuse, smoking and noise due to traffic. Hence the data controls for the presence of possible pre-conditions that make certain populations more vulnerable.

A variety of air pollution data sets exist. For the main analysis, annual average particulate matter concentrations from the RIVM are used to capture long-term exposure (2017, published September 2019).<sup>12</sup> The data is used by the government for official monitoring in accordance with EU guidelines on air quality monitoring and has a resolution of 25 meter grids. These high-resolution grids are produced by spatial interpolation of groundmeasurements. For this analysis, the grids have been averaged to the municipality level. The spatial distribution of pollution has remained relatively stable in recent years. The intensity of air pollution has gradually gone down since 2013, though the difference between the 2017 and 2015 data is relatively small. This suggests that the spatial variation of the 2017 data is still relevant to analyze the role of long-term pollution exposure in the current situation. The temporal lag in the pollution data also ensures that there is no endogeneity due to feedback between
|
|_Technical Inefficiency_|||||||||
|Manager Completed Primary Education°|-0.064|-0.052|18.151***|-0.018|-0.013|-0.009|0.063|0.024|
|Manager Age|-0.008|-0.006|-0.308*|0.018|-0.007|-0.007|†|†|
|Manager Age (squared)|0.000|0.000|0.005**|0.000|0.000|0.000|†|†|
|Manager Received Agricultural Extension Services°|0.205|0.209|3.786*|0.130|0.038|0.061|-0.011|-0.001|
|Dependency Ratio|-0.079|-0.078|-13.581***|-0.057|†|†|†|†|
|Count of Agricultural Assets|-0.031*|-0.034*|-0.490***|-0.044*|-0.012|-0.011|-0.010|-0.006|
|CV of Flowering Season Rainfall (1999-2014)|-0.114|-0.149|†|†|2.344*|1.966|2.970**|2.926**|
|Constant|6.447***|6.335***|-13.098***|5.349***|6.231***|6.399***|5.984***|6.039***|
|_Random Error Term (v)_||||||
(Agwu and Okhimamhe, 2009). Women are also often excluded from decision-making processes inside and outside their homes, including farming decisions and participation in local community organizations and politics (Ogunlela and Mukhtar, 2009; Nagarajan, 2017; UN Women 2020).

The Women, Peace, and Security Index by the Georgetown Institute for Women, Peace and Security ranks countries according to 11 indicators in three different categories, namely inclusion, justice and security. Nigeria as a whole is ranked as number 145 of 167 countries (GIWPS, 2019). A pilot study to produce the index at the sub-national level shows that the extent of these issues also varies significantly within Nigeria, with the three states Adamawa, Borno and Yobe scoring worst.

Demographic and Health Survey data suggest that 30% of women aged 15-49 in North-east Nigeria have experienced physical violence and 35% state that a husband is justified in beating his wife (NPC and ICF, 2014). In addition, a recent World Bank study on Gender Based Violence (GBV) in Nigeria (World Bank, 2019) emphasizes that gender norms in North-east Nigeria are a key risk factor for Intimate Partner Violence (IPV). Such norms include, for example, the presumed social superiority of men, the belief that paying a bride price is equivalent to purchasing a wife, and men’s rights to ‘correct’ their wives’ behavior. In addition, only 23% of female survivors of GBV in Northeast Nigeria seek help.

# **3 Methodology**

# **3.1 Intersectionality approach**

To grasp the complex role of gender, forced displacement and violence for coping behaviors in conflict crises, we take an ‘intersectional’ perspective (Bastia, 2014). Such an approach aims to explore different forms and sources of disadvantage and emphasizes their interconnectedness and interdependencies, rather than approaching them as separate domains (Crenshaw, 1990; Valentine, 2007). Specifically, intersectionality seeks to understand outcomes by exploring the relationships and interactions between different factors and
theoretical framework of the gravity-models see Deardorff (1995) or Anderson and van Wincoop (2003). In terms of our Heckman selection model we estimate the following: and we assumed that X is observed if ijk and u1 and u2 have correlation ρ.

Xijk is the utilization rate of preferences for product k, in other words the percentage of country _i's_ imports of product k from country _j_ which requested preferential access. The data originates from Eurostat and contains import data at 8-digit level for the year 2001.

As a proxy for the trading countries' _income_ and _size_ the GDP (GDPi is the level of income in country _I)_ and population (POPi is the population in country i) of both partner countries are included in the regression. GDP and population data is taken from the World Development Indicators database.

