rafmacalaba/data-use-mentions-extended huggingface dataset:
household surveys (e.g., TFPM 2016). Furthermore, given the large and frequent shocks that could potentially affect welfare and induce displacement (e.g., Favari et al. 2021), high frequency data collection is necessary to differentiate between potential mechanisms affecting displacement.

Given the substantial constraints on traditional data collection and the need for high frequency data, we turn to rare, non-traditional survey data.<sup>7</sup> This paper utilizes the most geographically complete survey covering the Republic of Yemen following the start of the conflict- the mobile Vulnerability and Assessment Mapping Survey (mVAM) conducted by the WFP. The monthly data begin in August 2015, just months after the escalation of violence and the peak in forced displacement. The data provide a rare opportunity to study household decisions during an active conflict, with important implications for governments and humanitarian and development organizations.

The primary purpose of the mVAM is to assess the food security situation in the Republic of Yemen each month. On average, it takes about 20 minutes to complete. The survey is conducted via mobile phone and respondents are reached via random digit dialing (RDD). The survey is stratified by governorate/capital city, where potential respondents continue to be contacted until a sufficient number of completed responses have been met in each governorate (aside from the island of Soccatra).<sup>8</sup> The number of minimum responses per governorate is determined by its share of the total population. The number of respondents surveyed each month is approximately 2400.<sup>9</sup> The survey is a rolling panel, where the call center initially acquires a list of 2400 phone numbers that answered the survey in the initial month. In the second month, the same 2400 phone numbers are contacted for an identical survey. Of these 2400, approximately 400 on average are not reached and have to be replaced for the second survey via random digit dialing, and approximately 200 do not respond for two months
report results, rather than absolute values.<sup>5</sup> Other options that may potentially improve accuracy are: (i) using the official Rosstat household income survey; and (ii) using administrative data for microsimulation (Scandinavian countries do that). A more detailed discussion on RUSMOD validation can be found in Annex 2. Main assumptions and model validation.

Not all the programs could be (fully) simulated. First, some policies are beyond the scope of the model entirely—these include, for example, corporate taxes or in-kind benefits (such as free or subsidized health care, education and other social services).<sup>6</sup> Second, due to lack of information in the survey, a number of programs could not be simulated. For example, the simulation of contributory pension benefits requires information about the contribution record and wages over a person’s lifetime. Other such programs include: a _monthly cash payment_ , _cash and near-cash categorical benefits_ (such as discounts for housing, utilities, transportation or medication), _unemployment benefits_ , and _scholarships_ . Third, some benefits cannot be separated from other payments. For example, a _social supplement to pensions_ is paid together with pensions and recipients are not able to tell them apart. Finally, there are some programs targeted at smaller groups of the population that can neither be simulated due to the lack of information in the survey, nor are available in the data set (for example, payments to families with foster/adopted children). Amounts for these programs are taken directly from the survey data, if available. The main drawback here is that the impact of parametric changes to these programs (for example, an increase in the statutory retirement age) cannot be simulated without strong assumptions.

# III. Performance of the Russian tax-benefit system

## **Baseline scenario: Actual incidence of the Russian tax-benefit system as of 2017**

**The fiscal system in Russia reduces poverty by half and inequality by a
<br>4|______<br>5|______<br>6|_______<br>7|
|---|---|---|---|---|---|---|---|---|
|__________|__________________|_______<br>|______<br>(%)|______|______|______|______|_______|
|Zhejiang|town in-migrant|<br>1.38|<br>7.71|3.80|19.59|4.02|63.46|0.04|
||<br>local population|2.57|8.84|4.93|23.38|17.22|43.00|0.05|
|Henan|town in-migrant|3.90|20.48|11.25|28.38|11.85|23.95|0.18|
||local population|3.19|12.55|7.65|17.45|43.01|16.00|0.14|
|Sichuan|town in-migrant|3.62|19.41|8.59|34.22|10.47|23.43|0.26|
||local population|3.14|14.50|6.57|24.41|31.81|19.38|0.20|

|________________________________________________________________________|
|---|

Source: calculated from Tabulation on 2000 Population Census of the People’s Republic of China, Table 4- 2b page 1167-1201, and Table L7-1b and L7-2b.

note: occupation title: 1, manager and administrative personals, 2. professionals and technician, 3. staff and other auxiliary workers, 4. salesperson, 5. agriculture, animal husbandry, and fishery, 6. production workers, 7.
# **3.2 Estimating a utility’s asset base**

