rafmacalaba/datause-encoder-data huggingface dataset:
completely rule out possibility of different response pattern between face to face and telephone interview modes, they provide some evidence that the combination of CATI with the S2S approach appeared successful.

Attrition rates are significantly lower than the previous telephone surveys. In urban areas, around 9.7 percent of sample households did not respond to telephone interviews after agreeing to participate in the telephone survey, while in rural areas, around 9 percent of sample households did not. These numbers are consistent with the official telephone surveys in Serbia, but much lower than the corresponding numbers reported in the L2L study. Such low rates are likely to be attributable to SORS’s long experience of telephone data collection.

The sampling for this pilot was conducted very carefully and the non-response rates did not affect comparability of treatment and control groups much. Also, the key characteristics of households did not change much between the census data and the data collected by the pilot.

The cost for this experiment was relatively inexpensive at a total cost of $28,000. This included the cost to visit 1,200 households in the field and 600 phone interviews. The unit cost for this experiment was around $23 per interview (similar to L2L). If telephone numbers of households were already available, conducting the phone survey would be substantially lower.

As for implementation, the interviews were completed quickly with the average time for an interview of about 5 minutes. Also, sending official letters in advance to notify selected households of a potential upcoming visit seemed to contain nonresponse and attrition rates.

Other challenges still remain. Telephone coverage in a developing county is still limited to obtain nationally representative results, although it is rapidly expanding. Mixed mode data collection may be one possible solution in such contexts. The mixed mode data collection approach proved to be effective in increasing response rates but is found to be vulnerable reporting biases (see more in Dillman 2007). Further research will be necessary.

Finally, it is important to note that this pilot was conducted by SORS, who has a long experience of telephone data collection and well-established infrastructure for it, along with respondents who are used to it. To introduce this system in other countries where there is no such experience or infrastructure, there must be unobservable attributions of SORS that helped this pilot succeed well but this paper failed to pick and properly describe. Therefore, if this telephone data collection were to be carried out in other countries, it would be useful to review all steps carefully and consult SORS staff to make sure all fundamentals for the telephone data collection are properly satisfied.

22
46

# **Notes** to Tables **5, 6, 7,** and **8**

Unfilled lines in these tables are for countries in our sample for which-=the International Labour Office has not received satisfactory information. In several cases information should be available in time for an updated version of this paper and these tables.

# Table 5.

* Expenditure figures do not include some administrative expenses which apply to several programs.

All figures are for financial years ending in 1983.

Sources: Financial flows: International Labour Office (1988), The Cost of Social Security: Twelfth international inquiry. 1981-1983: Basic tables, Geneva (provisional figures from forthcoming publication). Gross Domestic Product: International Monetary Fund, International Financial Statistics Yearbook 1986 and Government Finance Statistics Yearbook 1986, Washington.

Table 6. Trends in financial flows: Table is forthcoming.

Financial flows are from earlier editions of I.L.O., Basic Tables. GDP is from I.M.F., °Fs cit.

# Table 7.

*Plus additional universal contribution. **Average rate.

***Figures reflect old system. Under new system, the insured worker's contribution rate is 19.5%. Employer rate is unchanged.

Contribution rates are for 1985, revenue and GDP are for 1983. Sources: Contribution rates: United States Social Security Administration (1986), Social Security Programs Throuahout the World - 1985, Washington. Distribution of revenue: I.L.O., op. cit.

# Table 8.

*Amounts are included in other tables. **Division among types of benefits is not explicit. [Jamaica-oad&d]: Includes 6.6% for which the nature of benefits is not explicit. (Mexico-other]: Day care for children. [Turkey-other]: Funds which apparently provide multiple types of benefits. [Portugal-other]: Nature of benefits is not explicit. Heading "Other Medical Care": Care not included under other benefits. Heading "Total Medical Care": Care included under various benefits.