Dij is distance between the partner countries proxying _trading costs_ . Previous empirical results showed that distance had an important negative impact on trade. The further away the trading partners are located from each other the higher the costs will be for transporting the products. Distance is thus also included in our regression and is expected to have a significant and negative impact on preferential trade. The distance data originates from the CEPII distance database and calculated following the great circle formula, which uses latitudes and longitudes of the capital cities.

To investigate the importance of the quality of economic environment in a given exporting ACP country an _indicator of economic freedom_ was used (Freedomindex). The index was obtained 17
**Figure 4: Share of households with an inverter or generator, evolution over time**

How has this trend evolved over time? In this case we draw from ENFT workforce survey which, contrary to the sole ENIGH 2007, presents 6 months frequency and has a revolving panel structure interviewing the same household for 5 consecutive quarters. As observed in the figure, a common trend shared by Dominican families is the tendency towards acquiring more electrical inverters over time. The upward evolution of the trends between 2005 and 2010 is quite similar across socioeconomic quintiles. Interestingly, the poorest households have more propensity to count with an electrical inverter than those in Q2 and Q3. A possible explanation is that the poorer households are located in neighborhoods that suffer blackouts more frequently, resulting in that they are more in need of having alternative generation sources.

It is worth noticing that, even when trends over time are similar, there is a large difference between the share of alternative electricity generation devices hold by the richest quintile respect to the rest of the population. Simple mean tests suggest that Q5 is the only quintile that presents a statistically significant difference in term of share of households that opts for having generators or inverters at home. A binary choice _logit_ regression shows that, at the 0.05 percent level of significance, only belonging to Q4 and Q5 significantly influences the choice of opting for acquiring alternative electricity generation devices. This suggests that the

[9]
to get the subsidy in the future) to producers who obtain a low social return but invest only because they are able to access the subsidy. The net effect on total investment is ambiguous, but the efficiency impact is negative as a consequence of effect (ii).

The above example is not just a curiosity. That subsidies, at least in the form in which they are usually allocated, do not generally promote investment has been shown by several empirical studies in various countries. Empirical studies using detailed firm-level data by Bregman, Fuss, and Regev (1999) for Israel, Fakin (1995) for Poland, Lee (1996) for Korea, Bergstrom (1998) for Sweden, Estache and Gaspar (1995) for Brazil, Harris (1991) for Ireland, and several others have shown that subsidies and corporate tax concessions targeted to specific firms are at best ineffective in promoting investment and technological adoption and, in some instances, even counterproductive. Crowding-out of private investment as a consequence of the subsidies occurs.

(iii) Subsidies may _indirectly_ crowd out private investment in the intermediate run. The third type of crowding-out of private investment as a result of public expenditure biases toward subsidies is long-run in nature. The low stocks of public goods caused by continuous underinvestment in public goods causes low productivity of private investments over the long run. This, in turn, is translated into slower investment and slower productivity growth over the long run (World Bank. 2000).

_The structure of public expenditure and rural poverty_ . Public goods affect rural poverty through _direct_ effects and through _indirect_ effects: (i) Direct effects. Important components of rural public goods are human capital and investment in the protection of natural resources and the environment. Human capital is the main productive asset for most poor. However, as a consequence of credit and other market failures they are not in general able to fully finance investments in human capital, regardless of how high the rate of return to these investments might be (World Bank, 2000)
Distribution. Bilkent University. Available at <u>http://www.bilkent.edu.tr/~yeldane/B&YCEPA2002.PDF</u> Buch, C. and Lipponer, A. (2004) Clustering or competition? The foreign investment behavior of German banks, Deutsche Bundesbank Discussion paper series 1: Studies of the Economic Research Centre No. 06.

Cartensen k., and Taubal, F., 2004. Foreign investment in Central and Eastern European countries: a dynamic panel analysis, Journal of Comparative Economics, Elsevier.

Celasun, O., Denizer, C. and Dong He, 1999. "Capital flows, macroeconomic management, and the financial system - Turkey, 1989-97," Policy Research Working Paper Series 2141, The World Bank.

Demir, F., 2004. “A Failure Story: Politics and Financial Liberalization in Turkey, Revisiting the Revolving Door Hypothesis”, _World Development_ , Elsevier, vol. 32(5), pages 851-869, May.

Dunning, J., 1998. “Location and the Multinational Enterprise: A Neglected Factor?”, Journal of International Business Studies, 29, 1 (First Quarter 1998), pp.45-66 Egger, P. & Winner, H., 2006. "How Corruption Influences Foreign Direct Investment: A Panel Data Study," _Economic Development and Cultural Change_ , University of Chicago Press, vol. 54(2), pages 459-86, January.

Erdilek, A., 2003. “A Comparative Analysis of Inward and Outward FDI in Turkey,” _Transnational Corporations_ 12(3).

