rafmacalaba/data-use-mentions-extended huggingface dataset:
and returnees). These results hold when enabling correlation between topics and adding time and location effects, as shown using mixed effects models. Furthermore, using more granular data on return and displacement flows we show that increased return is associated with economy topics and negatively associated with violence topics, while increased displacement is positively associated with violence topics. Finally, we use causal mediation analysis to show that in months with lower violence levels increased discussion of violent events mediates the relationship between violence and return.

In summary, our paper makes the following contributions: First, it shows the benefits of social media data, including images and messages, for fleshing out motives and discussions around displacement and return. Most research to date has been done using surveys and on the ground interviews which are costly and not always possible. By combining survey and other data with social media data, future research can leverage the precision of the former with the ease of remote access of the latter (Singh et al. (2019)). Second, we combine text analysis with image analysis to see to what degree images dovetail with the discourse and what additional information we can extract from this medium.

The rest of the paper is organized as follows: First, we discuss the context for our project including the situation in Syria, reasons for displacement and ongoing violence, and relevant literature on displacement, attitudes around return, and social media use. Next, we outline our research design and data collection, including procedures for choosing groups as well as text and image analysis methods. We then present our results and discussion, outlining our findings on return and internal displacement from topic models, images, mixed effects, and causal mediation analysis. We conclude with program and policy implications and suggestions for next steps.

# **2 The Syrian Context**

Since the start of the Syrian civil war in March 2011, Syria has seen unprecedented levels of internal displacement, movement of refugees, and civilian casualties. The war began as a protest movement in 2011 to
69% vs. 86%).</mark> However, deriving any definitive conclusion on inequalities across disability status remains problematic in this literature that uses varying measures for disability, well-being indicators, data sources, and methodologies. Some studies measure disability through broad activity limitations (e.g., Mitra, 2008) or impairments <mark>(Pinilla-Roncancio & Alkire 2020) while more and more use the WG recommended questions (Mitra 2018, Pinilla-Roncancio et al 2020).</mark> Studies using impairments <mark>(PinillaRoncancio & Alkire 2020)</mark> have results on the disability gap in outcomes that are less consistent across countries than those using the WG questions (Mitra 2018; <mark>Pinilla-Roncancio et al 2020)</mark> .

Several studies (Mitra et al 2013; WHO-World Bank 2011) rely on the World Health Survey (WHS) that was designed to collect a detailed health and disability profile of individuals but provide only summary measures of economic well-being, for instance on employment and household expenditures. Besides, not every individual in a household in the WHS was interviewed, only one individual per household. Hence, differences across disability status may be underestimated for household well-being indicators. Finally, and more importantly, results vary across well-being dimensions, making the evidence mixed. Studies relying on traditional poverty measures based on consumption expenditures or asset ownership data do not find any consistent significant association between disability and poverty, while studies on multidimensional poverty measures tend to (United Nations 2019). This is surprising given the consistent evidence found in HICs, whatever the measure of poverty (United Nations 2019).

More research is therefore needed on inequalities that might be associated with disabilities for LMICs using internationally comparable disability questions. The aim of this paper is to contribute to filling this gap. The rest of the paper is organized as follows: Section 2 presents the 3
# **A1.  Basic Framework**

This section sets out a simple framework used by the development accounting literature to measure human capital and uses it to motivate the Human Capital Index (HCI).<sup>2</sup> This literature begins from the observation that the productivity of an individual worker is higher the more educated she is and the healthier she is. This gain in productivity represents the contributions of health and education to her human capital.

Let s� represent the years of school of an individual worker i , and let z� be a measure of her health. The human capital of a worker is: Section A2 discusses how years of school s� are measured and adjusted for differences in quality as reflected in performance on international student achievement tests. Section A3 discusses the mapping from unobserved “latent” health z� to observable health indicators. The parameters φ and γ represent the “returns” to an additional unit of education and health. For example, when education is measured as years of school, this formulation implies that an additional year of school raises the human capital of the worker by 100 �φ percent. As detailed in Sections A2 and A3, plausible values for φ and γ can be drawn from the large microeconometric literature that has estimated returns to education and health using individual‐level data.

