rafmacalaba/datause-encoder-data huggingface dataset:
The objective of the present paper is to provide a critical account of the material and methodology used for the first-generation IPPS. Section 2 provides a brief assessment of the available databases. Section 3 describes our methods for estimating pollution intensities by combining US Manufacturing Census data with the US Environmental Protection Agency's pollution databases. Section 4 focuses on estimation of toxic pollution intensities weighted by human and ecological risk factors. Section 5 describes the media-specific pollution intensities developed for the US EPA's criteria air pollutants, major water pollutants, and toxic releases by medium (air/water/land). The results are critically assessed in the final section. The complete set of IPPS intensities is available from the authors on request.

In order to establish a reliable picture of industrial pollution, a large cross~-sectoral sample of facilities is required. Perhaps the world's largest sample is available in the databases maintained by the US Environmental Protection Agency and the US Census Bureau. Five of the databases with the greatest potential for constructing useful estimates and projections of industrial pollution are described below.

# 2.1 US EPA Emissions Databases

|

The US EPA maintains a number of databases at the 3
The IOF-2014/15 surveyed a sample of households that is representative at (i) national, (ii) national urban and national rural, (iii) regional (South, Center and North) and (iv) provincial levels (ten provinces plus Maputo City). The IOF-2014/15 survey is a panel, with each household interviewed thrice during the period Aug 2014 - July 2015. The sample size ranges from 10,373 to 11,506 with some variation across the three rounds owing to attrition, with attrition being higher in some provinces. The IOF-2014/15 recorded food consumed by the household in the past week from both purchases and auto-consumption through a 7-day diary. It also provides details of household income, including net income from sales of agricultural products, but it is recorded only at the aggregate level and not by commodity. Therefore, the IOF data is insufficient to calculate NBR based welfare estimates. At best, it can be used to calculate NBR values for representative groups of households, similar to the values presented in Arndt et al (2008). Therefore, additional information on household production is necessary to calculate more accurate values of the NBR.

The IAS 2015 (IAS-2015) collects detailed farm level data, including information on crop quantity produced, share and volume of output sold in the market by crop from a sample of 7,485 agricultural households. Using sales data from IAS, we impute values of the crop sales to households in the IOF, and then use these imputed values for calculating the NBR. To start with, we will impute values at an aggregate regional level. However, a caveat to be borne in mind is that the AIS samples agricultural households with an over-representation of rural households. Additionally, even within urban households the AIS samples those who have agricultural operations. This may result in a bias towards a positive NBR when using aggregate regional values from the IOF and AIS. In later steps, we will use a more refined approach level exploiting the geocoded locations of the sample households in the IAS and the IOF to impute values of sales at a finer spatial level to limit this potential bias.

# **3.2 Household expenditures and food consumption shares**

Food expenditure forms a disproportionately large share of consumption for households in Mozambique. Based on the consumption data from the IOF-2014/15, 56% of the total consumption at the national level goes towards food on average (Column (3), Table 1). We find a large regional variation in the households’ food budgetary share corresponding to the variation in expenditure levels. The three poorest provinces of Niassa, Nampula and Zambezia have a median daily per 13
poverty estimate for rural Vietnam of around 7.5 percent. This is considerably lower than the MOLISA rural poverty rate of 19 percent for that same year.

Although, MOLISA sets up an income poverty line to identify poor households, this poverty line is not applied since collection of income for the whole population is very costly, and almost impossible. In reality, the poverty classification procedure is rather complicated. Basically, a village committee prepares a list of the poor based on their own criteria, which may, for example, include asset levels, food security, type of housing, and school-going of children. The number and nature of the criteria differ widely between villages. The preliminary list is submitted to a commune-level committee of Hunger Eradication and Poverty Reduction (HEPR), which might conduct a very simple income survey for some households on the list. These surveyed households are expected to have income around the poverty line, thus their income data should be collected for crosscheck. The resulting incomes are compared to the income poverty line of the Ministry of Labour, War Invalids and Social Affairs (MOLISA). Those households with higher per capita income than this poverty line are excluded from the list. Finally, the refined list is updated by the village committee and the People’s Committee and People’s Council in an iterative procedure (MOLISA, 2003). Thus there is a large difference between the poverty estimates based on the VHLSS and the poverty incidence reported by MOLISA.