Data are for 1983. Source: I.L.O., 2op cit*
Above, ∆Capitali are either changes in total regulatory capital ( _ΔRegulatory Capital/RWA_ ) measured as a percentage of risk-weighted assets, changes in Tier 1 capital ( _ΔTier1/RWA_ ) measured as a percentage of risk-weighted assets, or changes in a simple leverage ratio ( _ΔEquity/TA_ ) measured as a percentage of total assets for the banking systems in each country. These changes are calculated for each country _i_ by subtracting the level in 2010 from its value in 2016.<sup>19</sup> ∆Tier1Stringency is the change from 2010 to 2016 of an index that captures the stringency of the Tier 1 capital definition. The index ranges between 0 and 8, and is based on the number of components that supervisors do not allow as part of Tier 1 regulatory capital plus the number of components that are deducted from Tier 1 capital.<sup>20</sup> Thus, higher values of the index indicate greater stringency. BankingCrisisi is a dummy variable that takes on a value of 1 if a country experienced a systemic banking crisis between 2007 and 2009. This variable is constructed using data on banking crises episodes from Laeven and Valencia (2018). In the regressions, we control for the initial levels of each dependent variable to capture potential mean reversion in capital ratios or capital stringency (Capitali,2010, Tier1Stringencyi,2010). Macroi,2010p are four country-level variables that are commonly used as controls in cross-country analyses. These controls are the natural logarithm of the GDP per capita ( _ln(GDP per capita) 2010_ ), inflation computed using the consumer price index ( _Inflation 2010_ ), natural

> 19 We use as reference dates for BRSS 5 and BRSS 4 the years 2016 and 2010, respectively. This is because quantitative information is recorded up to year 2010 in BRSS 4 and up to year 2016 in BRSS 5.

20 The capital instruments that might be allowed into the definition of Tier 1 capital are: a) hybrid debt capital instruments, b) asset revaluation gains, and c) subordinated debt. The instruments that might be deducted from Tier 1 capital are: a) goodwill, b) deferred tax assets, c) intangibles, d) unrealized losses in fair valued exposures, and e) investment in the capital of certain financial intermediaries. The selection of these capital instruments reflects availability of information over the two most recent BRSS surveys.

25
In the case of India, official rural and urban poverty lines are readily available from national sources. Chen and Ravallion (2010) used the official poverty lines from India’s Planning Commission for 2004-05, which suggest cost of living being 51% higher than in rural areas.<sup>44</sup> More recently, an Expert Group constituted by the Government of India (2009) recommended a higher rural poverty line based on corrections for biases in past price deflators. The revised socalled Tendulkar poverty lines for 2011-12, propose an urban line being only 22% higher than the rural line (Government of India, 2013). This is the ratio used in the present estimates for India. For Indonesia, the estimates use the official urban and rural poverty lines from 2011 (Statistics Indonesia, 2015). Table 4 summarizes the data used in applying these adjustments and the resulting rural and urban 2011 PPPs for China, India and Indonesia.

# _Within-country spatial price adjustments in LAC and ECA_

The second category of within-country spatial adjustments is pertinent to the Latin America and Caribbean (LAC) region, and the Europe and Central Asia (ECA) region. For most countries in these regions, PovcalNet draws on spatially adjusted consumption and income aggregates from regional databases. In LAC, PovcalNet uses aggregates from the Socioeconomic Database for Latin America and the Caribbean (SEDLAC) in which all rural incomes are increased by a factor of 15% to capture differences in rural-urban prices, based on average ruralurban price differences observed in the region (CEDLAS and World Bank, 2010). Although this uniform adjustment is somewhat arbitrary, CEDLAS argues that it is preferable to ignoring the problem of spatial price differences or allowing for different methods across countries. Adjusting all incomes from SEDLAC to urban price level is particularly reasonable in the context of international poverty measurement using PPPs, since the ICP exercise (both in 2005 and 2011) in

> (national) 2011 PPPs are similar to those obtained using the original (separate rural/urban) surveys and our PPP adjustments. The harmonized distributions for 2011 and 2012 give national poverty estimates of 8.0% and 6.0%, respectively, compared to 7.9% and 6.5% from the original rural and urban distributions and above adjustment. The World Bank will update China’s poverty estimates when integrated and revised surveys become available in the future.