The expected future human capital of a child born today is:

> 2 Klenow and Rodriguez‐Clare (1997) and Hall and Jones (1999) are early examples of the development accounting approach, and Caselli (2005) and Hsieh and Klenow (2012) provide surveys. See also Caselli (2014) for an application of this methodology to Latin America, commissioned by the LAC region of the World Bank. The discussion of the contribution of health to human capital draws heavily on Weil (2007) and Ashraf, Lester and Weil (2009).  Galasso and Wagstaff (2016) use the development accounting approach to assess the macroeconomic costs of stunting.

21
sup>_exp_</sup> is the price gap for net energy exporting countries, _P_<sup>_hub_</sup> is the price of a unit of energy at the nearest international hub, and _P_<sup>_dom_</sup> is the retail pump price of a unit of energy in the domestic market.

With regard to the relationship between fossil fuel subsidies and public goods provision variables we examine the relationship between diesel- and premium gasoline subsidies and the public goods provisions as expressed by _central government public health expenditures as a percentage of GDP_ .<sup>14</sup> Figures 1 – 4 show relationships between central government health expenditures as average share of GDP for the 1991 – 2005 period, and subsidies to diesel and premium gasoline in 2006 – 2009 respectively, separately for democratic and non-democratic governments. The democratic/non-democratic distinction is identified by two variables related to “legislative and executive political competition” in the DPI dataset (see Appendix B). Both, Legislative Index of Electoral Competition ( _LIEC_ ) and the Executive Index of Electoral Competition ( _EIEC_ ), range from 1 (no legislature, no elections) to 7 (elections with largest party or executive gets less than 75 % of the votes). A country is considered democratic if both LIEC and EIEC take the value 7; otherwise it is considered a non-democracy.

> 12 The difference between periods for dependent and independent variables comes from cross-section estimates that are not reported here for reasons of space.

> 13 We also calculated subsidies with the method as used by Coady et.al (2010) who uses the international hub dollar prices as the benchmark and found hardly differences in the ranking of countries with regard to their subsidy rate as reported in Figures 1 – 4 below. We prefer the benchmark price as described in the main text to escape the limited availability of the relevant hub prices. 14 In many low-income countries regular gasoline is consumed more than premium gasoline. However, the number of observations for regular gasoline
correlation coefficients between the three variables<sup>13</sup> . However, the relation between bank, borrower and project performance is the core behind this investigation and as Figure 2 shows through a three-dimensional scatterplot, there is a remarkable dispersion and variability of projects ratings between these variables, which encourages this research.

In addition to these, we also introduce another available project specific control: the meannormalized size of the project in terms of million dollar value<sup>14</sup> . In addition to this specific control, other country-wide variables are included as well in ( ): GDP per capita (constant 2005 international dollars), from the World Development Indicators (World Bank 2010); the growth rate of income per capita (World Bank 2010) between the years of the project; a measure of volatility, as the standard deviation of GDP per capita (World Bank 2010) during the years of the project, in order to catch the relation between a volatile economy and project performance (Guillamont and Laajaj 2006); an infrastructure index, based on Calderon, Moral-Benito and Serven (2011)<sup>15</sup> ; drawing from the previous literature, democratic accountability (reported from now on as accountability) and government stability (reported as stability); some variables that account for the quality of national administrative institutions, bureaucracy and corruption; last but not least that ethnic tensions (reported as ethnic) and external conflict (reported as ext. conflict) as a solid literature (Easterly and Levine, 1997) proofed their detrimental impact on growth.

These last six institutional variables<sup>16</sup> come from the International Country Risk Guide (Political Risk Services 2010), all of them are measures of risk and are constructed through an inverse index: therefore a low measure of “corruption” means a high risk of corruption and viceversa; therefore zero is the highest corruption risk, while six is the lowest.