We begin our calculations by estimating the value of the asset base for each utility with adequate representation in IBNET. To do so, we first estimate the average efficient asset base per customer for small, medium, and large utilities. Currently SISS regulates 59 utilities varying greatly in size; one is categorized as large (almost 1.7 million customers), seven as medium size (between 200,000 and 700,000 customers), and the remaining as small (fewer than 200,000 customers). Thirty-seven small utilities are excluded from our sample, as they serve fewer than 5,000 customers. Additionally, four medium size utilities are removed (three had yet to present their tariff revision studies, while one was deemed an outlier due to its reported assets), resulting in an overall sample of 15 (see Table 1).

**Table 1. Sample of 15 Chilean water and sanitation utilities**

|**Utility**|**Custo**<br>|**mers**<br>|**Data Sourc**<br>|**e**<br>|
|---|---|---|---|---|
||**Water**|**Sanitation**|**Year of Tariff Revision**|**Year of Data**|
|Aguas Andinas|1,691,195|1,652,946|2015|2013|
|ESSBIO|657,785|585,562|2011|2009|
|ESVAL|576,285|524,750|2015|2013|
|Nuevosur|204,343|193,202|2011|2009|
|ESSAL|192,311|180,554|2011|2009|
|Aguas del Valle|180,457|172,409|2011|2009
in responses provided by respondents in these two groups (Appendix D provides details about the methodology and additional results). Results are shown in Figure 10. Employment creation, health, community and social spaces, poverty reduction, and price controls were much more likely to be suggested by respondents who conditionally supported subsidy reforms than by those who supported them unconditionally. Employment creation was a particularly common theme among respondents who had only conditional support in each country. There were smaller but still statistically significant differences between these two groups for many other themes, including support for child and elderly care (which was cited by female respondents in particular). Collectively, the diversity of answers provided by respondents who had only conditional support for subsidy reform illustrates that this group has a relatively larger variety of policy preferences. The only theme that was statistically significantly more common among respondents who had unconditional support for subsidy reforms was directly related to their preferences about access to and quality of energy sources.

### **[Insert Figure 10]**

# **5 Experimental results**

This section presents the results of the main randomized survey experiment and shows the heterogeneous treatment effects across countries and between respondents.

## **5.1 Results of the main randomized survey experiment**

### **5.1.1 First-stage treatment effects**

The information treatments about the negative impact of energy subsidies considerably increased the share of respondents who agreed that energy subsidies are inefficient, inequitable, and harmful to the environment. Immediately following each treatment, respondents were asked a first-stage question about their level of agreement with a statement summarizing 35
previously underserved groups. For the already-served groups the impact may be the opposite, where greater transparency on the available products, for example a credit card with high rates and fees, would induce clients to reduce the use of services. At the country level, the result on financial access—in terms of the number of individuals served—may be positive, negative or neutral. Another possible outcome, at least on the credit side, is better loan repayment and lower nonperforming loans, as consumers are able to better evaluate available options and avoid taking loans they are not able to repay. However, country-level data on non-performing loans is unreliable and often driven by a number of macroeconomic and institutional factors that are difficult to control for.

We have experimented with a number of composite indicators as well as testing direct correlations between each subcomponent and country level characteristics. Table 1 presents pair-wise correlations for 10 consumer protection indices using the survey data. Quantitative measures for financial consumer protection are positively and significantly correlated with one another, suggesting that once legislation is put in place, it normally covers most of the areas. Yet, the correlations between consumer protection variables and account penetration are small in 15
the average density is 5 inhabitants per square kilometer , whereas in the west and north population density exceeds 200 inhabitants per square kilometer.

Following the distribution of economic activity and population, the country’s roads, power, and ICT backbones are concentrated in urban areas, in particular around Douala and Yaoundé. About 50 percent of Cameroon’s population live in urban areas, which account for the largest share of the country’s economy. Urban population growth remains high—more than 4 percent per year versus 2.3 percent in the country as a whole, with peaks of 7 percent in Yaoundé and 6.5 percent in Douala. In the past 15 years, urban population growth has been absorbed by poor housing spreading out from the edges of towns, with growing density in low-income neighborhoods near city centers. Most of the urban sprawl has occurred without links to infrastructure and with poor access to basic services. The current urban infrastructure stock is almost the same as it was at the end of the 1980s, while the population has more than doubled. Nearly 70 percent of the urban population, and all of the poorest urban inhabitants, have no access to public utilities or basic services (World Bank 2010b).