> 44 India’s official poverty lines for 2004-05 were Rs 17.71 and Rs 11.71 per day for urban and rural areas, giving a ratio of 1.51 in 2005. These lines date back to Government of India (1979) and have since been updated by rural and urban inflation. Update by rural and urban CPIs to 2011 this ratio is 1.45 in 2011.

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- 21-

Table 6. Mean Earnings, Years of Schoolng and Mincerian Returns to Education: Entire Sample, chrca 1980

|Country|Year|Earnings<br>local curr.)|Years of<br>Schooling|Rate of<br>Return(m )|
|---|---|---|---|---|
|Argentina|1980|735,000|7.9|9.3|
|Brazil|1979|10,244|5.3|11.4|
|Colombia|1980|8,401|6.9|18.6|
|Costa Rica|1981|1,925|6.7|16.8|
|Panama|1979|231|_8.5_|13.0|
|Paraguay|1983|42,203|8.2|11.6|
|Uruguay|1981|4,375|_8.5_|10.3|
|Venezuela|1981|2,619|7.3|11.8|

Source: Annex 3.
# 1. Introduction

Accurate data on the key economic variables affecting people who have been forcibly displaced, such as consumption and assets, are essential to understanding their situation and to developing evidence based policies to support them. Poor information can lead to flawed diagnostics or incorrect assessments of impact. Data inaccuracies may lead policy makers to allocate funds to the wrong people or to the wrong programs. The standard way in which the World Bank and other policy organizations develop statistics is through individuals’ responses to questions in economic surveys. Self‐reported information is vulnerable to myriad reporting inaccuracies when social scientists ask personal or intrusive questions or when respondents anticipate social or material implications to the answers they provide.<sup>2</sup> This is of particular concern when respondents believe that misreporting may provide relief, both because of the of the sensitivity and the gravity of the policy challenge. In situations where it has been possible to compare survey responses to revealed economic behavior, striking disparities are sometimes found. In one investigation for example, Poterba and Summers (1986) report that misstatements regarding employment status in the Current Population Survey led to an underestimation of the duration of unemployment by up to 80 percent and even greater overestimates of the frequency of labor market entries and exits. In another study, Rosenfeld, Imai, and Shapiro (2016) look at voting behaviors in a sensitive anti‐abortion referendum held in Mississippi in 2011. They compare actual county level vote shares against survey results from a sample frame of individuals who voted during the election (based on public records). Surveys that used direct questioning led to an underestimation of casting a “no” vote by more than 20 percentage points in the majority of counties.

There are a number of mechanisms through which the validity of self‐reported information in surveys can be compromised. Some inaccuracies result from cognitive biases – for example, acquiescence or “yea‐saying” (Bachman and O’Malley 1984; Hurd 1999), extreme responding (Cronbach 1946; Hamilton 1968), and question order bias (Sigelman 1981). One solution to problems such as question order bias is to randomize the order of questions (Warner 1965). Other inaccuracies emerge from conscious but not calculated behavior. Respondents may deliberately misreport information on sensitive subjects not to distort statistics but to maintain their reputation or to abide by political norms (Gilens, Sniderman, and Kuklinski 1998). A common solution to this is to enable participants to cloak their behaviors or beliefs. List experiments, endorsement experiments, and randomized experiments are commonly used techniques for this purpose (Rosenfeld, Imai, and Shapiro 2016).

> 2 This is of particular concern, for example when asking about race (Kuklinski et al. 1997) or corruption (Gingerich 2010).

2
ECOWAS travel certificate for member states, the establishment of the Brown Card insurance scheme, the creation of the West African Unit of Account to facilitate international payments within the region, and the elimination of some tariffs.