> 13 To clarify this refer to Figure
non-response are specific solutions for specific problems. A more general solution to the more general problem of missing top income recipients (whether due to sparsity, measurement error, non-random unit non-response etc.) is to replace the incomes at the top of the distribution with values drawn from a specific distribution. For example, if top incomes are assumed to have a Pareto distribution it is possible to estimate inequality measures for the top X% of observations assuming the incomes are Pareto distributed, where the Pareto parameter is estimated using the survey data above the Xth percentile. An overall measure of inequality, like the Gini coefficient or the share of the top 1%, is then estimated from the top X percentiles and the bottom (100-X) percentiles.

Jenkins (2017) argues that this method of replacement of top incomes does not work very well in the UK for solving under-capturing of top incomes, by comparing it to methods using tax data. Hlasny and Verme (2018) use this method to estimate income inequality in Egypt. In their preferred results Hlasny and Verme (2018) replace the top 10% of the distribution with a Pareto distribution estimated using 16
underestimating the drop in poverty in the capital. This reflects the particular distributional incidence of growth (in the survey data) which is illustrated by the growth incidence curves. Our modified “two survey” method provides a close match between the simulation and the final year survey data, irrespective of the particular distributional incidence of growth. Consequently, we take the two versions of the analysis using this method (Figures 12 and 13) as our preferred simulated poverty trajectories.

We also presented some supporting evidence that suggests it is very likely that poverty declined since 1994. The alternative hypothesis—that poverty has _not_ declined—would require that inequality dropped significantly from 1992 to 1994.

It is important to recognize that the precision and accuracy of the presented results is only as good as the underlying data. Uncertainty is associated with the macro data, the micro data, and the assumptions that go into the simulation analysis. Only the sampling error arising from the household survey can be readily quantified, and the standard errors on the survey-based estimates provide an extreme lower bound on the standard errors on the simulated poverty rates. This suggests that the year-to-year pattern of changes in poverty may differ substantially from what is implied by the simulations. Nonetheless, the analysis presented here provides the best estimates of poverty rate trajectories in Tanzania with available data.

In terms of the Millennium Development Goal of cutting poverty in half by 2015, growth at the end of the last decade has put Tanzania roughly on the path necessary to meet the goal for urban areas. This is despite the fact that the poverty-reducing impact of growth has been partly offset by increasing inequality, particularly within Dar es Salaam. Rural areas, where the large majority of Tanzanians live, are lagging behind their MDG target.

Reaching the MDG goal for poverty reduction by 2015 will require sustaining and improving upon the rural growth rates achieved in recent years. Given that only a tiny minority of the population lives in the capital, even rapid urban growth will have only a small impact on poverty in
in Latin America by exploiting an unusual rich survey carried out immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). In particular, by codifying the occupation variables in these surveys we are able to construct a variable for potential for work from home (WFH) and analyze the role of this factor in people’s adjustments to the shock.

Our analysis, mainly based on fixed-effects models of job losses and other labor outcomes, leads to some interesting results. First, we confirm that the impact of the COVID19 shock was (i) harder for women and (ii) strongly decreasing in the ability of doing work from home. More important, we find that the mitigating effect of WFH on the severity of the impact was especially relevant for women with children. These effects were larger in those countries/periods in which the containment measures implemented by governments against the spread of the disease were more stringent. Our results are consistent with a plausible mechanism: women with children were more likely to stay home due to school closures and the traditional intrahousehold distribution of childcare responsibilities, and thus the possibility of WFH was crucial for them to keep their jobs.

Our results provide evidence consistent with a relevant policy lesson highlighted in 17
views the family network and the party network as two distinct types of social capital, the positive effects of both types tend to operate in lagging regions and in rural areas. They differ in one crucial aspect, however, in that the positive effect of the family network tends to benefit poor households, while the positive effect of the more organized and modern party network tends to benefit non-poor households.

