rafmacalaba/datause-encoder-data huggingface dataset:
- an excess prevalence of disease,

- an excess intensity of infection, and/or

- further transmission of disease.

“Incidence” refers to the number of new cases of a disease in a community in a specified period of time (usually a week, month, or year), and “prevalence” refers to the number of cases of a disease in a community at a given point in time; incidence is normally used for acute diseases (e.g., diarrheal disease) and prevalence for chronic diseases (helminthiases). In the present context of wastewater use in agriculture, if fieldworkers and/or crop consumers have an excess incidence or prevalence of a disease, or an excess intensity of infection (e.g., a higher worm burden as shown by the number of helminth eggs per gram of feces), compared with a control group, then these excesses are attributable to the use of wastewater in agriculture, which is therefore an issue of public health importance—and this is the only way that this importance can be ascertained.

Shuval _et al_ . (1986) showed that, when untreated wastewater was used for irrigation, there was an excess prevalence (and also an excess intensity of infection) of ascariasis and hookworm disease in fieldworkers compared with a control group (Figure 3.1), but not when treated wastewater was used. Blumenthal and Peasey (2002) confirmed this for both geohelminthic and bacterial diseases.

## _Approaches to microbial risk control_

There have been three basic approaches to the control of microbial risks:

- Approaches based on potential risks, such as the very strict Californian standards and the even stricter US EPA/USAID guidelines (State of California, 2001; US EPA/USAID, 2004);

- Approaches based on actual risks, such as the 1989 WHO Guidelines (WHO, 1989); and

- Approaches based on quantitative microbial risk analysis (QMRA), such as the Australian National Guidelines (EPHC/NRMMC/AHMC, 2006) and the 2006 WHO Guidelines (WHO, 2006a) discussed in Chapter 4.

The currently recommended approach is that based on QMRA as it provides greater flexibility in risk management. Risk reduction, for example, can be achieved not just by wastewater treatment, but also by including post-treatment health-protection control measures. This approach also reduces costs. More details are provided in Chapter 4 and Annex A.

# _3.1.2     Chemical risks_

## _Chemical risks to human health_

Health risks from chemicals are caused by heavy metals (e.g., cadmium, lead, and mercury) and many organic compounds (e.g., pesticides). These mostly derive from industrial wastewaters and, if these are discharged to public sewers, they are present in municipal wastewaters. The health effects of prolonged exposure to many of these chemicals are well known (e.g., cancers).<sup>13</sup> There is an emerging class of chemical contaminants, the so-called ‘anthropogenic’ compounds, which include pharmaceuticals, hormones and endrocrine disruptors, antimicrobials and antibiotics, and personal care products, the long-term health effects of which are less clearly understood (see Bhandari _et al_ ., 2009).

> 13 A useful summary is given in WHO (2008c).

27
**Trucking costs and the margins of internal trade: Evidence from a Trucking Portal in India**<sup>**1**</sup>

Somik Lall, Sutirtha Sinha-Roy, Forhad Shilpi World Bank

**Key Words** : Trucking cost, Trade Elasticity, Spatial Margin of Trade, COVID-19, Network Externality **JEL Codes:** R4, R1, O1

> 1 We would like to acknowledge Muneeza Alam, Arnab Bandyopadhyay, Fuad Malkawi, Catalina Marulanda, Alejandro Molnar, and Shomik Mehndiratta for their comments and discussions. The project received generous financial support from the South Asia Regional Trade Facilitation Program **(** TF0B0631). The authors are responsible for all remaining errors.
Policy Research Working Paper 6985

# **Abstract**

This paper studies the optimal transition from existing coal power plants to gas and renewable power under a carbon budget. It solves a model of polluting, exhaustible resources with capacity constraints and adjustment costs (to build coal, gas, and renewable power plants). It finds that optimal investment in renewable energy may start before coal power has been phased out and even before investment in gas has started, because doing so allows for smoothing investment over time and reduces adjustment costs. Gas plants may be used to reduce short-term investment in renewable power and associated costs, but must eventually be phased out to allow room for carbon-free power. One risk for myopic agents comparing gas and renewable investment is thus to overestimate the lifetime of gas plants—e.g., when computing the levelized cost of electricity—and be biased against renewable power. These analytical results are quantified with numerical simulations of the European Commission’s 2050 energy roadmap.

