rafmacalaba/datause-encoder-data huggingface dataset:
Table 10. Program impact on health outcomes: Child weight of infants (weight-forage z-scores).

|Dependent variable||weight-|for-age z-s|cores||
|---|---|---|---|---|---|
|||1-18 month|s|19-36 m|onths|
|Speci...cation|(1)|(2)|(3)|(4)|(5)|
|Program impact|0.16*|0.17**|0.15**|0.012|0.04|
||(0.09)|(0.08)|(0.07)|(0.09)|(0.06)|
|Child age (log)|||-1.28***||0.07|
||||(0.07)||(0.17)|
|Female|||0.26***||0.08|
||||(0.09)||(0.06)|
|Constant|-0.64***|-0.71***||-0.95***||
||(0.07)|(0.06)||(0.08)||
|Controls|No|No|Yes|No|Yes|
|District ...xed e¤ects|No|No|Yes|No|Yes|
|Observations|1167|1135|1135|1300|1300|
|R<sup>2</sup>|0.002|0.003|0.22|0.00|0.04|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Dependent variable is weight-for-age z-scores.

c. Speci...cation: (1) Includes all children under 18 months, (2) Includes all children under 18 months with observations with recorded weight above the 90th percentile in the growth chart reported in Cortinovis et al (1997) dropped. (3) Includes all children under 18 months plus controls. (4) Includes all children between 18 and 36 months plus controls. (5) Includes all children between 18 and 36 months plus controls. d. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas. e. Control variables: see note (d) in Table A.8.

55
**Figure 7: Sigma-convergence Across Ethnic Groups in the Mean Quantity of Selected Durable Goods, 1995** **<u>vs. 2001 vs. 2003</u>**

|Panel (A): Au<br>|tomatic washing machines<br><br>|
|---|---|
|1995|2001<br>2003|
|0|.1<br>.2<br>.3|
|Panel (B): Co|lor TVs|
|1995|2001<br>2003|
|0|.1<br>.2<br>.3|
|Panel (C): Pe|rsonal Computers|
|1995|2001<br>2003|
|0|.1<br>.2<br>.3|
|Panel (D): Ca|rs|
|1995|2001<br>2003|
|0|.1<br>.2<br>.3<br>sigma (std. dev.)|

Source: Author’s calculations.

- 25 -
NHIS membership is voluntary. Although the government encourages all to join, less than half of the population is affiliated in any given year (Nsiah-Boateng and Aikens 2018). To obtain membership, individuals must pay a registration or annual renewal fee which, over our analysis period, varied between zero and 8 cedis (zero to 4.6 PPP dollars),<sup>8</sup> and an annual premium of 15 cedis (8.6 PPP dollars) in rural areas and 22 cedis (12.6 PPP dollars) in urban areas (Nsiah-Boateng and Aikens 2018). The premium is waived for several selected groups, including those under 18 years old and those aged 70 years old or older, as well as the “indigent,”<sup>9</sup> Social Security and National Insurance Trust (SSNIT) contributors and pensioners,<sup>10</sup> pregnant women, and women with infants under 3 months (and the infants). The indigent, pregnant women, and women with young infants also do not have to pay the registration or renewal fee.

The fees charged, even with increases in recent years, are far short of what would be actuarially fair. Instead, over our analysis period, NHIS received the bulk of its funding— roughly three-fourths—from a special 2.5 percent addition to the VAT called the National Health Insurance Levy (NHIL). A further one-fifth of its funding came from SSNIT, which pays NHIS fees for its members (Wang, Otoo, and Dsane-Selby 2017).

NHIS covers most illnesses found in Ghana. In principle, members can receive healthcare free of charge from all public providers and some participating private providers.<sup>11</sup> In practice, NHIS has a long history of being slow to reimburse healthcare providers (Wang, Otoo, and Dsane-Selby 2017), leading some providers to refuse to treat NHIS members and others to charge them despite that they are insured—something to keep in mind as we examine the difference between reported OOP healthcare expenditures for NHIS members and nonmembers.

> 8 In 2017, the purchasing power parity conversion factor for cedis to international dollars was 1.75 cedis per dollar. We use this conversion factor wherever we report values in PPP dollars. The conversion factor was obtained from the World Bank’s World Development Indicators databank.