The rest of the paper is organized as follows. Section 2 describes our data set in more detail and the variables that we construct from it, in particular those that we use to measure social capital. Section 3 provides summary statistics describing rural-urban consumption patterns and how usage of formal and informal finance varies with household characteristics. Section 4 lays out our approach to estimation and hypotheses for the explanatory variables used in our regressions. In section 5 we discuss our empirical results, while section 6 offers conclusions.

# **2. Data and Variables**

# **a. Data Set**

We rely on data from the Chinese Household Finance Survey (CHFS) of 2013, which was collected by Chinese Household Finance Research Center of Southwestern University of Finance and Economics. The data are representative of Chinese households due to their stratified random sampling design in three stages: first, random sampling of counties; then communities within a county; then random sampling from a community.<sup>9</sup> When choosing counties, all of the counties in China were first ranked according to GDP and classified into groups. Within each group a random sample of counties was then selected. In the end, a total of 262 counties, roughly 10% of all Chinese counties, were selected to be part of the sample. Within each county, four communities were chosen. Then within a community, 25-50 households were chosen for urban areas, and 20 households for rural areas. The final CHFS sample consists of 28,100 households and 98,000 individuals in 1,029 communities, 262 counties, and 29 provinces
country in recent years.

**Evidence from other countries shows that natural and human hazards disproportionately affect poor people (Hallegatte, 2020), further fostering the case for studying vulnerability to poverty in these settings.** Poor people are often more exposed to natural hazards, more vulnerable to them (when exposed, they lose a more significant fraction of their assets), and have a lower socioeconomic resilience (a lower ability to cope with its impacts). This can create a vicious circle between poverty and disasters (Hallegatte, 2020). Therefore, disaster risk management can be part of a poverty reduction strategy and vice versa. Similarly, human hazards and poverty are intertwined. To give an example, crime may induce poverty traps under low levels of modernization and high levels of production (Mehlum et al., 2005). Poverty, on the other hand, can trigger crime (McCrea, 2019). Based on this evidence, it is crucial to understand the relationship between both and design adequate policies to address these.

> 8 Analyzing three waves of data from Nigeria, for example, shows that households are unlikely to remain in poverty over time. Only 7 percent of households were always below the poverty line while 35 percent were never below it. The rest of households reported a switching poverty status (Skoufias and Baez, 2021).

> 9 Further examples are studies by Chaudhuri et al. (2002), Chaudhuri (2003), Azam and Imai (2010), Imai et al. (2011), Dutta et al. (2011), Barrientos (2013), Klasen and Waibel (2015), Ward (2016).

6
VHLSS, using panel data from two consecutive surveys.</mark><sup>16</sup> <mark>This approach aims to measure the 2-year lagged effect of childcare attendance on parental education. We use the same model specification as the model of current effects.</mark>

> 16 Fifty percent <mark>of households sampled in one VHLSS are re-interviewed in the next one. For example, 50% of households in the 2010 VHLSS are resampled in the 2012 VHLSS. Then 50% of the households covered in the 2010-2012 VHLSS panel are resampled in the 2014 VHLSS. In addition, 25% of households in the 2012 VHLSS are also resampled in the 2014 VHLSS. Thus, in two consecutive surveys, around 50% of households feature in panel data, and around 25% of households are included in panel data in three consecutive VHLSS. In practice, the dropout or attrition rate in two consecutive surveys is around 8%.</mark>

16
, _Doing Business_ presents data in terms of two main aggregate measures: the _Doing Business_ score and the ease of doing business ranking, which is based on the Doing Business score. The ease of doing business ranking compares economies with one another; the Doing Business score benchmarks economies with respect to regulatory best practice, showing the absolute distance to the best performance on each _Doing Business_ indicator. When compared across years, the _Doing Business_ score shows how much the regulatory environment for local entrepreneurs in an economy has changed over time in absolute terms, while the ease of doing business ranking can show only how much the regulatory environment has changed relative to that in other economies. Therefore, for the purpose of this paper, we mostly use the _Doing Business_ score as one of the main outcome variables. In the historical data set the minimum _Doing Business_ score is 19.98 percentage points (the country is far removed from the perfect score of a100), while the highest _Doing Business_ score is 90.41 (see Table 1 in the Annex).