This paper is a product of the Office of the Chief Economist, Climate Change Group. 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 oskar.lecuyer@vwi.unibe.ch and avogtschilb@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
**Table 2. Correlation matrix of volatility indicators**

||Equity|Credit|Currency|Commodities|
|---|---|---|---|---|
|Equity|1||||
|Credit|0.74|1|||
|Currency|0.80|0.60|1||
|Commodities|0.77|0.65|0.76|1|
|Source: Bloomberg|||||

The overall market volatility index is derived as the common factor in a factor analysis of eight variables spanning equity (VIX); currency (rolling standard deviations of daily changes in USD/Euro, USD/Yen, USD/Sterling; commodities (rolling standard deviations of three broad sets of commodity prices: agriculture, energy, and industrial metals prices); and credit (TED).

**Figure 3. Indicators of wealth decline**

<!-- Start of picture text --> U.S. equity wealth (2004-09 in trillions of USD )<br>20<br>18<br>16<br>14<br>12<br>US total market cap<br>10<br>8<br>Apr-04 Apr-05 Apr-06 Apr-07 Apr-08 Apr-09<br>MSCI world index - developed countries(2006-09)<br>1700<br>1600<br>1500<br>1400<br>1300<br>1200<br>1100<br>1000<br>MSCI world<br>900<br>20-day moving average<br>800<br>700<br>Apr-06 Jan-07 Oct-07 Jul-08 Apr-09<br><!-- End of picture text --> <!-- Start of picture text --> MSCI Index of emerging markets (2006-09)<br>1400<br>1300<br>1200<br>1100<br>1000<br>900<br>800<br>700<br>MSCI Emerging Markets<br>600<br>20-day moving average<br>500<br>400<br>Apr-06 Jan-07 Oct-07 Jul-08 Apr-09<br><!-- End of picture text --> Source: Bloomberg Note: The MSCI EM index includes Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Israel, South Korea, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, South Africa, Taiwan, Thailand and Turkey.

The MSCI WORLD index includes Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, HK, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, United Kingdom and USA.

9
Although these measures do help predict levels of employment, Table 6 shows there is very limited heterogeneity of treatment impacts with respect to any of them. Individuals who have previously taken a training course before, and those who are the main decision-makers about work, have larger impacts of the training according to our survey outcomes. However, the significance of neither variable survives corrections for multiple hypothesis testing, nor do we see significant impacts of these variables on the administrative measures of employment. The most statistically significant coefficient is that on tenacity, which has a negative impact on the training effect on formal employment in August 2013. This could reflect that tenacious individuals will find employment anyway, so that training substitutes for a lack of this characteristic. However, the p-value of 0.010 is not small enough to survive a correction for testing heterogeneity across so many different measures.

# **5.4 Impacts on Well-being**

Our survey measured impacts approximately one year after the completion of training. This is similar to the timing in other experimental vocational training evaluations in developing countries, and should allow long enough for most individuals to use their training in finding a job. Nevertheless, the concern remains that perhaps some of the impacts will take time to manifest. If that is the case, we should expect to see that training leads individuals to think their future job prospects and future household well-being will be better, even if there is relatively little change in current well-being. We test this in Table 7.

While 42 percent of the control group is currently working at all at the time of the follow-up survey, column 1 shows that 54.1 percent expect to be employed in two years’ time. However, there is no significant impact of treatment on this expectation. Column 2 shows that training does not improve mental health. Column 3 shows a marginally significant increase in current subjective well-being, although the impact is very small in magnitude: 0.07 steps on a 10 stepladder, equivalent to 0.04 standard deviations. While control applicants do believe that their households will be on a higher step in five years than they are today, treatment has no additional impact on this. Treated individuals also do not have significantly higher household income, but they do have significantly higher durable asset levels. Again the magnitude of the impact is small, equivalent to 0.06 standard deviations.

