rafmacalaba/datause-encoder-data huggingface dataset:
Additionally, for each sensitive question, Table 2 identifies the response that is likely the most sensitive for respondents to choose. For example, when asked which party to the conflict that the respondent held most responsible for the violence they witnessed, the least sensitive option would be to avoid naming a party and to list ”Other parties involved in the conflict.” Alternatively, choosing any other party would potentially be sensitive, with the authority in control of their region potentially being the most sensitive.

Importantly, in our initial questionnaire drafts, we had originally included questions that were deemed too sensitive to even ask in a mobile phone survey, where the survey firm was worried about high attrition and about drawing the attention of authorities. These questions included those about whether individuals had supported groups in the conflict, what type of support they might have provided, and who was responsible for the start of the war. The inability to even pose basic questions about the conflict further illustrates the need for alternative modalities in such settings to more fully inform stakeholders.

# **Section 4b. Data Issues**

Before analyzing the degree to which the anonymity of internet-based surveys allows respondents to choose more sensitive responses, it is important to address three important data issues. First, although the surveys are identified as being anonymous, it is important to further investigate the degree to which respondents might have believed in the guarantee of anonymity. By analyzing attrition rates at particular questions in the internet survey, we are better able to infer the degree to which respondents’ beliefs about anonymity. Respondents were offered questions one at a time, and were only offered an additional question after responding to each question already posed. If respondents did not believe the survey was anonymous, we might expect a higher share to drop out before answering but after being posed a sensitive question.

However, Figure 1 reports the attrition rate at each question and illustrates that this was not the case.<sup>14</sup> Although the large sample size in the survey and the resulting

> are described in Table 2.

> 14The attrition rates for demographic and non-sensitive questions are reported in the left panel of the figure; and the attrition rates for sensitive questions are reported in the right panel. Within each panel, the attrition rates from left to right are reported in the order in which the questions were given to respondents.

12
As for the sample design in the unorganized sector, a stratified multi-stage design has been adopted. The first stage units (FSU) are the census villages in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units are enterprises in both sectors. In case of large FSUs, one intermediate stage of sampling is the selection of three hamletgroups/sub-blocks from each large rural/urban FSU. Two frames were used (as per the 62<sup>nd</sup> round survey): List frame and Area frame. List frame was used only for urban sector and that too for selection of manufacturing enterprises only. For unorganized manufacturing enterprises, a list of about 8000 big non-ASI manufacturing units in the urban sector, prepared on the basis of the data of the census of manufacturing enterprises conducted by Development Commissioners of Small Scale Industries in 2003, was used as list frame for surveys in 2005-06 and 2010-11. Area frame was adopted for both rural and urban sectors. The list of villages as per census 2001 was used as the frame for the rural sector, and the latest available list of UFS blocks was used as frame in the urban sector. The relevant year’s economic census was used as frame for the towns with population 10 lakhs or more (as per Census 2001).

The survey years from both formal and informal sectors that are included in our study are: 198990, 1994-95, 2000-01, 2005-06, and additionally 2009-10 from the ASI for the formal sector is studied while 2010-11 from NSSO for the informal sector is also incorporated. For sake of simplicity, we normally refer to the sample year by the starting year—for instance, the sample survey in the year 2005-06 is referred to as the 2005 survey. We likewise use 2010 in the tables and text to refer to the years 2009-10 for ASI data and years 2010-11 for NSSO data. As depicted above, the NSSO data provide the maximum possible coverage in terms of included years for the unorganized sector. Our development procedures below will seek to address changes in internal organization of surveys as well to the extent they exist.

> (NIC-2004, code 01405); and (d) non-ASI enterprises manufacturing bidi and cigar (those registered under bidi and cigar workers (condition of employment) Act, 1966, as well as those un-registered). Some of the surveys (such as 2010-11) covered trading enterprises and services establishment, but we exclude them for the purposes of our paper. Additionally, the NSSO excluded: (a) repairing enterprises not falling under Section ‘D’ of NIC-2004; (b) departmental units such as railway workshops, RTC workshops, government mint, sanitary, water supply, gas, storage, etc. in line with ASI coverage; (c) units covered under ASI; and (d) public sector units.