To facilitate comparisons between our income poverty estimates and the incomebased poverty rates from MOLISA, we adjust the income poverty line such that income poverty estimated using the 2006 VHLLS coincides with the MOLISA poverty rate (at around 18.5 percent for rural areas in 2006). The income poverty is set at 3,288,000 VND/person/year (in national real terms).

# **III. Methodology**

The small area estimation method developed by Elbers, Lanjouw and Lanjouw (2002, 2003) is arguably most popular in the context of poverty analysis. In ELL two data sets, a socio-economic survey and a census are combined through an income or expenditure model. This combination allows us to obtain small area estimates (SAE) of income or expenditure based poverty and inequality. By using the survey alone, we would only be able to disaggregate at the region level.

Typical indicators considered are average expenditure/income, percentage of poor (with expenditure/income below poverty line), poverty density (number of poor per area) and the Gini coefficient. We will determine both the point estimates and the standard errors associated with them. The standard errors are important because they make explicit 5
0.6 percent of Mexico City’s adult population obtaining the training annually.<sup>9</sup> Low take-up has severe consequences for statistical power. We therefore tried several approaches to screen individuals for their interest in participating in financial literacy training and thereby measure the impacts of such training on these individuals. This process gives insights into the demand for financial literacy training, and provides training impacts for a policy relevant group: since it is difficult to make adult financial education mandatory, a key policy question is whether policy efforts should try and encourage more people who are interested in attending such programs to actually attend them.

# **4.1 Approach One: Low Demand for Financial Literacy Demonstrated through a Mailing Campaign**

Our first approach to obtaining a sample of people interested in financial literacy training was to send a screener survey to clients of our partner financial institution. The institution agreed to partner with us and to provide us with a de-identified list of all their clients in Mexico City who have a savings account and a credit card. By conditioning on these characteristics we intended to identify individuals for whom debt management and saving advice is likely to be relevant and who have credit card and savings behavior that we could study. We further narrowed down the list of clients to those who lived in a municipality with a financial literacy training location or less than 5 km away. From this sample, we randomly selected 40,000 clients to receive a mailing with the screener survey. The randomization was stratified by gender and age. The mailings were sent by our partner institution through their usual provider between January 7 and 12, 2011. The delivery company confirmed delivery of 98.8 percent of the letters. Nondeliveries were due to clients having moved or the address not being found.

The mailing contained a letter informing clients we are partnering with their financial institution to help research ways people are managing their savings and credit card debt, and that we would like to see if they are interested in participating in a financial education training session. The letter only mentioned the training in general terms and did not refer to the specific program we are studying or the locations where this training was being offered. The mailing also contained a two-page screener survey that clients could mail back to us in a pre-paid envelope to indicate their interest in the training. This short screener survey collected information on name, address, phone number, sex, age, education level, occupation, household income and expenditure (in bins), as well as basic usage of savings accounts and credit cards. Clients also had the option of responding to the survey by going to a website or by calling a tollfree number. In order to increase response to the screener survey, half of the letters (20,000)

> 9 CONAPO projects that in 2012 there were 17,372,952 individuals 17 years old or older living in the Metropolitan Zone of Mexico City. http://www.conapo.gob.mx/es/CONAPO/De_las_Entidades_Federativas_2010-2050

