rafmacalaba/datause-encoder-data huggingface dataset:
detergent residues, cleaning fluids, personal care products (e.g., deodorants), and pharmaceutical residues, all of which are discharged as part of the greywater into the household wastewater.

**Table 3.2 Tentative guideline values for the maximum permissible concentrations of selected inorganic and organic pollutants in wastewater-irrigated soils**

|_Inorganic compounds_<br>|**Maximu**<br>|**m permissible concentra**<br>|**tions (mg per**<br>|**kg soil)**<br>||
|---|---|---|---|---|---|
|Antimony|36|Arsenic|8|Barium|302|
|Beryllium|0.2|Boron|1.7|Cadmium|4|
|Fluorine|635|Lead|84|Mercury|7|
|Molybdenum|0.6|Nickel|107|Selenium|6|
|Silver|3|Thallium|0.3|Vanadium|47|
|_Organic compounds_||||||
|Aldrin|0.48|Benzene|0.14|PAH<sup>_a_</sup>|16|
|Chlorodane|3|Chlorobenzene|211|Chloroform|0.47|
|Dichlorobenzene|15|2,4−D|0.25<br>|DDT|1.54|
|Dieldrin|0.17|Dioxins|1.2×10<sup>−4</sup>|Heptachlor|0.18|
|Hexachlorobenzene|1.4|Lindane|12|Methoylchlor|4.27|
|Pentachlorophenol|14|PCBs|0.89|Pyrene|41|
|Tetrachloroethane|1.25|Toluene|12|Toxophene|0.0013|
|2,4,5−T|3.82|Trichloroethane|0.68|Phthalate|13,733|
|Styrene|0.68|||||

_Source:_ Chang _et al_ . 2002. _a_ As benzo(a)pyrene.

# **3.2   Environmental risks and benefits**

# _3.2.1 Environmental risks_

Soil and groundwater pollution is clearly a potential disadvantage of using wastewater in agriculture. Under most conditions, wastewater irrigation does not present a microbiological threat to groundwater since it is a process similar to slow sand filtration: most of the pathogens are retained in the top few meters of the soil, and horizontal-travel distances in uniform soil conditions are normally less than 20 meters. However, in certain hydrogeological situations (for example in limestone formations) microbial pollutants can be transported for much greater distances, and careful investigation is required in such cases (BGS, 2001). Chemical pollutants, among which nitrates are of principal concern in the case of domestic wastes, can travel for greater distances, and there is the potential risk that drinking-water supplies in the vicinity of wastewater irrigation projects may be affected. In general, therefore, and unless a rigorous hydrogelogical appraisal indicates otherwise, water supplies should not be located within, or close to, wastewater-irrigated fields; conversely, wastewater irrigation should not take place in areas where the groundwater is used for drinking-water supplies.

As a result of increased rates of salinization and waterlogging, soil pollution can occur through wastewater irrigation if adequate attention is not paid to leaching and draining requirements. Saline drainage waters should be used to irrigate salt-tolerant crops where possible, and crop and field rotation will generally be necessary to avoid long-term damage to the soil structure. Adherence to good irrigation practice is essential to avoid adverse environmental effects (Ayers and Westcot, 1985; Rhoades _et al_ ., 1992; Hillel, 2000; Tanji and Kielen, 2002). Often a trade-off has to be made between agricultural production and environmental protection, and this must be carefully evaluated at the 30
allocations and misallocations). Third, they more frequently include information on the government's strategy for the sector. When a sector financing model is used, the tradeoffs often reflect the government's explicit priorities. One problem area that is highlighted much more consistently in single-sector reports is the insufficiency of funds allocated to materials and maintenance. This imbalance exemplifies the type of budgetary underfunding or misallocation that is often not apparent from the macro perspective. Fourth, the singlesector reports cite a wider variety of objectives for the education sector. Fifth, they often discuss the social consequences of education reforms. Finally, single-sector reports are more often developed within a long-term (to year 2000) framework. Yet the detailed sector work in single-sector reports is often presented in a macroeconomic near-vacuum.