But beyond urban areas, Cameroon has managed to developed natural backbones for roads, rails, power, and ICT networks, especially by the standards of Central Africa (figure 4). For instance, there is a visible North-South axis, and greater density on coastal areas. However, regional connectivity with Nigeria and the rest of Central Africa remains limited.

5
studies commonly do not distinguish between domestic<sup>4</sup> and international investors when investigating the relative importance of risk factors. Yet, their risk preferences might differ due to the exposure and sensitivity to different risk classes. In terms of power sector segments, there is limited research investigating the differences between the different segments (generation, transmission, distribution, retail, off-grid, and mini-grid), even though there are likely differences between segments. With the increasing importance of index data to track power sector liberalization efforts, there is a growing need to ground-proof data and provide weighting of different factors (i.e., what is most important to actors in the power sector) (RISE, 2019). Lastly, there is a wide array of risk categories used across studies, increasing the need for a standardized risk assessment approach that can be easily replicated across different countries and regions.

This paper contributes to filling the existing gaps in the literature by first condensing the wide array of existing risk categories into a comprehensive and coherent framework. We then used this framework to survey 51 equity investors and lenders in the power sector in SubSaharan Africa between January and May 2019. We used a risk factor-based framework to structure the survey, which is described in detail in Section 3 (Waissbein _et al._ , 2013). This framework distinguished 10 risk factors, grouped into three categories, to evaluate the attractiveness of investment environments in the power sector. The survey focused on three sets of questions: (1) How important are different risks when evaluating a new power sector investment? (2) How ready are different segments of the power sector for private solutions? (3) How are different countries in Sub-Saharan Africa perceived in terms of past investment experience and prospects for new investments?

This paper is organized as follows. After reviewing the existing literature in Section 2, in Section 3 we introduce the aforementioned framework, sample and survey methodology, and Section 4 presents the results. Section 6 then ‘reality-checks
# **6 Conclusion**

In this paper we discussed and estimated a fat-tailed Spatial Vector Autoregressive Moving Average model that enables analyzing high-dimensional interactions between multiple crosssectional time series. This type of model is particularly useful to study Granger-causal interactions. The model requires specifying multiple matrices that define cross-sectional spillover channels. Networks may be part geographic, but also relate to economic distances. We introduced a framework to dynamically construct networks from the data. The smoothness of the network is controlled by a bandwidth parameter that can be integrated into the Likelihood framework.

We estimated the model parameters using remotely sensed pollution statistics of urban areas in Indonesia, together with subnational household expenditure data from surveys. We estimated two models with respectively 113 urban and 60 highly polluted urban areas over the period 1999-2014. Our networks are based on the similarities in sample moments and quantiles of local returns in the data, and the smoothness has been estimated within the likelihood framework. We contrasted the spatial model with a non-spatial counterpart and found that the spatial framework improves considerably in terms of various diagnostics, while using fewer ARMA parameters. Our approach to network modeling is not only favored by the data, it also provides additional insights. We find that cross-sectional dependencies in pollution are centered in smaller, but stronger, clusters than the economic variables. Expenditures of poor households, spill over more locally than those of average households.

Our economic findings are summarized in three main points: first, expenditure growth reduces pollution, particularly growth of poor households; second, pollution reduces growth in expenditures, particularly of poor households; third, growth is non-exclusive, there are significant spillovers from bottom-up growth but not from top-down growth. This imbalance in growth spillovers aligns with a body of literature debunking so-called “trickle-down” economics (see, for example, Quiggin (2009); Ranieri and Almeida Ramos (2013)), and suggests instead that investment in the poor is more effective than raising average incomes. Non-inclusive growth, lower resilience of the poor to pollution damages, and the importance of
which to believe that different types of intervention may be more effective in camp versus urban contexts. A next step for intervention-based studies and pilots might, for example, focus on attempts to increase economic interactions relating to inter-group exchange and employment in camp contexts, and attempts to address cultural or identity-based stereotypes in urban contexts.