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8 hand, shorter recall periods are subject to the “telescoping” effect by which health expenditures are compressed into a shorter time to fit the recall period (Beegle et al. 2012). Exploiting the two recall periods for inpatient care in the WHS, Lu et al. (2009) find that the 4-week recall period leads to a larger (annualized) estimate of out-of-pocket spending than the 12-month recall period. Fourth, surveys vary in <u>how they collect out-of-pocket expenditure data. Sometimes, the data are collected</u> in the expenditure section, and the respondent is asked to provide information on the household’s total out-of-pocket expenditures for a specific item. In other cases, the data are collected in the health section, and the respondent is asked to go through each household member in turn and report the household member’s expenditures for the specific item. In some surveys, out-of-pocket expenditures are collected in both sections. The choice of which approach to use may well affect the reliability of results.<sup>5</sup> Fifth, different adaptations of a survey may have different values of the out-of-pocket expenditure variable or variables. For example, the outof-pocket health expenditure variables in the US public-release Consumer Expenditure Survey data are top-coded.

# <u>Measuring consumption and income</u>

A consumption aggregate should capture consumption across a broad range of categories, should capture home-produced food and other items, and should also capture the use value of durables (Deaton and Zaidi 2002). Housing should also be included, measured by the value of the flow of services that the household receives from occupying its dwelling (Balcazar et al. 2014). An income aggregate should capture both wage and nonwage income, wage income including earnings from dependent activities, and nonwage income capturing all other income including household crop and livestock production, self-employment earnings and transfer income (Carletto et al. 2007). While in the past it was unusual to find income aggregates for developing-country household surveys, this is changing (Davis et al. 2017). It remains the case, however, that consumption provides a broader measure of a household’s living standards than income. Constructing consumption and income aggregates is a lengthy process, especially if they are to be constructed in a way that is similar across data sets. Using pre-constructed aggregates is therefore very attractive, whether via ex post harmonized surveys or via other surveys where aggregates have been constructed.

# <u>Additional data needs</u>

Additional data elements include the thresholds – the catastrophic expenditure threshold and the poverty line. Typical thresholds for consumption and income are 5%, 10%, 15%, 20% and 25%; the SDG (3.8.2) is 10%. Sometimes out-of-pocket expenditure is related to consumption net of a deduction for food costs, but this practice is ill-advised, as it makes it hard to interpret the resultant incidence rates (Wagstaff 2019). Obvious poverty lines in multi-country studies are the family of international poverty lines (Jolliffe and Prydz 2016; Ferreira and Sanchez 2017).
real effective exchange rate, adjusted so that the average for 1976-85 equals Dollar’s (1992) index of overvaluation<sup>19</sup> (Source: Easterly 2001). The number of years in which a country underwent a systemic banking crisis as a fraction of the number of years in the corresponding period is computed based on data from Caprio and Klingebiel (1999) and Kaminsky and Reinhart (1998).

The terms of trade shock in the matrix of growth determinants _X_ ’ in model (4) is computed as the log difference of the terms of trade (Source: World Bank 2002). The periodspecific shifts _μ_ in model (4) are time dummy variables.

# _3.2 The forecasting model_

The forecasting model used in this study is derived by differencing model (4): where _s=10_ since we work with 10 year periods, matrix _G_ contains the initial output gap, matrix _X_ , and the time specific shift; and φ = ( β , λ ,' μ ) .<sup>20</sup> In general, the forecasting model (5) can be used to forecasts growth for any number of periods of length _s_ , where _s_ could be any positive integer number. However, using this model to produce growth forecasts for the next 1 to 3 years is not recommended since many of the factors affecting the short term outlook are not included in the model.

Coefficients ( α ,ˆ φ<sup>ˆ</sup> ) were estimated by Loayza _et al._ (2005) using the GMM systems estimator on a 5-year average panel dataset<sup>21</sup> (see last column, Appendix Table 1 and the fourth column, Appendix Table 2). The GMM systems estimator, developed by Arellano and

> 19 The Dollar’s index of overvaluation gives an idea to what extent the real exchange rate is distorted away from its free-trade level by the trade regime.