17
**Table 11:** Dynamic effects of ’Visa’ provision: Dechaisemartin & D’Haultfoeuille estimator

|Dep var:||Bil. Mig.|stock (ln)|||
|---|---|---|---|---|---|
|Time|_t_0|_t_+1|_t_+2|_t_+3|_t_+4|
|Visa|0.041**|0.130***|-0.022|-0.199|-0.180|
||(0.024)|(0.052)|(0.100)|(0.155)|(0.126)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Country-Pair Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|1990-2020|1990-2020|
|Observations|6,846|5,295|3,784|2,348|2,232|
|Switchers|242|215|96|66|66|

Note: Controls includes the log of GDP difference, RTA and Schenghen. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.

32
2 In Kiguel and Liviatan (1992), _both_ the fixing of the exchange rate _and_ lack of credibility in its efficacy constitute necessary conditions for the emergence of a business cycle driven by speculative demand. This paper extends their sample of ERBS in an attempt to compare the experience of chronic inflation countries to that of low inflation countries. We thus take three European stabilizations of the 1980's that used the exchange rate as a nominal anchor: Denmark '82, Ireland '82 and France '83.' Our starting point is the observation that the behavior of real variables during disinflation in the latter group has not always fitted the pattern described in Kiguel and Liviatan (1992). On the surface, the experience of Denmark looks similar to that of chronic inflation countries. There, the fixing of the exchange rate was followed by expanding output and falling unemployment. Instead, Irelad '82 and France '83 fit the classical recessionary scenario. We compare the Latin American and European experiences below.

Our main argument is that, to a large extent, the differences in outcomes are related to expectations about how the disinflation program would proceed. Credibility played in the outcomes of the ERBSs in our sample.<sup>5</sup> We distinguish two types of credibility problems: (i) expectations of a total collapse of the plan with a future reversal of policies; and (ii) expectations that the program will eventually succeed, but that disinflation will proceed at a different _pace_ than the one announced. Expectations about the pace could be either optimistic or pessimistic. We argue that the first concept is more relevant to the high inflation economies of Latin America, while the second one is better suited to understand disinflation in the European economies. In Latin America, the boom was fueled by the I There was a second Irish program in 1986. Of the two programs we only analyze the first, as that was the only one in which inflation was a concern.

S Our approach should perhaps be seen as complementary to that of Giavazzi and Pagano (1990), that also tries to explain the contrasting experiences of Denmark and France during stabilization. These authors emphasize the fiscal aspects of the programs implemented in those countries and explain the aforementioned differences by subscribing to the "German view" on budget cutting. According to this view, fiscal consolidation has a benign impact on expectations if it is accomplished by cutting expenditures. According to Giavazzi and Pagano, Denmark's fiscal consolidation was achieved by cutting expenditures. This explains the Danish expansion.
Cameron, S., S. Chaudhuri, and J. McLaren (2007). “`Trade Shocks and Labor Adjustment: Theory,”' NBER Working Paper 13463.

Chaudhuri, S. and J. McLaren (2007). ``Some Simple Analytics of Trade and Labor Mobility.'' NBER Working Paper 13464.

Cooper, R. and J. Haltiwanger (2006). ``On the Nature of Capital Adjustment Costs,'' Review of Economic Studies, 73(3), pp. 611--633.

Cosar, A. K. (2010). ``Adjusting to Trade Liberalization: Reallocation and Labor Market Policies,'' CEPS Working Paper 2010.

Davidson, Carl and Steven J. Matusz (2000). ``Globalization and Labour-Market Adjustment: How Fast and At What Cost?'' Oxford Review of Economic Policy, 16: 42-56.