# **5) Incorporate data collection relating to both perceptions and interactions into UNHCR and World Bank socio-economic household surveys focusing on refugee and host communities.**

Important advances have been made in both UNHCR and World Bank household survey data collection in refugee-hosting areas. One of the key insights of this paper has been to recognise the value of systematically collecting data relating to both interactions and perceptions. The analysis within this paper has been possible because the Refugee Economies Dataset is based on a wide-ranging socio-economic survey that included questions on the quality and quantity of different types of refugee-host interaction (e.g. business exchange, conversation, shared meal) and host community attitudes towards refugees/refugee attitudes towards hosts (e.g. relating to the economy, security, identity). Including modules on these variables alongside a wide range of other socio-economic indicators and control variables enabled us to explore the relationship between interactions and perceptions, both as aggregated indexes and disaggregated types of interaction and perception. A weakness of our overall Refugee Economies Dataset is that we only collected data on the quality and quantity of host community interactions with refugees in Uganda, although we collected data on refugee interactions with the host community and both communities’ mutual perceptions across all three countries. We believe our research highlights the value of refining these variables and systematically collecting related data in future UNHCR and World Bank data collection. In particular, UNHCR and World Bank socio-economic household surveys in refugee-hosting areas should systematically focus on both refugees and hosts, and include attitudinal questions relating to both social cohesion, and its likely determinants (including contact).

36
be used as dependent variables in the descriptive regression analysis to explore the heterogeneity of the impact of price changes by household 9 This diversity index is calculated by the formula: where, sjk referes to the share in the total expenditures of the whole population of the expenditures of individual _j_ on K N good _k_ , and sj. = ∑k=1 sjk and s.k = ∑j=1 sjk, N is the total number of individuals and K is the total number of expenditure categories. This index can be interpreted as one minus the ratio of the probability that two currency units drawn randomly from the expenditures of an individual are spent on different expenditure categories to the probability that two currency units drawn from the expenditures of the whole population are spent on two different expenditure categories.

> 10 Microdata from the 2020 Household Budget Surveys in EU and EEA countries has not yet been released.

> 11 These are the standard assumptions for calculating the CPIs in most countries (Kints and Breunig 2021).

11
statement. The data that support the findings of this study are based on two main sources, for satellite imagery and displacement data respectively. The Nighttime Lights (NTL) data obtained from the NASA/NOAA partnerships through the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite, openly available at</mark> <u><mark>https://earthdata.nasa.gov/learn/backgrounders/nighttime-lights. All displacement data used for this study,</mark></u> <mark>alongside with the agricultural indicator in 2020, were collected by IOM, through their Displacement Tracking Matrix (DTM). The data originates from the Baseline Mobility Assessment (BMA) and the Community Based Need Assessment surveys, openly accessible at</mark> <u><mark>https://displacement.iom.int/afghanistan. Any extraction, translation, reproduction, and</mark></u> <mark>distribution, in any form, or by any means, electronic, mechanical, photocopying, or otherwise, requires the explicit prior written permission of IOM. Source</mark> _<mark>: “International Organization for Migration (IOM), December 2021, Displacement Tracking Matrix (DTM)”.</mark>_ <mark>The population data comes from WorldPop</mark> 13
necessarily getting the best outcomes. While regions that were more affected by the conflict were reported to have become more conservative, the statistical evidence in this study allows us to question the consistency between what families would want their young women to do (presumably stay at home and raise a family) and what they allow them to do, when the need for survival exceeds the desire to adhere to traditional values.<sup>27</sup> What are the implications of the higher female employment? On the positive side, we should observe greater female empowerment, financial independence and an acquisition of labor market experience. On the negative side, studies suggest that women who work outside their household spend almost the same amount of hours in non-market activities such as preparing food, fetching water, taking care of children and elderly<sup>28</sup> as women who do not hold outside employment. Both, market and nonmarket employment contribute to exhaustion and lower the amount of time spent with children.

The results have important policy implications. The increased workforce participation among younger women signals that they are likely to positively respond to new employment opportunities if the government were to invest in industrial development policies. The creation of new local jobs would be particularly welcomed by women<sup>29</sup> as they are on average significantly less geographically mobile than men due to societal constraints and childcare duties. Further research that employs a combination of quantitative and qualitative techniques would help to understand better the benefits and disadvantages of an increase in female employment in Tajikistan and the fruitful directions for regional development policies.

> 27 I would like to thank Shahrbanou Tadjbakhsh for this observation.

> 28 The 2007 TLSS data suggest that on average women in Tajikistan spent about 60 hours per week on non-market activities. 29 Olimova and Bosc (2003) mention that until very recently there were very few young unmarried women travelling outside of Tajikistan in search of employment. Living abroad without close supervision by neighbors and relatives damages may damage reputation of such women