> 20 As a result of the differencing the country specific term is eliminated from forecasting model (5). 21 The 5-year panel dataset contains 78 countries – fifty five of them developing – and for each of them, a minimum of 3 and a maximum of 8 non-overlapping 5-year observations (the sample is unbalanced). A minimum of 3 observations per country is required to implement the instrumental –variable methodology for the GMM estimators. The total number of observations equals 350.

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here have made a commitment for health services under the ASEAN Framework Agreement in Services (AFAS).

**Table 9: Explicit policy barriers and GATS commitments (Mode 3)**

||**Explicit policy**|**GATS commitments**|
|---|---|---|
|Cambodia|Full foreign ownership allowed; one director<br>must be Cambodian.|No restriction, except at least one director for<br>technical mattersmust be Cambodian.|
|Indonesia|Conflicting policies; full foreign ownership<br>allowed according to investment policy; 90<br>percent foreign equity limitation according to<br>Ministry of Health.|Unbound.|
|Laos|Full foreign ownership allowed.|Laos is in the process of acceding to the WTO.<br>US-Laos BTA grants full market access and<br>national treatment for health and medical care<br>services.|
|Malaysia|30 foreign equity limitation (though foreign<br>equity in one hospital exceeds 30 percent);<br>economicneeds test;minimumof 100 beds.|30 percent foreign equity limitation; economic<br>needs test; minimum of 100 beds.|
|Philippines|Foreign equity ownership limited to 40<br>percent for hospitals; full foreign ownership<br>allowedfor health maintenance organizations.|Unbound.|
|Thailand|Foreign equity ownership limited to 49<br>percent; can be circumvented by assigning<br>“nominees”; compliance with local<br>regulationsrequired.|Unbound.|
|Vietnam|Full foreign ownership allowed in principle;<br>economic needs test; transfer of technology<br>and training of local staff required.|US-Vietnam BTA commits Vietnam to full<br>foreign ownership, but sets minimum investment<br>requirements. Vietnam is in the process of<br>acceding to the WTO. Third offer in services<br>reflects commitment under the BTA.|

<u>Source:</u> National research studies (see footnote 2) and GATS schedules of commitments.

As for the movement of healthcare workers, explicit policy barriers vary from country to country. Among the richer ASEAN economies, Malaysia and the Philippines subject the entry of foreign medical professionals to economic needs tests. Certain professions, such as dentistry, are completely closed to foreign professionals. Market access in these countries is also granted on a preferential basis. For Malaysia, preferences take the form of bilateral agreements for the supply of nurses (with Albania, Bangladesh, India, Indonesia Myanmar, the Philippines, and Vietnam). In the case of the Philippines, there are numerous requirements for reciprocal treatment of Filipino professionals in foreign countries. Access of foreign healthcare workers to the Philippines is also subject to visa quotas set annually by the Department of Foreign Affairs. Thailand is, in principle, open to the entry of foreign professionals. However, few foreign professionals have entered the country, as they would need to pass a professional examination in Thai language (see below).

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Figure A6: Informality Engel Curve Slopes Controlling for Geography

# (a) Control: Rural Location (b) Control: Survey Block

<!-- Start of picture text --> CL<br>BI CD CL BI<br>NE TD BJ ST CG CDNE TD TZ BJ ST<br>MZ BF RWTZ CMBOPG ZA CRBR MZ BF RW KMCMBOPG CG TN CO ZA CR MX BR<br>SNKM TNSZ CO UYMX SN MA SZ DO PE UY<br>MA DO<br>PEME EC ME<br>EC<br>RS RS<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>5 5<br>0 0<br>−5 −5<br>−10 −10<br>−15 −15<br>Informal Consumption Slope Informal Consumption Slope<br>−20 −20<br>−25 −25<br><!-- End of picture text --> This figure shows each countries Informality Engel curves’ on their GDP per capita, when controlling for geographical variables. Panel (a) controls for a rural/urban dummy variable and panel (b) controls for survey enumeration blocks.