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-4-

it very difficult to provide basic services. At the same time, cities are growing rapidly, the major cities at a yearly rate of 5-7%, and some, including Lagos, Ibadan, Kinshasa and Accra, even faster. The rush of immigrants to towns over-extends fragile urban services for water, sanitation, electri'ity and communications. In many countries, intemnal policy-making may be complicated by externa! political or economic pressures to adopt certain policies or to deploy resources according to certain priorities. Where this happens, social welfare budgets, such as those for health, are often especially vulnerable, increasing the number of competing priorities for already meagre health-care resources. This problem is worse in countries where governments endorse primary health care but do not fund it, and policymakers are pressured to overspend on urban hospitals, which serve relatively few of the people, leaving very little for primary health care for the vast majority (Fig. 3). In many ways this conflict of health-care priorities parallels the ethical debate about family planning in Africa; it too is affected by competilig priorities, and internal and external pressures, and has a moral dimension, with the health and welfare of individuals and families at stake.

_Fig. 3._ The health care dilemma in Ghana <!-- Start of picture text --> Population<br>to be served TeHealth expenditures<br>Tertiary health care<br>1 % (specialised hospitals) 1 7 40%<br>9% _ _V_/<br>Secondary health care<br>(general hospital services) 4<br>/90% < \ \ Primary health care<br>--<br>(promotive, preventive,<br>and basic curative 15%<br>services at health posts,<br>health centres and<br>satellite clinics)<br><!-- End of picture text --> 85% of health expenditure goes to hospitals for 10% of the people. 15% goes to primary care for 90% of the people.

_Source:_ Morley, D. and Lovel, H. (1986): p. 148.
discussion of their potential for research on internal migration. More generally, given their increasing availability and huge potential, there is a clear need to invest more in research aimed at developing methods for improving integration and interoperability of household surveys with these new data sources .

**Weather data.** To investigate climate-related migration outcomes, accurate and georeferenced weather information is needed. However, most household surveys include, at best, self-reported measures of weather shocks based on individuals’ recalls, which can hardly be reliable or comparable given their subjective nature. For this reason, household data from multi-topic surveys are almost always autonomously integrated with external weather information. The main public domain sources of weather data include NASA _Modern-Era Retrospective analysis for Research and Applications_ (MERRA-2), the _Terrestrial Air Temperature and Precipitation_ database from the Center for Climatic Research at the University of Delaware, and the _High-Resolution Gridded Datasets_ from the Climatic Research Unit of the University of East Anglia. Remote sensing weather data sets, which can take the form of gridded, satellite, or reanalysis data, have been used in many studies leveraging LSMS-ISA data to address weather- and climate-related research questions in Sub-Saharan African contexts (see, among many, d’Errico et al., 2019; Letta et al., 2018; Mueller et al., 2020; Di Falco et al., 2022).

While this integration is typically carried out directly by the research team, this practice can be suboptimal for several reasons. First, the intrinsic diversity of weather data products. For instance, satellite data provide less accurate data than ground stations in most locations and do not extend as far back in time. Gridded data, on the other hand, aggregate data from ground stations via interpolation and across a given space. This works well in developed countries, where there is wide and uniform coverage of weather stations across the entire territory, but not so much in developing contexts, where often gridded data aggregate weather information from a few old stations spread across the country. Sparse coverage is a serious issue given the interpolation method adopted by gridded products. Finally, entry and exit of stations (quite common, especially in poorer countries) can be endogenous and represents an additional source of measurement error of true weather conditions experienced by people.<sup>11</sup> Such diversity in weather data products can affect econometric estimates of the relationship between climatic events and a given socioeconomic outcome of interest. Second, the need for spatial anonymization for privacy protection in household surveys, usually implemented through a random offset of true household

> 11 See Auffhammer et al. (2013) and Dell, Jones and Olken (2014) for further discussion. For a comprehensive overview of the availability and quality of climate data in the context of Africa see, instead, Dinku (2019).

