rafmacalaba/datause-encoder-data huggingface dataset:
respondents in psychological distress show no tendency to work less than others in their communities. A slight tendency is observed in Indonesia and a much greater tendency exists for women in the Indian sample.

# <u>4.2 Utilization of Health Services</u>

Large proportions of severely mentally ill populations in the developing world receive no treatment for their disorders, suggesting widespread under-utilization and poor access. On the other hand, the situation is further complicated when patients present with primarily somatic complaints for psychiatric conditions. A study of primary care attendees in Goa revealed that 97 percent of patients presented with physical symptoms, but roughly half of those were psychiatric cases according to biomedical criteria (Patel et al., 1998). This has a direct bearing on diagnosis and treatment, because somatic symptom presentation is associated with lower recognition rates of mental disorders by primary care physicians (Paykel & Priest, 1992).

The three large scale nationally representative surveys allow us to examine the extent to which mental health status predicts usage of health facilities in the last month, after conditioning on self-reported physical health status, income, and other possible determinants of use. Panel A of Table 5 depicts the probit marginal effect when the standardized GSI is used as the measure of mental health status, while Panel B depicts the marginal effect of being in the worst 10% of mental health scores within a country.

The results suggest that individuals with poor mental health are more likely to use existing health services. This effect occurs for both men and women in all three countries, with similar-sized absolute effect across genders in Mexico, a stronger effect for males in BiH, and a stronger effect for females in Indonesia. Comparing the size of effects to the proportions using health facilities, we see quite large associations. For example, a Mexican male in the worst 10% of the mental health distribution has a 0.06 larger probability of using health facilities, which is 54 percent of the overall proportion of Mexican males using health facilities (0.11).

The common observation in both developed and developing economies that individuals with poor mental health present themselves to health facilities at high frequency, independent of their (self-assessed) physical health status, is also found in these data. This behavior may pose economic and social burdens to households with individuals in psychological distress as well as to the overall health system, especially if the underlying cause of morbidity is misdiagnosed.

- 12 -
# **5. The empirical analysis**

## **5.1 Explaining differences in responses between the two surveys**

Based on the literature on subjective measurement and on the role of methodological issues on the measurement of innovation, we identified three sets of elements that may affect respondents’ answers to innovation questions (Table A1):

- Cognitive problems due to different understanding of innovation and quality of the interview

- Respondent framing ‐ both firm characteristics and context in which firms operate

- Recall period – time elapsed between the two interviews conducted for the experiment

Regarding cognitive problems, there are several factors that can play a critical role in the way respondents answer innovation questions. First, familiarity with the context of innovation is likely to be associated with accurate responses. This is represented by exposure to innovation activities as measured by whether or not a firm is currently engaged in or has abandoned innovation efforts in the past. Having additional information on what is considered as innovation is also likely to be associated with more accurate responses. This is captured by the use of explanatory cards with examples of innovations during the interview. The level of education of the manager or the years of experience of the respondents working with the firm are also likely to influence the response and make it more accurate. Finally, the gender of the respondent may play a role, as female and male respondents may be characterized by a different response behavior. The second set of issues that can affect the responses is linked to the quality of the interview. Quality of the interview is proxied by: whether the person interviewed in both surveys is the same; the number of interviews per supervisor ‐the higher the number of interviews per person the more difficult to control quality; and the accuracy of the answers provided, measured by the perceptions of the interviewer about the quality of the interview.

Regarding context and framing, the accuracy of answers is affected by survey fatigue that can be measured by several proxies. A first proxy is represented by the burden of the business environment, as measured by the number of meetings with tax officials. Having frequent meetings with tax officials can be a burden for firms as it reduce the time dedicated to productive activities. Therefore, as the number of meetings with tax official increases, firms may be more inclined to reply to surveys with less accuracy in order to quickly go back to productive work. Moreover, the length of the interview during the first wave of data collection is a good proxy for survey fatigue. Having already replied to a long interview could be an incentive to reply no to innovation questions in a follow‐up interview and reduce the time devoted to 18
Policy Research Working Paper 8138