> 9 NHIS defines an “indigent” as an individual who (a) “does not have any visible source of income,” (b) “does not have a fixed place of residence,” (c) “does not live with a person who is employed and has [a] fixed place of - residence,” or (d) “does not have a consistent source of support from another person” (https://nhis.gov.gh/Faqs/the <u>benefits-of-the-national-health-insurance-scheme-2?msclkid=60d5acdcab8811ec95eecede25b33dfb; accessed on</u> March 24, 2022).

> 10 SSNIT is a statutory organization that administers a national social security scheme. The scheme offers old-age, disability, and survivor benefits to its members.

> 11 Thirty-six percent of all participating healthcare providers are private. Because staff salaries at private healthcare providers are not paid by the government, NHIS reimburses these providers more generously than it does public healthcare facilities, but not by enough to cover the entirety of provider salaries.

7
BOX 2.2 TEXT REFERENCE FOR THIS BOX In East Africa many agricultural payments go into mobile money accounts <!-- Start of picture text --> FIGURE B2.2.1<br>Agricultural payment recipients and how they<br>receive payments in selected countries<br>Adults receiving payments for agricultural products in the<br>past year, by method (as % of all adults), 2014<br>60<br>In cash only<br>40 Into a financial institution<br>account<br>Into both a mobile money account<br>and a financial institution account<br>20<br>Into a mobile money account<br>0<br>Kenya Tanzania Uganda<br>Source: Global Findex database.<br><!-- End of picture text --> Given the widespread use of mobile money in Kenya, Tanzania, and Uganda, it is no surprise that many people in these countries receive payments for the sale of agricultural products into a mobile money account.<sup>a</sup> In Kenya 16 percent of all adults reported receiving agricultural payments into a mobile money account in the past 12 months. And among all recipients, 30 percent reported receiving the payments into a mobile money account—including 6 percent who also received payments into a financial institution account—while just 7 percent reported receiving them into a financial institution account only (figure B2.2.1).

Similarly, in Tanzania 23 percent of those receiving agricultural payments (or 12 percent of all adults) reported receiving them into a mobile money account, including 4 percent who also received payments into a financial institution account. Just 1 percent reported receiving payments into a financial institution account only.

Uganda lags somewhat behind its two neighbors. In this country 13 percent of those receiving agricultural payments (or 9 percent of all adults) reported receiving them into a mobile money account, including 3 percent who also received payments into a financial institution account. Another 2 percent of recipients reported receiving payments into a financial institution account only.

- a. See CGAP (2014) and GSMA (2015) for overviews of recent developments and challenges in digitizing agricultural payments for smallholder farmers.

> THE GLOBAL FINDEX DATABASE 4040
**<u>Table 9: The Growth Pattern, Education and the Structure of Employment</u>**

||Secondary Sector<br>Employment-intensive<br>Growth|Agricultural<br>intensive|Productivity-<br>Growth|
|---|---|---|---|
|Share of Workers in Agriculture|0.0933**<br>(2.48)|||
|Share of Population with No<br>Schooling||-0.000278**<br>(-2.60)||
|Average|-0.00275*<br>(-1.80)||0.00187*<br>(1.97)|
|Constant|-0.0178***<br>0.0102|0.00164|-0.0145**|
||(-3.17)<br>(1.19)|(0.77)|(-2.38)|
|Observations|105<br>88|88|88|
|AdjustedR-squared|0.091<br>0.009|0.074|0.034|
|*p<.10,**p<.05,***p<.01.T-st|atisticsinparentheses.|||

# **4.2.3 Trade, the Regulatory Environment and the Investment Climate**

Recent research into the determinants of growth has concentrated on “investment climate indicators” “competitiveness” and trade (see Dollar and Kraay, 2002; Dollar et al., 2003; and Kaufman et al., 1999). The general idea is that a poorly operating regulatory environment, corruption, excessive red tape, poor protection of property rights, macroeconomic instability and uncertainty and poor governance diminish returns to investment and thus hamper growth. Equally important has been the role of trade in promoting broad-based growth, as trade can affect markets for land and labor, which are the most abundant factors among the poor, as well as the prices of products produced by the poor (see Winters, 2000). In this section we extend this literature by analyzing whether the investment climate, the regulatory environment, and openness to trade affect the growth pattern.