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# **3 Methodology: Global Income Distribution Dynamics**<sup>**10**</sup>

The GIDD framework is based on CGE-microsimulation methodologies developed in the recent literature, including Bourguignon, Bussolo and Pereira da Silva (2003); Chen and Ravallion (2003); Ferreira and Leite (2003); and Bussolo, Lay, and van der Mensbrugghe (2006). The starting point is the global income distribution in 2000, assembled using micro data from various sources as follows: a) for 73 low and middle income countries, household surveys are available (together they account for 1.2 million households and over 5.1 million individuals); b) for 25 high-income and 22 developing countries, only grouped income data (usually vintiles) is available. These 120 countries cover more than 90 percent of the global population (see Bussolo et al. (2007) for a full detailed list).<sup>11</sup> The hypothetical 2030 distribution is then obtained by applying three main exogenous changes to the initial distribution: (a) demographic changes, including aging and shifts in the skill composition of the population; (b) shifts in the sectoral composition of employment; and (c) economic growth, including changes in relative wages across skills and sectors.

The GIDD's framework is depicted in Figure 1. Our simulations include the expected changes in the shares of population by groups formed by age and education characteristics (top boxes of Figure 1). The future changes in population shares by age (upper left part of Figure 1) are taken as exogenous from the population projections provided by the World Bank's Development Data Group. Therefore, we assume that fertility decisions and mortality rates are determined outside the model. As fertility rates decrease and life expectancy increases, older age cohorts will become larger in many countries. The change in shares of the population by education groups incorporates the expected demographic changes assuming that--since the younger generations are better educated than older ones--as population ages, educational attainments increase (linking arrow from top left box to top right box in Figure 1). To

> 10 See Bussolo, De Hoyos, and Medvedev (2008) for a detailed explanation of the GIDD’s methodology.

> 11 Throughout the paper, when we talk about the global distribution, we are indeed referring to the GIDD’s sample covering 90 percent of the world population.

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_Fareed M. A. Hassan, ECSPE_ _6_ What is the potential revenue that this income tax structure could generate? To calculate the potential revenue, it is important to know not only the aggregate tax base -- individual incomes -- but also its distribution.

**Table 2: Bulgaria's Personal Income** Tax **Schedule, 1992**

|Annual incorne (Lev)|Annual tax schedule|
|---|---|
|0- 9,000|tax free|
|9,000 - 12,000|20 % on excess over 9,000|
|12000.01- 36,000|600+ 24 % on excess over 12,000|
|36,000.01 - 72,000|6,360+ 28 % on excess over<br>36,000|
|72,000.01-120,000|16,440 + 32 % on excess over<br>72,000|
|120,000.01-240,000|31,800 + 36 % on excess over<br>120,000|
|over 240,000|75,000 + 40 % on excess over<br>240,000|

_Source: Tax Notes International Vol. 6, No. 19, 1993, p. 1140._ Bulgarian households' income level and distribution for 1992 was derived from the 1992 Individual Budgets of Households Survey compiled by the National Statistical Institute (NSI) of Bulgaria.<sup>2</sup> The sample covered 2202 households. It was constructed as a two-tier random sample based on a sample frame developed from the 1985 Population Census. The data allow a preliminary examination of the questions I am interested in: (i) how well the tax brackets and rates capture the revenue potential of their respective income groups, and (ii) how well they serve any distributional objectives implicit in the existing tax structure? To this end, the section briefly discusses the relationship between the distribution of income and the 2/A more detailed study of the country's income distribution, including details of the survey and a discussion of its representativeness, is given by Hassan and Peters (1996).
Findings in Table 5 show that in the deciles 2-5 around 90 percent of landholders engage in cultivation activities and around 85 percent raise one or more animal. These numbers are 10 and 3 percentage points lower for the 20 percent poorest farmers.