# **Trade Liberalization**

Trade facilitation has been one of the main objectives of ECOWAS since its establishment. Early attempts at trade liberalization were hampered by the unwillingness of many countries to implement the provisions of the ECOWAS treaties relating to tariff and non-tariff barriers.<sup>8</sup> Thus, the difficulty of implementing the treaties of the community Treaties is the most often-cited obstacle to integration. This is partly due to the very limited statutory powers of ECOWAS to force governments to implement the trade liberalization directives and agreements. Other problems include the: relatively low levels of intra-regional trade (during 1994-2000 the share of intra-community trade was 19.8% for exports and 20.9% for imports);<sup>9</sup> overlapping integration arrangements with many countries belonging to more than one regional community; lack of political commitment; and the inadequacy of compensation mechanisms. In addition, trade has been impeded by corruption on the borders and inadequate transport infrastructure.<sup>10</sup> Recently ECOWAS has paid considerable attention to trade integration and has set up a “Roadmap to the ECOWAS Customs Union.” This roadmap includes six broad categories of action that include the creation of a free trade area, a Common External Tariff (CET), and the harmonization of customs legislation and regulation. With the help of the USAID and the ECOTRADE project, ECOWAS has expanded the UEMOA’s CET throughout the region. In May 2005, ECOWAS adopted the plan to implement a common external tariff that will come into force in December 2007. The planned CET encompasses four tariff bands (20%, 10%, 5%, 0%). The newly created ECOWAS CET Management Committee has the oversight responsibility for the CET.<sup>11</sup>

> 8 OECD. 2001: “Regional Integration in West Africa”.

> 9  United Nations Economic Commission for Africa, 2004: “Assessing Regional Integration in Africa” 10 www.panos.org.uk.

> 11 Sources : www.usaid.gov, www.aird.com.

10
12 In part, because of these differences, the cost of stunting, in terms of the reduction in per capita income from some of today’s workforce being stunted in childhood, varies considerably across countries – from 1% to 13%, with an average of 7%. Africa and South Asia are the regions with the largest average penalties – around 10% of GDP per capita. Countries (and territories) with stuntinginduced per capita income reductions less than 2% include Bermuda; Chile; Fiji; Hong Kong SAR, China; Samoa; the Seychelles; Tonga; and Trinidad and Tobago. At the other extreme, Ethiopia’s per capita income is 13% less than it would have been if none of its workforce had been stunted in childhood. Other countries with large ‘stunting penalties’ include Burundi, Guatemala, Malawi, Mozambique, Rwanda, and Vietnam.

<u>Table 2: Costs of childhood stunting among today’s workforce</u>

|World Bank region|No. countries|Mean|
|---|---|---|
|East Asia & Pacific|23|-7%|
|Europe & Central Asia|9|-5%|
|Latin America & Caribbean|33|-5%|
|Middle East & North Africa|19|-4%|
|North America|1|-2%|
|South Asia|8|-10%|
|Sub-Saharan Africa|47|-9%|
|Total|140|-7%|
# **0. Main Job [JOB INCUMBENTS ONLY]**

**0.1 In the last 30 days, did you do any work for at least 1 hour to receive a salary or wage, engage in any productive or business activities to generate income, or do any unpaid work to generate family income?**

- 1 YES 2 NO [STOP SURVEY]

# **0.2 In your main job/business, what kind of work do you do?**

- 1 Main tasks and duties 2 ISCO code [STOP SURVEY IF MAIN JOB IS NOT RELATED TO 4-DIGIT OCCUPATION]

# **0.3 What is the main activity of the business or establishment where you worked?**

1 Name of establishment (if any) 2 Main activity, goods, or services 3 ISIC code

# **0.4 How many people including you are there in the business or establishment?**

- 1 1 person 2 2-4 persons

- 3 5-9 persons

- 4 10-19 persons

- 5 20-49 persons 6 50 persons and above

37
expenditures for pregnant women. However, wealthier women are also significantly more likely to be counseled on family planning, prescribed iron, and asked about their blood type. Of the six non‐clinical quality indicators, only one is statistically significant at the 95 percent level. A standard deviation increase in wealth is associated with a 2.6 percentage points increase in the likelihood of being encouraged to ask questions during the consultation.