# **Abstract**

Methodological research has showcased GPS technology as the new gold-standard in land area measurement in large-scale household surveys. Nonetheless, facing budget constraints, survey agencies continue to measure with GPS only plots within sampled enumeration areas or a given radius of dwelling locations. It is, subsequently, common for significant shares of plots not to be measured, and research has demonstrated that the incomplete datasets are subject to selection bias. This study relies on nationally-representative survey data from Malawi and Ethiopia that exhibit near-negligible missingness in GPS-based plot areas and uses these datasets to gauge the limits to the accuracy of a Multiple Imputation (MI) application for predicting GPSbased areas for plots that would typically be considered out-of-scope. The analysis (i) artificially creates missingness in area measures, ranging from 1 to 100 percent, among the plots that are beyond two operationally-relevant distance - thresholds with respect to the dwellings; (ii) multiply-im putes “missing” values in each dataset created by a distance threshold-missingness combination; and (iii) compares the distributions of the imputed plot-level outcomes with the distributions of their true, observed counterparts. In Malawi, the multiply-imputed distribution of plot-level land productivity is statistically indistinguishable from the true distribution in each imputed dataset with up to 82 percent missingness in GPS-based plot areas that are more than 1 kilometer away from the associated dwellings. The comparable figure in Ethiopia is 56 percent. The study highlights the promise of MI for simulating missing area measures and provides recommendations for optimizing fieldwork to capture the minimum required data.

This paper is a product of the he Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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the health benefits from reduced air pollution have been found to outweigh the cost of climate change mitigation in India (Markandya et al. 2009) and globally (Markandya et al. 2018).

With respect to local air pollutants, in PAKMod we concentrate on the most important air pollutant, particulate matter (PM 2.5). IIASA’s GAINS model, provides a much more detailed modeling of air pollution (including from non-hydrocarbon sources), although without the fiscal and economic levers of the present model.<sup>15</sup>

# 3.7.3 Fuels and pollution level

CO2 stays in the atmosphere for a long time and is decomposed only slowly over time. PM 2.5 by contrast does not accumulate in the atmosphere, because it settles to the ground a short time after emission. The concentration of PM 2.5 in the atmosphere thus strongly depends on recent emissions. Its harmfulness depends on wind patterns, the distance between emission sources and air quality within airsheds.<sup>16</sup> For example, it makes a difference, whether fuels are combusted in power plants, transportation, crop burning, construction or in waste burning.

While the CO2 emissions per fuel is a fuel-specific but global constant, such a constant does not exist for local air pollution. We thus use the data from the Carbon Pricing Assessment Tool (CPAT), see (World Bank 2020b), which calculates country-specific values for the pollutant intensity of fuel combustion from very detailed data. Note that NOx and SO2 are precursors of PM2.5, that means NOx and SO2 are emitted when fuels are burned and then they become PM2.5 in the air (Fantke et al. 2017; Humbert et al. 2011). These data distinguish, for example for five different types of liquid fuels, between emissions in different sectors and whether pollutants are emitted at ground level or from stacks.

Table 2: Fuel consumption and pollution intensity in Pakistan

|**Fuel**|**Fuel consumption in 2017 (ktoe)**|**Pollution (ug/m3 per ktoe)**|
|---|---|---|
|**Coal**|10,079|5.35E-05|
|**Natural gas**|26,155|2.96E-05|
|**Oil**|7,213|2.82E-04|
|**Gasoline**|8,037|3.31E-04|
|**Diesel**|9,539|4.38E-04|
|**LPG & kerosene**|1,508|3.05E-04|
|**Jet fuel**|115|2.93E-06|

Source: for fuels: : (IEA 2021), for pollution: (World Bank 2020b) Table 2 shows the fuel consumption for 2017 and the pollution intensity for Pakistan according to CPAT. We use the pollution intensity for coal and gas directly from the table. For the liquid fuels we take a weighted average, 3.53E-04 ug/m3 per ktoe. This is assigned to oil, which represents liquid fuels in the model. The concentration of PM 2.5 pollution in Pakistan due to the domestic burning of fossil fuels in the year is be calculated as SS

> 15 <u>https://iiasa.ac.at/web/home/research/researchPrograms/air/GAINS.html</u> 16 Air pollution can be transported over long distances due to wind and cloud chemistry.