Ertuğrul, A. and Selçuk, F. 2001. “A brief account of the Turkish Economy, 1980-2000”. _Russian and East European Finance and Trade_ , Vol. 37, No. 6, Financial Markets, Disinflation Policies, and Economic Restructuring in Turkey (Nov. - Dec., 2001), pp. 6-30.

Esiyok, B., 2011. "Determinants of foreign direct
(before the Doha round), they are imposed largely unilaterally. Agricultural tariff data are therefore appropriate for testing the Grossman-Helpman model.

The subsidy measures are taken from Hoekman, Ng and Olarreaga.<sup>29</sup> Unlike the protection measures, the closest we can get to an ad valorem equivalent is the ratio of the dollar value of the subsidy to the dollar value of exports. Two subsidy measures are available, export subsidies as a proportion of exports, XS, and total subsidies (including production and export subsidies) as a proportion of exports, TS.<sup>30</sup> All protection and subsidy data are for the year 1999.

# _Trade and Output_

The USDA’s Production, Supply and Distribution database PSD Online (at <u>http://www.fas.usda.gov/psd/complete_files/default.asp) is the source for trade and production</u> data. The data are kept at a commodity level that map neatly into the harmonized system (HS) of trade data-keeping at the 4-digit or 5-digit level. Since imports and production are both measured in physical units in the database, they yield precise measures of the inverse import-to-output ( _z_<sup>M</sup> ) and inverse export-to-output ratios ( _z_<sup>X</sup> ).

We use the following criteria to categorize commodities as pure import or pure export goods: if for any commodity _z_<sup>M</sup> >30 (imports small relative to output) and _z_<sup>X</sup> <30 then it is categorized as a pure export good; if for a commodity _z_<sup>X</sup> >30 (exports small relative to output) and _z_<sup>M</sup> <30 then it is categorized as a pure import good. In the data, in sectors where one ratio is greater than the cutoff of 30, the other
the LPA process, and which we discuss later in this report. As a result, all interviewees were male, which limited not only the diversity of perspectives but also the quality of gendered analysis.

To complement the personal perspectives of key-stakeholders, the authors utilized secondary data taken from public databases and published sources. The purpose of the secondary data was to offer a data-based perspective on the context in each location – including security, social relations, and extent of IDP return – before and after each LPA was signed. To do this in a systematic way, the authors selected a set of indicators associated with safe and sustainable IDP return. The choice of indicators was based on two factors: first, we identified indicators which reflected the rights and standards set out in the IASC Framework on Durable Solutions, and second, we excluded indicators that did not have data available throughout the study period (2015-2021). The final set of indicators focused on the number of returnees, security conditions, and social cohesion, as set out in Table 2 below. All indicators were measured at sub-district level, and at regular intervals: as close as possible to LPA signature, and quarterly or bi-annually after signature.<sup>67</sup> The data source for the IDP return indicators was IOM DTM’s _Master Lists_ ,<sup>68</sup> while the security and social cohesion indicators were drawn from two sources: IOM DTM’s _Integrated Location Assessments_ (ILA),<sup>69</sup> as well as IOM DTM’s _Return Index_ .<sup>70</sup>

> 67 Specifically, IDP return indicators were measured at the closest point in time before each LPA was signed, as well as approximately 3, 6, and 12 months after each LPA was signed, while security and social cohesion indicators were measured at the closest point in time before each LPA was signed, as well as approximately 6 months after each LPA was signed (due to the lower frequency
# **Pre-trends**

Given the empirical setting of this exercise we expect the population growth effects to take place after 1994, but not before. A natural test is to see if indeed population growth is independent of the distance to homelands before 1994. Our dataset does not cover any year other than 1991 in the period pre-1994, and so we can’t use it for this purpose. Instead we use the Dysturb dataset (Giraut and Vacchiani-Marcuzzo, 2013), a dataset that maps population in comparable units over time in South Africa. Dysturb provides data at two different levels of aggregation, ‘urban agglomeration’ (UA) and ‘magisterial district’ (MD). We use the UA dataset because unlike the MD the units used here are defined consistently over time. In Table 3 we regress population growth on the distance to homelands variable. In columns 4, 5 and 6 we control for initial population, in the first three columns we do not. In columns 1 and 4 we use all units with non-missing data in 1980 and 1991. In columns 2 and 5 we use all units with non-missing data in 1991 and 2001. In columns 3 and 6 we use all units with non-missing data in 1980, 1991 and 2001 to make sure the difference in coefficients between columns is not driven by sample selection. The table shows that we find the expected negative correlation between distance to the homeland and population growth for 1991-2001, but not for 1980-1991.