The above-mentioned literature has benefited from recent efforts at collecting data to assess governance and the investment climate. Governance indicators have been collected by the World Bank since 1996, and investment climate indicators have been available since 2002. Unfortunately, few data points match our growth spells, and therefore we were unable to make use of this recent high quality data. However, the Heritage Foundation has been collecting data on “economic freedoms” since 1995 for a wide number of countries. Most of the measures are aimed at capturing government intervention in the business environment. The data contain measures for 10 economic freedoms and an overall score. Each index goes from 0 to 100, where 100 represents the 27
<!-- Start of picture text --> Soybeans<br>N<br>=<br>3<br>EO _—_®<br>a<br>no]<br>N<br>2002 2003 2004 2005 2006 2007<br>Period<br>—?— Biofuel —?— income<br>——®— Exchange rate<br>——®— energy prices<br><!-- End of picture text -->
<!-- Start of picture text --> Services2 Trade Restrictionsa Database Mm Home<br>STRI Key restrictions Main policy measures if<br>All policy measures comp tered Prey) Newt<br>Make<br>selection<br>Albania | Banking | Lending by Market entry allowed yes In cases. wherea foreign bank proposes to own qualifying<br>E banks. holding in a local bank, the foreign bank must be authorized by<br>the relevant authorities in its home country to carry out<br>acceptance and collection of deposits in money or other<br>repayable funds. The foreign authorities in charge of the<br>supervision of the foreign bank should give their consent to<br>such ownership in writing.<br>Albania | Banking | Lending by 3 ‘Greenfield branch-<br>banks allowed<br>Albania | Banking | Lending by ‘Greenfield subsidiary -<br>banks allowed<br>Albania | Banking | Lending by 3 ‘Greenfield subsidiary - 100<br>banks. max. ownership<br>allowed<br>Albania | Banking | Lending by 3 Acquisition domestic<br>banks. private entity - allowed<br>Albania | Banking | Lending by Acquisition domestic 100<br>banks. private entity -max.<br>‘ownership allowed by<br>a single entity<br>Albania | Banking | Lending by 3 2008 | Acquisition domestic 100<br>banks. private entity -max.<br><!-- End of picture text -->
<!-- Start of picture text --> All Manufacturing<br>Recyling<br>Other Transport<br>Motor<br>Medical<br>Radio, TV etc.<br>Electrical Machinery<br>Office. Equipment.<br>Machinery<br>Fabricated Metal<br>Basic Metals<br>Other Non-Metalicoo.Minerals<br>;<br>Rubber & Plastic<br>Chemicals<br>Fuels<br>Paper<br>Wood<br>Textiles<br>Food<br>Source: BPS<br>0 0.1 0.2 0.3 0.4<br><!-- End of picture text --> <!-- Start of picture text --> @ Transport<br><!-- End of picture text --> <!-- Start of picture text --> mg Communications<br><!-- End of picture text --> <!-- Start of picture text --> TM Wholesale & Retail<br><!-- End of picture text -->

- @ Finance, Insurance & Other Business Services

<!-- Start of picture text --> @ Electricity, Gas & Water<br><!-- End of picture text --> m Other (Non Trade Related Services)
37 Vergara, Elsa, Jamshid Gharajedaghi, & Russell Ackoff, 1980. "A Guide to Interactive Planning." _S-Cubed Papers, 80-4,_ Slpp., Social Systems Sciences Dept., The University of Pennsylvania, Philadelphia.

- World Commission on Environment and Development, 1987. _Our Common Future._ Oxford University Press, Oxford & New York.

- World Resources Institute, 1985. "Tropical Forests: A Call for Action." report of a task force of WRL the World Bank, and the UNDP, Washington, DC.

- 1987, -88, -89. _World Resources Report._ Washington, DC.

Worster, Donald, 1977. _Nature's Economy: A History of Ecological Ideas._ Univ. of Cambridge Press, Cambridge, U.K.
For our survey, we measure the income level of a household and the different sources of energy consumption. We follow the aggregation of different sources of income for the household members to determine the aggregated total income, and categorize it by income deciles for urban and rural households separately. We also calculate the expenditure for each of the energy sources for households reporting expenditure on at least one source of energy, leaving out of the sample households without energy consumption.<sup>3</sup> With regard to energy consumption, it may be difficult to derive conclusions on the comparable effects of income and household characteristics since each energy item has a different measure. In order to make a comparison between these different sources of energy consumption, we transform the expenditure to a measure of energy intensity, Kilograms of Oil Equivalent (KgOE), which is a standardized measure of energy that can be extracted from one kilogram of crude oil. This measure allows all data for energy consumption to be presented as comparable between sources of consumption. Also, the conversion to KgOE requires quantities of each type of energy used, and ENIGH only record the expenditure, so we extract prices from the Commission for Energy Regulation, calculate an approximated quantity for each energy type, and then convert it to KgOE.