Analysis of the household survey data reveals a strong relationship between education level and reported content of care during the sequence of consultations. Relative to women who never went to school, women with at least some primary or secondary education reported receiving an additional 3.3 and 9 percent of the content of antenatal care (table 2, column 2). As can be seen in table 2, column 3, holding wealth level fixed, women with higher education levels report receiving better care relative to less educated women. Conversely, holding schooling level fixed, wealthier women receive higher quality care. Interestingly, the education‐quality relationship does not show in the direct observation data. It could be that the relationship presented in columns 2 and 3 is due to different recall by women of different education levels. Alternatively, it could be that this relationship is because, conditional on wealth, education is significantly correlated to the number of consultations. However, controlling for the number of consultations does not change the quality‐education relationship.

The existence of a wealth‐quality gradient could theoretically be driven by several mechanisms. First, wealthier women may simply live in the catchment areas of facilities with higher structural quality. Table C.4, in appendix C, shows that wealth is indeed positively correlated with the availability of equipment and consumables in the health centers.<sup>13</sup> Wealthier women are also more likely to live in the catchment areas of referral health centers that offer a wider package of services, including laboratory services, and have a doctor on staff. These correlations are significant at the 99 percent level. When controlling for province, these relationships weaken but remain positive and statistically significant at least at the 95 percent level, suggesting that there is residential sorting even within provinces.

To assess the extent to which residential sorting explains the wealth‐quality relationship observed overall, table 3 presents village fixed effects results using the household survey data. Without any controls, the coefficient reduces from 0.041 (column 1 in table 2) to 0.018, but it remains statistically

> 13 Indexes of equipment and consumables used in antenatal care were created with principal component analysis. The items included in the equipment index are scale, meter, fetal stethoscope, adult stethoscope, and blood pressure cuff. The items in the consumables index are diagnostic tests for HIV and syphilis, pregnancy test, capacity to do a urine test, tetanus vaccine, deworming pills, iron supplements, antimalarial pills, and treated mosquito nets.

11
burden of disease 2006, are available by a number of distinct regions of the world (WHO, 2006). This computation was therefore conducted for each country using the DALY for the region in which the country is located.

The increase in DALY that will result from the increased disease incidence from the exposure to DDT in each country provided a physical measure of the DDT health externality. These physical measures in terms of years lost were then converted into monetary values using the per capita PPP GDP in each country. Like the estimation of direct cost, indirect costs were also separately estimated for the two scenarios, _DDT use widespread_ and _DDT use restricted_ . Once again, the increased _indirect_ costs from DDT exposure across countries of Sub-Saharan Africa are shown in Figure 3 as an illustrative example.

# **[Insert Figure 3]**

Estimates indicate that the _widespread DDT use_ scenario is likely to result in an increased cost of more than $24.92 billion, while a _restricted DDT use_ scenario will result in a cost increase of nearly $4.58 billion. Once again, this is an underestimate as it does not include the effect of DDT in all disease categories because of the non-availability of data for some diseases and the costs should be used as a lower bound.

# **5.** **_Direct and Indirect Costs of Malaria_**

The analysis of the externality caused by the use of DDT is based on the assumption that there will be continued use of DDT in countries that are using it at present and countries facing high incidence of malaria will re-introduce DDT for malaria control. Hence, the analysis will be incomplete unless a comparison is made between the externality caused by the use of DDT and the cost savings that can be attained by lowering the incidence of malaria with the use of DDT. In order to determine such cost savings, the overall costs imposed on society by malaria in the vulnerable countries were estimated by examining both the _direct cost_ s of treating the disease as well as the _indirect_ cost caused by the increase in morbidity and mortality among malaria patients.

14