22
Western countries” (Buttorff and Welborne 2015, 1).<sup>16</sup> That said, there seems to be some gender differences in the use of _wasta_ depending on the country context. In a study comparing several Arab countries, men and women appeared to use roughly comparable amounts of _wasta_ in Algeria, Morocco, and Lebanon, while men were using considerably more _wasta_ than women in Jordan, Palestine, and Yemen (Buttorff and Welborne 2015).

In Sudan, the use of _wasta_ to shape current institutions can be traced back to colonial times. Mann explains how the British colonial administration used the _wasta_ of Sudan’s sectarian and tribal leaders to develop Sudanese institutions modelled after British institutions. For example, following independence, Gordon Memorial College became the University of Khartoum, and the children of the sectarian and tribal leaders, and their trusted circles, had an easier time securing positions in Gordon Memorial College and then posts in the government. Similarly, Sudan’s post-1983 leaders used the _wasta_ of regional leaders in Islamic banking to Islamize Sudan’s banking system. Those whose personal connections brought them into the fold of these banks, like the youth of the Colonial era who were educated in Gordon Memorial College, became the country’s future leaders in business and politics.

_Wasta_ continues to play an important role in both accessing and rising within the Sudanese labor market. More than half, 56 percent, of the Arab Barometer (2018) respondents believe that it is often impossible to get a job without _wasta_ . According to Mann’s fieldwork in Sudan, _wasta_ plays a greater role among the current generation than it did among previous generations.<sup>17</sup> Mann writes, “[ _Wasta_ ] has become so important in securing livelihoods that young people refer to it as ‘Vitamin Waw’” (Mann 2014, 21). For example, more than 80 percent of Mann’s survey respondents who were between the ages of 20 and 24 learned about
)|(0.107)|(0.108)|
|∆Chinese competition|-0.116|-0.099|-0.067|-0.052|-0.062|
||(0.175)|(0.176)|(0.174)|(0.174)|(0.174)|
|∆Computers-per-worker*∆Chinese competition|1.121**|1.144**|1.034**|1.039**|1.046**|
||(0.475)|(0.472)|(0.484)|(0.480)|(0.480)|
|Controls||||||
|Age|No|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|
|FDI share|No|No|No|No|Yes|
|Observations|701|701|701|701|701|

Robust standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

39
# **2.2 Data**

The main data needed for the study is a social accounting matrix (SAM) for Egypt. We used the SAM for 2019, jointly prepared by Egypt’s national statistical office - the Central Agency for Public Mobilization and Statistics (CAPMAS), and the International Food Policy Research Institute (IFPRI). The SAM and detailed documentation of how the SAM was prepared is publicly available (Serag et al. 2021). We have further worked on the same and mapped the sectors and commodities to those needed for our analysis (see Table 1). For example, detailed agriculture commodities are unnecessary in our analysis as the agriculture sector is relatively less energy and emission intensive. Therefore, we aggregated all crops, fruits and vegetables, fishery, and forestry in one sector: agriculture and forestry. It helps us present our results clearly and focus on the sectors that are energy- and/or emission-intensive. The SAM also provides data by household income group, which allows us to conduct the distributional analysis. The SAM is the latest available for Egypt.

The SAM indicates that commercial services, including tourism services (hotels, restaurants), financial services, real estate, and business services, accounted for highest share of gross output (17%) in 2019 followed by the energy sector (crude oil and natural gas production, petroleum refinery and electricity sector), which accounts for 15% of the gross output. The agriculture sector that includes forestry and fishery accounted for 10% of the gross output. Commercial services also accounted for the highest share (27%) in total export of goods and services in 2019, followed by the transportation goods and services (17%) and chemicals and fertilizers (10%). The rich households (HH Quintile 5) received more than 40% of the total household income in 2019, whereas poor households (HH Quintile 1) received only 4% of the total household income. Taxes accounted for 99% of the total
. In Table 2, column 7, we argued that satisfying the exclusion restriction requires controlling for GDP proxies and climatic data. We therefore added rainfall and nighttime light intensity variables as controls, and averaged these variables over the same time span as for the instrument, 1995-2010. We use 2014 averages instead to measure values of GDP and rainfall that are contemporaneous with the dependent variable. The estimated 2SLS coefficient for internet access (panel B, column 1), though slightly lower, is still significant at the 5 percent level. Next, we investigate whether the documented association between internet and jihadist recruitment could be driven by usage of other media.

> 8Yearly data on lightning strikes with the same level of geographical detail is not available (Manacorda and Tesei, 2020) and lightning strikes are highly serially correlated (Andersen et al., 2012).

13