40
<u>Table 3 (cont.)</u>

|||Unaid<br>Wo|men|Leisure<br>45-|64|Unaid<br>Me|n|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|2.47|5.58|11.42|4.49|5.44|0.81|11.94|5.79|
|Argentina|2.79|4.13|n.a.|0.04|5.33|1.48|n.a.|0.03|
|Bangladesh|1.62|5.04|15.33|1.99|6.17|1.54|14.06|2.23|
|Colombia|3.00|4.84|9.98|6.37|6.20|1.33|9.82|6.40|
|Ecuador|3.09|5.63|9.63|3.33|6.47|1.59|9.40|3.18|
|Ghana|2.84|5.18|13.24|3.81|3.19|4.41|12.68|4.80|
|Guatemala|2.83|6.33|11.21|1.98|7.26|1.19|11.18|2.27|
|Iraq|0.80|4.73|n.a.|5.77|5.41|1.88|n.a.|5.73|
|Mexico|3.12|5.45|n.a.|12.06|6.94|1.61|n.a.|11.79|
|Moldova|3.74|4.81|10.65|3.49|4.51|3.61|10.80|4.28|
|Mongolia|2.99|6.53|11.69|2.87|3.58|5.43|11.70|3.69|
|Pakistan|0.48|5.04|14.86|3.58|4.10|3.12|12.92|3.85|
|West Bank and Gaza|1.21|0.58|12.22|5.88|5.12|0.23|12.07|5.91|
|Peru|3.78|5.85|9.59|3.95|7.39|2.38|9.32|4.29|
|South Africa|2.86|4.27|12.83|4.31|4.77|2.38|12.87|4.78|
|Timor|1.93|1.63|n.a.|n.a.|3.67|1.01|n.a.|n.a.|
|Turkey|0.78|5.06|11.03|5.70|3.07|0.63|11.01|6.16|
|Uruguay|4.74|7.17|9.11|1.61|7.16|2.58|8.94|1.63|
|USA|3.71|3.47|10.56|5.49|5.00|2.47|10.25|5.87|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

<u>Table 4. Mean hours per day, by marital status, type of activity and sex</u>

|||<br> <br>Wo|<br>men|<br> <br>Married/Livin|<br>gtogether|<br> <br>M|en||
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|
|Albania|2.34|6.58|11.00|4.00|5.79|0.74|11.75|5.70|
|Argentina|2.49|4.74|n.a.|0.03|6.00|1.40|n.a.|0.03|
|Bangladesh|1.63|6.32|14.29|2.06|6.79|1.51|13.55|2.15|
|Colombia|2.70|5.43|10.07|5.89|6.87|1.22|9.65|6.03|
|Ecuador|2.57|7.14|9.71|3.56|7.02|1.52|9.39|3.36|
|Ghana|2.74|6.90|12.37|3.34|3.65|4.68|11.94|4.81|
|Guatemala|2.14|8.35|10.82|1.75|7.78|1.32|10.87|2.16|
|Iraq|0.56|4.99|n.a.|5.27|6.80|1.56|n.a.|5.06|
|Mexico|2.68|7.43|n.a.|12.32|7.74|1.79|n.a.|11.89|
|Moldova|3.43|5.53|10.63|3.12|4.81|3.39|10.78|4.11|
|Mongolia|2.99|7.12|11.34|2.61|4.02|5.73|11.46|3.21|
|Pakistan|0.48|7.30|13.29|2.89|4.99|2.94|12.41|3.64|
|West Bank and Gaza|0.93|3.69|11.07|5.64|6.01|0.54|11.28|5.78|
|Peru|2.98|7.08|9.70|3.66|7.72|2.35|9.38|4.04|
|South Africa|2.95|4.83|12.33|4.09|5.97|2.18|12.19|4.47|
|Timor|1.10|2.63|n.a.|n.a.|3.90|1.32|n.a.|n.a.|
|Turkey|0.81|6.14|10.75|4.77|4.20|0.55|10.71|4.89|
|Uruguay|6.19|8.49|8.72|1.49|5.60|3.25|8.35|1.91|
|USA|3.51|4.13|10.58|4.82|5.79|2.57|10.11|5.06|

31
alternative approaches to VSL, each of which has pros and cons. The results of this model-based study are interpretable only to the extent that the approach is valid.

WHO’s projections are based largely on case notifications that depend on the extent to which TB is treated in the public sector and the quality of health reporting in individual countries. Since the precision of benefits evaluated depend both on the evaluation of the VSL in individual countries and on the precision of mortality projections provided by WHO, we have subjected these numbers to an extensive sensitivity analysis that suggests that even if WHO’s estimates overstate mortality reductions, the economic benefits of Sustained DOTS typically exceed the costs. Benefit-cost ratios of the Global Plan scenario exceed one relative to no DOTS, but the incremental benefit relative to Sustained DOTS is not statistically significant in a number of countries.

In addition, there is considerable uncertainty about the effects of DOTS on the transmission and incidence of TB. As such, this paper illustrates a VSL-based approach to measuring the benefits and cost–benefit ratios of global TB control. A more precise assessment of benefits evaluated in this paper will depend on more complete assessments of the effects of DOTS on mortality, transmission, and incidence.