Davidson, Carl and Steven J. Matusz (2002). ``Globalisation, Employment and Income: Analysing the Adjustment Process,'' in Trade, Investment, Migration and Labour Market Adjustment, David Greenaway, Richard Upward, and Katharine Wakelin, eds., Palgrave, Macmillan.

Davidson, Carl and Steven J. Matusz (2004a). ``An Overlapping-generations Model of Escape Clause Protection,'' Review of International Economics, 12: 749-768.

Davidson, Carl and Steven J. Matusz (2004b). International Trade and Labor Markets: Theory, Evidence, and Policy Implications, W.E. Upjohn Institute for Employment Research.

Davidson, Carl and Steven J. Matusz (2004c). ``Should Policy Makers Be Concerned About Adjustment Costs?'' in The Political Economy of Trade, Aid and Foreign Investment Policies, Devashish Mitra and Arvind Panagariya, eds. Elsevier.

Davidson, Carl and Steven J. Matusz (2006a). ``Long-Run Lunacy, Short-Run Sanity: a Simple Model of Trade with Labor Market Turnover,'' Review of International Economics, 14: 261-276.

Davidson, Carl and Steven J. Matusz (2006b). ``Trade Liberalization and Compensation," International Economic Review,'' 47: 723-747.

Davidson, Carl and Steven J. Matusz (2010). ``Modeling, Measuring, and Compensating the Adjustment Costs Associated with Trade Reforms,'' in G. Porto and B. Hoekman (2010).

de Melo, Jaime and David Tarr (1990). “Welfare Costs of U.S. Quotas in Textiles, Steel and Autos,” Review of Economics and Statistics, 72: 489-497.

Dix-Carneiro, R. (2010). ``Trade Liberalization and Labor Market Dynamics,'' mimeo Princeton University.

Francois, J., M. Jansen, and R. Peters (2011). “Trade, Adjustment Costs and Assistance: The Labour Market Dynamics,” mimeo ILO.

Freund, C. and M. Pierola (2010). “Export Entrepreneurs : Evidence from Peru," Policy Research Working Paper Series 5407, The World Bank.

Galiani, S. and G. Porto (2010). “Trends in Tariff Reforms and in the Structure of Wages,” The Review of Economics and Statistics, vol. 92, No. 3, pp. 482-494.

30
<!-- Start of picture text --> aTotal Aggregation - Labor Productivity 002-2010)<br>eo<br>mL<br>a A<br>[efaBa) wleemee-“ “ |<br>a a<br>bya - oea<br>Ho = gt [a<br>: - ated a—<br>q -—r<br>Hl tk<br>Ga 65 Log of Latergz Productihy7 Ir 2002 TZ Te<br><!-- End of picture text -->

<!-- Start of picture text --> Labor Productivity - Agriculture<br>3<br>@aAP AM<br>£3 @Pl eco<br>& ; ese @Es eto eT<br>3ssiy IER@BA @RJ ergeBR @DFgc<br>ao] @RS<br>gi ece PB @PA<br>&5e_ @AL ems<br>ePE @RN esc<br>5.4 5.6 5.8 6 6.2<br>Log of Labor Productivity in 2002<br><!-- End of picture text --> <!-- Start of picture text --> A Labor Productivity- Manufacturing<br>eo<br>cc eco<br># |<br>= - badd<br>a See ee<br>fq ao<br>= al —<br>H a ae<br>Hs aFh<br>: ° me—<— ye<br>gee tial<br>ac ae<br>a aFn<br>55 q 65 T<br>Logof Labor Produciudby irr 2000<br><!-- End of picture text -->

<!-- Start of picture text --> Total Aggregationon- Labor Productivity 2010-2015)<br>S| aco<br>[2]a_payee 7os“— wn “_ -<br>i —, __<br>bya -wane eesc ee |e<br>| — ogee” Cal ——__<br>| a a _<br>Pind<br>5<br>as Log of Later Produciul7 yin 200 1S<br><!-- End of picture text -->