We also have several characteristics of the household head, and of the household, in our estimations. All of the basic statistics are presented in the next table.

> 3 Households not reporting any energy consumption account for 6% of the sample.

9
than agriculture. Finally, Panel (d) suggests, consistent with Figures 1 and 2 that wage income losses were severe, especially in the LAC region, followed by some SSA countries. To understand the information content and internal validity of our different measures of labor market disruption, in Figure 7 we plot the relationship between four measures of income loss (total, farming, non-farming, and wage income) and stopping work. It appears that there is a broad positive relationship between each of the income measures and stopping work, although in general a higher share of respondents report household income loss than work stoppage. Furthermore, the relationship between work stoppage and wage income is much stronger than work stoppage and total income. This is consistent with income loss applying to all members of the household, and also with the finding that beyond stopping work, workers experience other disruptions, including reduced or no pay for work performed and changing jobs. Overall, however, the results suggest internal consistency between the prevalence of declines in household wage income and work stoppage, despite the latter being measured using a non-representative sample.

# **4.4 Relationship with Macroeconomic Projections and ILO Employment Data**

The labor market measure “stopped working” from HFPS differs in many cases from macroeconomic projections. Figure 8 shows the relationship between the HFPS-based measure of the loss of work and the change in the International Monetary Fund’s (IMF) World Economic Outlook (WEO) GDP projection for each country. The change in the WEO projection is the difference between the October 2019 and the October 2020 projections of 2020 GDP growth. Panel (a) suggests that the relationship goes in the expected direction in the LAC region: countries where the WEO projection has worsened more show higher shares of stopping work, with some notable outliers. At the same time, Panel (b) suggests that there is no relationship between the WEO’s projection change for countries in the SSA region. An important caveat when comparing the stopping work measures in the LAC and SSA regions 21
8 Our knowledge of adult mortality levels and patterns in the region comes from intercensal survival techniques, which have worked poorly in most applications, and from parental or spousal survival methods. Whether estimates from the survival methods are valid is still uncertain and, even if valid, they provide indicators for periods of decade or so before the survey date. Thus we have no data or techniques that allow us to measure recent levels of, or detect shortrun fluctuations in, adult mortality.

The situation with child mortality is somewhat more satisfactory. Much of our knowledge of child mortality levels and trends in the region is derived from census and sample survey data about the survival of children, as reported by mothers. Information from aggregate questions on child survival (numbers born and numbers dead) provides robust indicators of child mortality and of broad trends over time, typically to a point some five years before the survey date. But these indicators smooth out short-term fluctuations. Because of this, and because the data provide little usable information about recent (last five years) child mortality, their value for examining links between child mortality and economic crisis or adjustment is limited.

The Demographic and Health Surveys (DHS) program conducted 10 surveys during 1986-89 in Sub-Saharan Africa using a maternity history data collection format. With this format, information is sought about the date of birth and, if applicable, the age at death of each child borne by each woman interviewed. Such data permit the calculation of period life tables for childhood, showing both the level and the age pattern of child mortality for specific periods before the survey. Serendipitously, half the countries surveyed have been in structural adjustment, and half have not.
# **Annex B: Verification Monitoring of Pathogen Reductions Achieved by Wastewater Treatment and Health-protection Control Measures**

## **B.1 Wastewater Treatment**

It is not generally possible in developing countries, nor in most non-research situations in industrialized countries, to undertake routine monitoring of viral, bacterial and protozoan pathogens in treatment-plant effluents. Recourse has to be made to the use of indicator bacteria, typically fecal coliforms (section B.1.2).

Helminth eggs can be counted in raw and treated wastewaters, although the enumeration technique is not straightforward. If waste stabilization ponds are used for wastewater treatment, then the mean hydraulic retention time in the ponds can be used to calculate egg removal efficiencies (section B.1.2).