Table 3: Land Distribution by Farm Size in Haiti, 2001 (percent)

||Arti-<br>bonite|Center|Grand-<br>Anse|North|North-<br>east|North-<br>west|West|South|South-<br>east|Total<br>Haiti|
|---|---|---|---|---|---|---|---|---|---|---|
|< 0.5 ha|22.5|12.7|14.0|42.0|23.4|16.3|18.5|27.6|19.6|21.5|
|0.5-1 ha|32.7|25.8|27.5|28.4|33.0|26.5|31.1|28.2|25.4|28.6|
|1-2 ha|26.6|32.8|28.4|18.0|25.9|31.6|24.7|27.8|30.3|27.6|
|2-4 ha|11.2|19.7|13.1|7.6|11.7|15.9|15.7|9.8|17.3|13.7|
|4-7 ha|2.7|5.2|10.2|2.0|3.6|5.9|4.9|4.4|4.7|4.9|
|7-10 ha|1.7|2.0|3.2|1.0|2.5|2.1|2.4|1.9|1.3|2.0|
|10-20 ha|1.5|1.7|2.7|0.8|0.0|1.5|1.8|0.2|0.4|1.3|
|> 20 ha|1.0|0.2|0.9|0.3|0.0|0.2|0.9|0.2|0.9|0.6|

_Source:_ Own calculations based on HLCS 2001.

Table 4: Cultivation of Land, 2001

||Number|Percent|
|---|---|---|
|Cultivated|3932|87.91|
|Not cultivated|353|7.89|
|Leased out|174|3.89|
|Unknown|14|0.31|

Note: This table indicates results for any plot used (of those accessible)—if just one of several accessible plots are used it counts in this table as cultivated. Only households with access to land included.

_Source:_ Own calculations based on HLCS 2001.

Table 5: Land Holder Diversification of Activities in Haiti, 2001 (percent)

|||1(poorest)|2|Quintile<br>3|4|5 (richest)|
|---|---|---|---|---|---|---|
|Cultivation|Yes|80.22|90.08|89.24|89.94|90.04|
||No|19.78|9.92|10.76|10.06|9.96|
|Animals|Yes|81.97|84.04|83.61|86.37|85.86|
||No|18.03|15.96|16.39|13.63|14.14|
|Note: Onlyho|useholds|with land are i|ncluded. No. o|bservations: 4473.|||

_Source:_ Own calculations based on HLCS 2001.

Soil erosion is a serious problem for farmers. The once-abundant forests are now thought to be 97 percent depleted. Each year around 15,000 hectares of cultivatable land is lost to soil erosion **,** and in many areas the once-regular rainy season is something of the past. For the poorest rural people, the threat of drought and famine looms every year. A never-ending demand for charcoal has stripped the country almost bare. Today only 2-3 percent of the land is covered with forest.

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4 drup in bulk, generic form In some case, the same drugp purchased in bulk **throg** compotitive bidding by the pubUc sector are significntly cheaper than thoe purchased directly by the private pharmacies; in Zaire, the same drug was purchased by the public sector for las than balf of the priate sector price (Gluchberg and Singer, 1982).