One might argue that our full-income estimates overestimate the effect of TB because we have not adjusted VSLs for the specific socioeconomic groups where death from TB is most likely. TB is a disease of poverty in many countries where it is prevalent. Malnutrition and overcrowding create ideal conditions for transmission of infection from person to person, and the disease is concentrated not just in the poorer countries of the world but also in the more disadvantaged socioeconomic groups within these countries. The poor are at greater risk of unemployment than their wealthier counterparts, even when they are not infected with TB, potentially limiting the economic effect of the disease. However, the socioeconomic dynamics of TB may be changing in Africa, where the greatest increases in future burden are predicted. Because of the relatively high coinfection rate with HIV/AIDS and the relatively high prevalence of HIV among urban Africans in higher socioeconomic groups, our estimates for Africa may not be greatly biased upwards.

21
there is no gender wage gap in the sample, though the sample includes only individuals under 25 years of age, when most men and women have still not experienced maternity in the country. In fact, the typical age for child birth in Kosovo is about 27.

# **2. Methodology and Data**

## **2.1. Data**

The econometric analysis is based on the ILO’s School-to-Work Transition (SWT) survey collected in Azerbaijan on a sample of young workers aged 15 to 29 in 2005 representative of the national population. As the name suggests, the survey collects information on the labor market experiences of young workers at the beginning of their career shortly after leaving school and entering the labor force. The data are at the individual level<sup>2</sup> with a sample size of 4,972. The survey includes three age groups: “teenage workers” (aged 15-19 years), “young adults” (20-24 years), and for lack of a better-term, the “prime-age workers” (25-29 years). The sample size is large for this segment of the labor force, which is generally under-represented in typical labor force surveys. The data thus provide an unusual opportunity to analyze the labor market performance of men and women in this stage of their careers.

Between 2004 and 2006, the ILO conducted SWT surveys in 8 countries (Azerbaijan, China, the Arab Republlic of Egypt, Kosovo, Mongolia, Nepal, and the Syrian Arab Republic). and subsequently made the micro data publicly available.<sup>3</sup> Although as a result of the lag in the public release of the data the data are now a few years old, the general paucity of data on earnings or wages in Azerbaijan and other countries, especially among young workers, means that SWT data provide a rare opportunity to assess the emergence of gender wage differentials.

## **2.2. Variables**

> 2 The survey also includes a questionnaire administered to the employers of young people in the sample. The Employer and Managers Module of the survey includes detailed information about the characteristics of enterprises including their recruitment, hiring, and training practices.

> 3 For further information on the SWT survey, see the following website: <u>http://www.ilo.org/employment/areas/WCMS_159352/lang--en/index.htm.</u>

4
|**From 10 to 19 size_10t19**|1896|0.1519|0.3590|0|1<br>658|0.3419|0.4747|0|1|1238|0.0509|0.2199|0<br>1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**More than 20**|1896|0.1134|0.3172|0|1<br>658|0.1109|0.3143|0|1|1238|0.1147|0.3188|0<br>1|
|**Does not know the size**|1896|0.3618|0.4807|0|1<br>658|0.2842|0.4514|0|1|1238|0.4031|0.4907|0<br>1|
|**Goals in life: Success at work**|1896|0.1176|0.3222|0|1<br>658|0.0927|0.2902|0|1|1238|0.1309|0.3374|0<br>1|
|**Giving a contribution to society**|1896|0.1245|0.3302|0|1<br>658|0.1109|0.3143|0|1|1238|0.1317|0.3383|0<br>1|
|**Earning a lot of money**|1896|0.2210|0.4150|0|1<br>658|0.0760|0.2652|0|1|1238|0.2981|0.4576|0<br>1|
|**Having a happy family life**|1896|0.3370|0.4728|0|1<br>658|0.5502|0.4979|0|1|1238|0.2237|0.4169|0<br>1|
|**Gaining all type of work experiences**|1896|0.0438|0.2047|0|1<br>658|0.0289|0.1676|0|1|1238|0.0517|0.2215|0<br>1|
|**Understanding the meaning of life**|1896|0.0533|0.2246|0|1<br>658|0.0593|0.2363|0|1|1238|0.0501|0.2182|0<br>1|
|**Gain self-esteem**|1896|0.1028|0.3038|0|1<br>658|0.0821|0.2747|0|1|1238|0.1139|0.3178|0<br>1|
|**Living in rural areas**|1896|0.5527|0.4973|0|1<br>658|0.5653|0.4961|0|1|1238|0.5460|0.4981|0<br>1|
|**Living under the poverty line**|1896|0.1065|0.3086|0|1<br>658|0.0821|0.2747|0|1|1238|0.1195|0.3246|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 52
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17