5.06 Multisector PERs treat more thoroughly than do single-sector PERs the macroeconomic strategy, the effect of education reforms on the public budget, intersectoral trade-offs, dangers to the sustainability of reforms, and questions of political and institutional feasibility. More frequently than single-sector PERs, they use Bank-generated data rather than government data and government planning documents rather than actual expenditures. Because the PER is often a tool for the projection or analysis of a mediumterm expenditure program, the multisector reports focus on reforms that can be implemented within three to five years. Multisector reports also tend to refer to underlying sector studies or other Bank experience in the country. Single-sector reports rarely do because they usually precede any non-project involvement of the Bank in the sector, and they reflect either the initial sector work or an update after a lapse of several years.

5.07 In spite of the commonalities described earlier, there is great diversity of coverage among PERs. Although the reports serve different purposes at different stages of the Bank-country dialogue, greater standardization of coverage, format, data, and data sources is advised.

# B. Coverage of Education Financing

5.08 A major problem for the analyst is not knowing what portion of total education financing is represented by the public budget allocated to the relevant ministry. Three specific questions need to be answered:

# a) Are there significant nongovernment sources of education financing?

b) Are there significant public sources of education financing that do not appear in the central government's budget?

c) Are there significant central government sources of education funding that fall outside the line ministry?

5.09 Nongovernment sources of education financing. Reports cite two types of nongovernrent funding of education: private funding by beneficiaries (or their families), businesses, NGOs, religious organizations, or other groups; and private or public financing by foreign countries. The role of the private sector in the financing of education is described in 60 percent of the countries reviewed. Twenty reports describe private schools, 42
importance of macroeconomic stability for food market performance, there is no evidence to suggest that the observed changes in market margins simply marked a return to earlier performance levels following the economic disruptions of 1996.

Because the improvement in market performance coincided with the introduction of marketreliant reforms and consistent food policy, but took place during a period when related institutions were judged to be weak, the finding is consistent with the notion that some market institutions are endogenous and that, in some cases, there are measurable benefits to consistent policies alone. This speaks against delaying reforms until market-backing public institutions are judged to be strong.

Even so, the estimated changes in market performance are spatially uneven and it may well be the case that this measured heterogeneity is partly related to an uneven evolution of market institutions and related public investments. To the extent this is so, the results suggest that the weak performance of institutions can contribute to an unintended consequences in the distribution of the benefits and costs of reform. Moreover, when privately provided institutions are inferior to public institutions, any potential gains from good policy will be constrained. Consequently, policy makers will need to strike a difficult balance of pacing reforms, building up supporting market institutions and putting in place safety nets where markets are weak.

From a methodological perspective, the results suggest heterogeneous rates of market development, as expressed in the idiosyncratic components of the model. Consequently, methods that impose a fixed dynamic on how prices adjust to policy may produce misleading results. Moreover, the observed pattern of relative price changes and the predicted differences in relative transaction costs are consistent with shifting patterns of internal trade. This is especially important since many studies of spatial markets rely on observations over time of a limited number of markets and on time series approaches where fixed rates of adjustment are assumed.

# **References**

Ashley, Caroline and Simon Maxwell. 2001. “Rethinking rural development.” Development Policy Review 19(4): 395-425.

Balcombe, Kelvin, Alastair Bailey and Jonathan Brooks. 2007. “Threshold effects in price transmission: the case of Brazilian wheat, maize, and soya prices.” American Journal of Agricultural Economics 89(2), 308-23.

Brennan Donna, Jeffrey Williams and Brian D. Wright. 1997. “Convenience yield without the convenience: a spatial-temporal interpretation of storage under backwardation. The Economic Journal 107(443), 1009-1022.

18
Figure 2: Change in the cumulative distribution function due to the crisis <!-- Start of picture text --> 3<br>Change in 2<br>cumulative[L<br>distribution<br>function<br>(% points)<br>oA<br>6.70522 15.1677<br>Log per capita consumption<br><!-- End of picture text --> Figure 3: Percentage change in the cumulative distribution function due to the crisis <!-- Start of picture text -->
1.4 1<br>1. 3<br>-1.3 | r | Log poverty i<br>%Change in<br>cumulative 1.2 -<br>distribution<br>function<br>d1s[n,[ oR 1.1 T X<br>6.70522 15.1677<br>Log per capita consumption<br><!-- End of picture text -->

34
quintile. Proportionally more reductions in undernutrition occurred among children from the richest quintile. The rest, from the 1<sup>st</sup> to the 4<sup>th</sup> quintile, are within the 95% confidence intervals. Second, the widening income-related inequality in health is stronger in the long- than short-term nutritional status because the absolute gap clearly widened for stunting but not for wasting and underweight. Third, the rural–urban gaps are either constant or narrowing. Fourth, education followed the pattern exhibited in income; the improvement was higher among children whose mothers have more education, secondary education and above.