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**Table A14. In kind, in real terms (2014q1 set to 100)**

|||20|14|||20|15|||201|6|||20|17||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||Pla|ce of re|sidenc|e||||||||
|Minsk|100.0|66.4|112.1|105.5|85.2|73.0|109.9|103.6|96.3|82.5|128.0|131.2|118.4|87.1|145.9|125.5|
|Large cities|100.0|95.2|109.1|97.4|101.7|97.5|118.1|119.9|114.5|99.7|126.7|132.7|133.6|121.1|151.3|142.8|
|Small cities|100.0|86.6|99.9|94.6|113.4|93.6|110.3|122.6|123.2|112.4|118.6|137.5|128.1|117.9|127.2|140.9|
|Rural area|100.0|82.9|87.1|98.6|98.4|81.9|92.8|109.2|119.1|95.8|105.5|125.4|123.6|111.6|114.4|121.5|
||||||||Ag|e|||||||||
|Child|100.0|82.0|99.1|96.2|96.8|82.0|95.4|104.6|110.8|91.5|106.3|115.2|115.8|102.4|111.0|114.2|
|working age|100.0|86.4|96.3|96.4|99.5|83.9|99.7|108.0|112.7|94.0|109.3|124.5|121.4|106.1|120.6|121.1|
|Retiree|100.0|84.9|99.0|103.4|98.6|90.2|109.9|122.1|118.6|104.5|124.2|139.0|130.6|119.8|143.0|144.2|
|||||Em|ployme|nt stat|us for w|orking|age po|pulation|||||||
|Employed|100.0|84.1|92.9|93.1|98.0|82.1|101.6|108.4|106.9|92.5|107.5|121.4|118.1|102.0|119.1|118.6|
|Partly employed|100.0|74.4|87.5|103.4|83.7|81.0|94.0|99.5|120.3|83.3|98.5|108.0|103.7|107.2|99.6|92.5|
|Nonemployed|100.0|89.9|108.3|109.4|104.0|91.3|106.8|117.3|125.5|100.8|121.3|139.7|130.3|114.1|132.2|133.7|
|||||E|ducatio|n level|for wo|rking a|ge popu|lation|||||||
|high education<br>secondary|100.0|86.8|102.6|94.7|101.3|87.3|110.6|108.2|109.1|92.0|115.1|123.3|119.8|105.8|134.2|130.5|
|specialized|100.0|79.2|90.0|94.0|96.4|83.2|92.9|102.2|111.9|93.6|107.8|121.7|122.3|108.5|116.7|118.9|
|Other|100.0|92.3|98.8|99.2|103.1|83.6|100.6|113.5|116.8|96.6|108.6|128.8|124.0|105.8|117.2|118.9|
|||||||Type|of hous|ehold (|HH)||||||||
|HH without|||||||||||||||||
|children|100.0|88.3|96.7|100.0|101.6|90.2|107.2|116.9|116.4|102.3|119.1|136.8|129.4|116.4|137.6|138.3|
|Retiree HH<br>HH with|100.0|83.6|93.1|99.4|95.9|88.7|106.8|117.3|113.2|103.1|118.6|132.6|128.8|117.0|138.0|144.1|
|children|100.0|81.0|98.8|96.3|94.7|79.7|94.8|104.5|111.5|89.3|105.2|114.8|116.7|102.2|111.9|115.3|
|1 child|100.0|81.7|97.4|95.3|92.0|79.7|96.0|103.2|111.9|88.4|103.9|112.9|117.6|101.2|113.1|116.5|
|2 children<br>3 and more|100.0|79.5|95.7|93.3|93.0|75.7|89.1|103.5|108.8|89.0|105.3|119.2|115.2|104.4|109.5|117.2|
|children|100.0|81.8|115.3|110.7|120.4|93.3|107.5|118.3|119.2|97.4|114.6|111.7|115.2|101.3|111.2|98.0|
|singleparent|100.0|90.7|90.1|79.4|101.6|91.3|99.0|104.0|104.6|111.8|117.0|118.2|108.2|104.4|110.4|114.9|

Source: Authors’ estimates based on HSS data.

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