> 8 Average of the overall grades of the level of press freedom by year from 2004 to 2016. Source: Freedom House.

> 9 In general terms, a reform is defined as a legal or regulatory change that makes it easier for local firms to do business. Reforms can have different impact magnitudes and _Doing Business_ counts only one reform per topic per country in any given year.

15
0.044|2370|
|**Poland**|2.228|1-May-04|1.809|0.000|2548|
|**Romania**|1.97|1-Jan-07|1.775|0.001|2514|
|**Slovakia**|2.181|1-May-04|1.925|0.000|2780|
|**Slovenia**|2.122|1-May-04|2.265|0.017|2359|

Hanousek and Palda (2008) explore tax evasion using survey data (2000, 2002, 2004, and 2006) from the Czech Republic to measure its development for the years 1995 to 2006. Their results indicate that the number and percentage of evaders increased until the early millennium and started to decrease. The authors called the inverse-U shape “an evasional Kuznets curve” (p. 3) and suggested this could indicate that hysteresis “may not be a feature of evasion in a transition economy” (p. 3). However, our results on the development of the Czech Republic and Slovakia between 1999 and 2008 actually indicates a decay of tax
<!-- Start of picture text --> Figure 5. Progressivity and Redistributive Effect of Taxes<br>Figure A. Direct Taxes<br>0.70 0.035<br>0.60 0.030<br>0.50 0.025<br>0.40 0.020<br>0.30 0.015<br>0.20 0.010<br>0.10 0.005<br>0.00 0.000<br>Kakwani Marginal contribution<br>Figure B. Indirect Taxes<br>0.10 0.010<br>0.05<br>0.005<br>0.00<br>0.000<br>-0.05<br>-0.005<br>-0.10<br>-0.010<br>-0.15<br>-0.015<br>-0.20<br>-0.25 -0.020<br>Kakwani Marginal contribution<br>Kakwani Index<br>(change in Gini points)<br>Marginal contribution to equity<br>Regressive <------------------> Progressive<br>(change in Gini points)<br>Kakwani Index Marginal contribution to equity<br>Regressive <------------------> Progressive<br><!-- End of picture text --> Source: Source: Armenia: Younger et al (2016); Bolivia: Paz Arauco et al (2014); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2014); Colombia: Melendez (2014); El Salvador: Beneke et al., (2014); Georgia: Cancho & Bondarenko (2016)
**Table 1: Available evidence on the impact of outreach interventions**

||**Access**|**Graduation**|
|---|---|---|
|**Number of studies by type of interventions**|||
|Not specified (Any outreach programme)|1|0|
|Information|8|0|
|Information & support|18|3|
|Information, support & tutoring|3|1|
|**Total number of studies**|**28**|**4**|
|**Studies' characteristics**|||
|RCT design (in % of total studies)|82%|50%|
|Diversity of national contexts (nb of country)|6|2|
|National‐scale interventions (in % of total studies)|25%|25%|
|Single‐institution interventions(in % of total studies)|11%|0%|
|**Source**: Tables B.1, B.2, B.3, B.4, B.5 in Appendix.|||

# _4.1 Impact on access to higher education_

Only one study provides a quasi‐experimental evaluation of outreach programs in general, not limited to one specific intervention. Domina (2009) uses longitudinal data to compare the efficiency of outreach programs and found an increase in enrollment (+5.5 p.p.) in any higher education institution, but this was not statistically significant (Table B.1 in Appendix). Since no information was available on the type of services offered, it is possible that different program designs have very different impacts on college enrollment.

The evaluations of specific outreach interventions indeed suggest a great variety of effects on enrollment, depending on the characteristics of interventions. As shown by figure 2, interventions providing disadvantaged students with _additional information_ only on higher education seem