### **B.1.1 Indicator bacteria**

Fecal coliforms are the most commonly used indicator bacteria, and they are very easy to count using a very simple 5-tube most-probable-number (MPN) technique – full details of how to conduct this test are given in Ayres and Mara (1996).

If the QMRA-MC risk simulations show that wastewater treatment should achieve a 2-log unit pathogen reduction (see section A3.2), then the fecal coliform count has to be reduced from 10<sup>7</sup> −10<sup>8</sup> per 100 mL to 10<sup>5</sup> −10<sup>6</sup> per 100 mL, although a prudent designer might design the treatment plant to achieve a 3-log unit reduction of fecal coliforms (i.e., from 10<sup>7</sup> −10<sup>8</sup> per 100 mL to 10<sup>4</sup> −10<sup>5</sup> per 100 mL), given the uncertainty of the relationship between pathogen and fecal coliform numbers and how this might change through the treatment plant. Regular (perhaps weekly or fortnightly, but certainly monthly) monitoring would be required to show whether or not the effluent from the treatment plant contained ≤10<sup>5</sup> (or ≤10<sup>4</sup> ) fecal coliforms per 100 mL.

### **B.1.2 Helminth egg removal in waste stabilization ponds**

Waste stabilization ponds (WSP) (Mara 2004, Shilton 2006) are currently the only wastewater treatment process that can be reliably designed for helminth egg removal (Ayres _et al_ ., 1992). The design equation is: _R_ = 100[1 − 0.41exp(−0.49θ + 0.0085θ<sup>2</sup> )] (B.1) where _R_ is the percentage egg removal in a WSP which has a mean hydraulic retention time of θ days. Thus, if the number of eggs in the raw wastewater ( _E_ i eggs per liter) and θ are known, the number of eggs in the pond effluent ( _E_ e eggs per liter) can be determined.

For a series of WSP comprising an anaerobic pond, a facultative pond and _n_ equally-sized maturation ponds _E_ e is given by: where _r_ = _R_ /100 and the subscripts a, f and m refer to the anaerobic, facultative and maturation ponds, respectively – see ‘Worked Example A’ below.

Equation B.2 can be rearranged as follows: 90
# 7. Unit Costs

Our results on the determinants of earnings in Peru have corroborated a finding reported for some other countries, that the returns to investment in VTE are comparable to the returns to investment in general education. In most of the studies done elsewhere, however, the costs of VTE have been shown to exceed the costs of general education. In Peru, no such cost differential is clearly evident. Table 11 presents the average central government recurrent expenditure per stulent per year in secondary and post-secondary education in all years between 1960 and 1981 for which data were available.13 For post-secondary education, although some year-to-year variation exists, the Ministry of Education (MoE) data show quite consistently that less was spent per year of study in nondegree granting institutions than Ln universities. The average ratio of VTE costs to general education costs was 0.8 at the tertiary level between 1970 and 1981. Since most of the individuals in our 1985/86 sample would have attended post-secondary education before 1982, the period covered by the data series is qut-de relevant for the purpose at hand.

The situation with regard to unit costs at the secondary level is much less clear. According to the MoE figures, VTE costs were between two and three times more than general education costs in the period prior to 1970. For 1978 and 1979, the last two years in the available series, VTE again outstripped general education in terms of unit costs. For the intervening years, howeveo., the costs of the two educational streams appear to have been nearly equivalent. VTE's costs exceeded general education's on average by about 10 percent only.

Thers is little in the history of Peruvian education and the reforms announced in 1972 and 1982 that helps to explain the wild swings in the relative costs of secondary VTE and general education. Nearly all of the shifts in the relative positi ns of the two are accounted for by shifts in the former rather than the latter. Although there was an overall downward trend over the period, the fluctuations in expenditure per general education student between 1960 and 1979 were comparatively small, the reported figure in the highest year (1965) being just about double that in the lowest (1979). In the case of VTE on the other hand, reported expenditure per student in real terms was nearly four times larger in 1965 than in 1977. Some possible explanations for these puzzling figures will be considered in the following section.

13 Owing to the absence of data on unsubsidized educational costs incurred by individuals, on expenditure by lower levels of government, and on capital costs whatever the source of financing, the discussion of costs in this study will refer only to recurrent, central government expenditures.

26