<!-- Start of picture text --> Figure 1a. Trends in child malnutrition by wealth ranking, residence and mother's education<br>(2000-2014)<br>Stunting Wasting Underweight<br>100 100 100 100 100 100<br>Rich/Poor Wealth Quintile Rich/Poor Wealth Quintile Rich/Poor Wealth Quintile<br>richest richest<br>80 rich (top 60%)poor (bottom 40%) 80 richermiddlepoor 80 rich (top 60%)poor (bottom 40%) 80 richestrichermiddle 80 rich (top 60%)poor (bottom 40%) 80 richermiddlepoor<br>poorest poor poorest<br>60 60 60 60 poorest 60 60<br>40 40 40 40 40 40<br>20 20 20 20 20 20<br>0 0 0 0 0 0<br>2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014<br>100 100 100 100 100 100<br>Urban/Rural Mother's Education Urban/Rural Mother's Education Urban/Rural Mother's Education<br>80 urbanrural 80 secondary+primary 80 urbanrural 80 secondary+primary 80 urbanrural 80 secondary+primary<br>no education no education no education<br>60 60 60 60 60 60<br>40 40 40 40 40 40<br>20 20 20 20 20 20<br>0 0 0 0 0 0<br>2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014<br>Note: Computed from DHS data. Income is based on wealth index. Vertical lines in the graphs are 95% confidence intervals.<br><!-- End of picture text --> Figure 1b shows the trends for child mortality (NMR, IMR and U5MR). Child morality is an important outcome that the country has substantially reduced over the 14
statistical or legal definition.<sup>2</sup> For instance, the U.S. bases its business statistics on establishments, Canada reports Average Labor Units (ALU),<sup>3</sup> while countries reporting to Eurostat<sup>4</sup> and UNECE<sup>5</sup> use various measures including legal (enterprises), geographical (local unit), and activity-based (kind of activity unit) approaches for their business statistics. As a result, the data are not easily comparable across countries: the proposed unit of measurement must take into consideration the availability of the data, consistency across countries, relevance to entrepreneurship, and focus on the formal sector.

Hence, our definition of the unit of measurement of entrepreneurship is: _Any economic unit of the formal sector incorporated as a legal entity and registered in a public registry, which is capable, in its own right, of incurring liabilities and of engaging in economic activities and transactions with other entities._ There are no clearly-defined, internationally agreed-upon, minimum-size criteria for business activity (United Nations, 2005; Hoffmann et al., 2006). In this study, the aim is to collect the information of all businesses regardless of their economic or staff size.

# **_2.1   A Focus on the Formal Sector_**

This study focuses on the size and growth of the <u>formal</u> private sector. However, the informal sector (or “shadow economy”) plays an important role in many countries, ranging from over 75% of official GDP in Nigeria to about 10% in the United States (Schneider and Enste, 2000). The previous literature has highlighted the potential advantages to formal sector participation, including police and judicial protection (and less vulnerability to corruption and the demand for bribe), access to formal credit institutions, the ability to use formal labor contracts, and greater access to foreign markets (Schneider and Enste, 2000).<sup>6</sup> However, because of burdensome regulations, high marginal tax rates, the absence of monitoring and compliance (of both registration and tax regulations), and other weaknesses in the business

> 2 At the international level, Eurostat and the OECD have attempted to define the concept of business.  Other countries like the United States, choose the establishment as the main unit for business statistical purposes.

> 3 US Census Bureau: http://www.census.gov/econ/www.  Also see Longitudinal Employment Analysis Program (LEAP) of Statistics Canada: http://strategis.gc.ca/epic/site/sbrp-rppe.nsf/en/rd00827e.html.

> 4 Council Regulation (EEC) No 696/93 of 15 March 1993 on the statistical units for the observation and analysis of the production system in the Community, Official Journal L 076 , 30/03/1993 P. 0001 – 0011.