Private sector pharmaies in Africa are concentrated in urban areas. For enmpk, of a total of 542 pharmacess in 9 French-speakdng West Afrian countries, 301 (56%) were loated in tJo capital city (Scrip, November 27, 1985) In Mali, the World Bank project zone of Kita, BDfoulabe, ad Kenieba circles (districts) had 7% of Mali's population, but _only 13%_ of the Pharmacie Populaire du Mali's (PPM) sales were made in that area Half of the PPM's sales are in Bamako, wre 10% of the population liv (World Bank, 1983). While the situation in many Englishspeaki countries and particularly in East Afica is somewhat better, the same concentation of private pharmacies in urban area pertains Even the pesoe of phrmacie Table 3. Relative Importance of Public and Private Sector Drug Distribution

|Coutry|(1)<br>Public<br>by Value<br>(Unadjusted<br>(Percent)|(2)<br>Ae,asted<br>) Cotal<br>--ercent)y|(3)<br>Public<br>as Percent<br>of Adjusted<br> <br>Total|(4)<br>Private<br>by Value<br>(Percent)|_(5)_<br>Public<br>by Value<br>(Percent)|
|---|---|---|---|---|---|
|Burkin Faso|64|36|72|136|53|
|Niger|57|43|86|143|60|
|Rwanda|77|23|46|123|37|
|Senegal|85|16|32|117|27|
|Zambia|57|43|86|143|60|
|Zimbabwe|_50_|50|100|150|67|

a Value of public sector drugs adjusted to private sector prices for comparison by multiplication by factor of 2

# SOW WOd BUnk Pbraeutka in the ThtW **WorId** PHN TecL Note 86-31, 1966, and WHO data

may not in itself guarantee access; the nrban poor are also prevented from having acss to drugs due to the high prices of drug available through the privte sector and the sbortages of free or low-cost drug available in the public sector facilities In Burkina Faso and Mali, for example, the price of French brand-name drugs in private pharmacies was fixed at 15-2C0% of the retail prke in France, despite the very much lower purchasing power of the clients and the fact that reimbursement is virtually non-eistentL A study in Abidjan, Ivory Coast found that the average cost of a prescription was the
demographics indicated that teenagers with younger siblings were less likely to be enrolled in school suggesting both that elderly children sacrificed for their younger siblings or that additional teenage labor was required at home to free adult labor up to the labor market. Similar to the consumption results, social assistance and social insurance mechanisms are not associated with better performance during the crisis. In education, household with pensioner heads experienced lower enrolment in Upper Secondary, also due to household labor decisions that involved the use of teenage resources. In contrast to findings from other countries, Moldova shows that crisis can marginally affect children enrolment, especially when household characteristics increase competition for resources and increases home labor demand for teenagers, and public expenditures experience significant declines.

The impact on health was examined in two dimensions: expenditures and utilization. Health expenditures were significantly reduced particularly in small towns. Similar to what is found in consumption, health expenditures decreased more for households with heads with higher education (which were exposed to the largest declines in income/consumption). Households' assets like land ameliorate these negative effects but neither public nor private transfers have any offsetting effect. Given the broad coverage of public health services in Moldova, household responses may be reflected in utilization as well. About 34 percent of households utilized health services in Moldova, a fraction reduced to 27 percent after the crisis. Most of the utilization reduction was due to the decline in primary health care (not so much in hospital utilization), and mainly among the richest households and in rural areas. The parallel decline in public expenditures in health between 1997 and 1999 may explain part of the decline, but income and wealth variables have increased effects in periods of crisis. In contrast to the results on consumption and education, social benefits play a positive role in utilization of health care, particularly after the crisis. The (negative) role of distance to health care, is negligible when the crisis occurs, suggesting that distance and access are of lesser importance compared to financial and economic constraints.

The analyses discussed in this paper show that Moldovan households where differentially affected by the impact of the Russian crisis, and that -- because of the nature of the crisis (fall in exports and devaluation) -- urban areas and the better off resulted bearing much more than the poor. Further separate analyses for urban and rural areas or by gender may shed additional light on the mechanisms underlying the decrease in welfare in Moldova.

# 6. References

Beegle, K., E. Frankenberg and D. Thomas (1999) Measuring Change in Indonesia. RAND Labor and Population Program Working Paper Series 99-07. DRU-2014WB/NIH.

Deaton, A. (1997) _The Analysis of Household Surveys. A Microeconometric Approach to Development Policy._ Baltimore: John Hopkins University Press.

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