> 5 Terminology on Statistical Metadata, United Nations Statistical Commission, available at: http://www.unece.org/stats/publications/53metadaterminology.pdf

> 6 The benefits of formal sector registration might vary by industry.  This is discussed in Section 5.3.

4
where _Yt,i_ is a particular flow type of country _i_ at time _t_ , _Dt,t′_ is the set _Y t_ and _t_<sup>_′_</sup> and _N ′_ is the of countries with data for flow for time periods _t,t_ number of such countries. As done in the main text, the flow data are standardized by subtracting the mean and dividing by the standard deviation of the respective time series (before computing the covariance matrix (C.1)).

Comparing the first two rows of Table C.1 we see that using the simple principal component estimator yields a very small increase of the average adjusted _R_<sup>2</sup> ( _R_<sup>2</sup> ), from 0.054 (corresponding to the 0.05 average shown in their appendix figure A7), to 0.075 when analyzing disaggregated flow data at quarterly frequency. Considering instead total inflows and outflows (obtained by summing all flow types, in the fourth line of the table) leads to another increase in _R_<sup>2</sup> . In, turn, the same happens when we consider the disaggregated data at yearly, rather than quarterly, frequency (lines 6-7 of Table C.1). With any of these two changes we obtain _R_<sup>2</sup> around 0.125. We note that when considering flows at the yearly level, the factor estimation method seems to make less of a difference. Normalizin <u>g</u> by trend instead of nominal GDP also leads to a modest increase in _R_<sup>2</sup> . If flow aggregation and yearly frequency are combined, the _R_<sup>2</sup> rises above 0.2, almost four times the initial 0.054 value.

Unlike ours, the framework of Cerutti et al. (2017c) includes only two factors and no factors. One to assess how this affects group global way their results is to add to their setup one more factor per group, as a crude of the contribution of the factor to the variance of way capturing global in each of their These extra factors are cominflows country groups. puted as the second principal components of the non-central advanced and emerging-country inflows, respectively. Doing this raises the _R_<sup>2</sup> to 0.274. The 63 ’non-large’ countries considered by Cerutti et al. (2017c) are mainly emerging (although Australia, Canada, Denmark, Norway, New Zealand, Sweden, Switzerland, Iceland and Hong Kong SAR, China, are also included). The _R_<sup>2</sup> we obtain for emerging countries with our data is 0.290, thus roughly similar (this is the adjusted _R_<sup>2</sup> ; the slightly larger values in Table 5 correspond to the unadjusted _R_<sup>2</sup> ). Taking all countries in our sample, but excluding US, UK, GER, CHE and JPN, we obtain 0.376; if we further exclude all EMU countries, we obtain 0.315.

If we examine the explanatory power of the estimated factors for the flows of advanced countries (those with "ad"=1 or "nonlarge"=0 in the Cerutti et al. (2017c) data), we obtain the results shown in the middle section of 67
ferrying food and other goods across the border on foot or on bikes. As such, an intensive tracking exercise was conducted at endline to retrieve approximately 84% of the baseline sample. In addition, the research team conducted a thorough exercise to confirm and match the time-invariant characteristics of baseline and endline survey respondents.

Table 1 presents the baseline characteristics of the sample of 628 cross-border traders. Nine out of ten traders in the sample are women and three quarters live on the DRC side of the border (Table 1, Panel A). The average trader is 35 years old, has been engaged in cross-border trade for ten years, owns relatively few assets, and relies primarily on trade as her main cash source.<sup>15</sup> Overall, the traders’ activity is largely a channel for imports into the DRC from Rwanda: 87% of traders import goods, 12% export, and only 1% do both.

Prior to the training, only a third of traders could correctly identify all authorized and unauthorized institutions at the border (Table 1, Panel B), although traders were relatively more aware of which institutions were not authorized to be at the border (Appendix Table A.1). Table 1 also shows that half of traders cross the border before the official opening time (7:30 a.m.), and they crossed the border 3.4 times a week (Table 1, Panel B). Despite trading small quantities of duty-free goods such as vegetables and fruit, approximately 19% of the traders reported paying some amount to cross the border. In addition, 22% of traders experienced GBV or harassment, primarily in the form of being insulted or spit upon, at the border in the preceding 30 days (Table 1, Panel C).

Balance tests indicate that treatment and control assignment groups had largely similar baseline characteristics, except for the type of trade they did and their experience with GBV. At a 10% level of significance, there were 5% more exporters among traders randomly selected to receive the training than among those assigned to the control group (Table 2). This could give incentives to traders from the treatment group to cross the border early at unofficial morning times, to avoid meeting with border officials, and sell their products in the neighboring country to avoid paying fees on their return border crossing. However, there was no significant difference in the border crossing times between the assignment groups at baseline (Table 3). While Table 3 shows no significant treatment-control differences in trader knowledge, crossing patterns, or bribes, it does reveal an imbalance in the incidence of GBV. Approximately 19% of traders assigned to the control group

> 15A comparison of this sample with DHS data from DRC and Rwanda suggests that these traders’ households are at a similar welfare level to other urban households along the border (Garc ́ıa Mora and Roshan, 2013).

19
Garnier, J. (2003): How much does intra-industry trade matter in business cycle transmission?, mimeo.http://www.iue.it/Personal/Researchers/JulienGarnier/Julien Garnier 3YF II.pdf Helg, R., P. Manassa, T. Monacelli and R. Rovelli (1995): How Much (A)symmetry in Europe? Evidence from Industrial Sectors, _European Economic Review_ , 39, 1017–1041.

Krolzig, H. M. (2001): Business Cycle Measurement in the Presence of Structural Change: International Evidence, _International Journal of Forecasting_ , 17, 349–368.

Rubin, J., and N. Thygesen (1996): Monetary Union and the Outsiders: A CointegrationCodependence Analysis of Business Cycles in Europe: _Economie Appliquée_ , XLIX (3), 123– 171.

Breitung, J., and B. Candelon (2001): Is there a European Business Cycle? New Insights from a Frequency Domain Analysis, _Vierteljahrshaefte zur Wirtschaftsforschung_ , 70. Jahrgang, 3/2001, 331–338 19
in Spanish), they find that Nini rates have decreased since the 1960s. Moreover, they find that recessions increase the prevalence of Ninis by 3 to 4 percentage points (p.p.), and that the majority of Ninis are women whose personal lives have affected their schooling and labor force participation decision (e.g. 61 percent of women aged 19 to 23 have children). Notably, the authors conclude that Ninis are present in all levels of society and regions of the country, but that being a Nini does not necessarily mean being unproductive, as many of them are involved in activities that are socially useful.

Several authors have questioned the adequacy of the definition of Ninis and its implications for social policy precisely because of the assumed unproductivity of these youth under the definition. This assumed unproductivity of Ninis is clearly reflected in newspapers and public opinion as referenced by Arceo-Gómez and Campos-Vázquez (2011). Two papers, NegretePrieto and Leyva-Parra (2013) and Leyva-Parra and Negrete-Prieto (2014), carefully analyze the numbers of Ninis in a cross-section of Mexican youth aged 14 to 29 from the Mexican Labor Force Survey (ENOE by its acronym in Spanish), the authors show that different definitions for identifying Ninis yield groups of young people with very different characteristics. For example, using the traditional definition of Ninis, the majority (around 70 percent) of Ninis are women. However, this percentage reduces substantially when housework and searching for work are excluded from the definition of Nininess. Leyva-Parra and NegretePrieto (2014) also exploit the longitudinal characteristics of ENOE data to show that most Ninis change their status in every quarter for which they are observed in the survey.<sup>6</sup> Other authors have also called for a better understanding and measurement of Ninis, in particular with regards to housework, family composition, schooling decisions, and the differential policy responses that these may point to (Pederzini, 2011; Rivero and Perderzini, 2014). This research calls for a better understanding of decisions to participate in schooling, the labor market, and housework.

The objective of this paper is to contribute to this body of literature by empirically analyzing youth decisions to attend school, work in the labor market, do both, or neither of these, and their association with past decisions regarding time use, as well as youths’ income, immutable endowments, socioeconomic context, and demographic characteristics, as suggested by the theoretical work of Behrman et al. (2014). Studying the dynamic decision process of Ninis is

> 6 This is the first attempt of analyzing the longitudinal nature of ENOE data to study the dynamics of youth decisions. In our paper we exploit further this characteristic of the data.

5