<!-- Start of picture text --> Labor Productivity - Extractyes<br>F ani.<br>eel<br>4<br>i =<br>Fz= aa ee Sal “=<br>~ _<br>g4 i<br>i ar reae 4c:ae— rr<br>a ai eH aFo.ai—ie<br>5 6 7 a<br>Log of Labor Prodceciul kt In 200<br><!-- End of picture text --> <!-- Start of picture text --> A Labor Productivity- Senices<br>ett<br>mg -<br>ra aco<br>s_<br>PF ords,, -<br>a |e 7 we<br>a a er<br>= | Paine re<br>H bate an<br>5: = - a,a .<br>a “— a<br>a a 7 ae<br>' ao<br>ae 6 L o f azLaborg ProduciudT by irr 2000 TZ Ta<br><!-- End of picture text -->
discrimination observed in the DRC. Section 5 presents the empirical approach and the results; finally, section 6 concludes.

# **2. Context: The Democratic Republic of Congo**

The DRC is one of the poorest countries in the world; it is the fourth most populous (67.5 million inhabitants) and the largest country in terms of land area in Sub-Saharan Africa. In 2012, the time period covered by the survey, it had an estimated GNI per capita of US$335.48 in constant 2010 US$ and was ranked 176 of 189 economies in the UNDP’s Human Development Index. Fertility rates were quite high at 6.4 births per woman and gross secondary school enrollment was still low at 43.3 percent. The economy suffers from low levels of human capital. The recent World Bank’s Human Capital Index ranked the DRC 146<sup>th</sup> of 157 economies in 2017.<sup>7</sup> The score for the DRC was 0.37, implying that the productivity as a future worker for a child born in 2017 in the DRC is 63 percent below what could be achieved with full health and education. Both the human capital index and primary school enrollment rates are similar between men and women in the DRC. However, in 2012 secondary school enrollment rates were much higher for males than females (54.2 percent versus 32.1 percent), and tertiary school enrollment for males was almost double that of females (10.6 percent versus 5.9 percent).<sup>8</sup> Women are as likely as men to work, but they are over-represented in the agricultural sector. Women made up 48 percent of the labor force all throughout 2012-2018. However, in 2012, 80 percent were employed in agriculture, 4 percent in industry, and 16 percent in services.<sup>9</sup> Informal employment is quite high with the most recent available estimate in 2005 of 89.6 percent of total employment (ILO); estimates of informal output are 45.9 percent of GDP in 2012 (Medina and Schneider, 2018). However, despite the prevalence of informality, a significant number of firms are registered with the government authorities (about 14,000 firms), and thus formal according to the a legalistic definition of informality.<sup>10</sup> In 2012, only 23 percent of firms in the manufacturing and services formal sectors had female top managers and only 22 percent of the firms had a majority female ownership.

> 7 Data are not available for previous years.

> 8 The figures have hardly changed since 2012. Data are from the World Development Indicators, World Bank. 9 In 2018 these figures were:  agriculture (87 percent), industry (9 percent), services (4 percent). Source: World Development Indicators, World Bank.

> 10 The data about formal firms operating in the DRC are from a block enumeration conducted in the country prior to the 2013 Enterprise Survey. Block enumeration was needed to build the sampling frame for the data collection in absence of comprehensive and updated lists of formal firms operating in the DRC (e.g. census data or data from firm’s registrar office).

5
|Satisfied with transportation infrastructure|-0.04|0.02|-0.06|0.04|0.11|-0.12|-0.23**|0.23|-0.12|0.12|
|---|---|---|---|---|---|---|---|---|---|---|
|Satisfied with educational system and schools|-0.03|0.17|0.04|0.09|0.09|0.05|0.15*|-0.00|0.01|0.20*|
|Satisfied with quality of air|0.02|-0.07|-0.09|-0.02|-0.05|-0.01|0.03|-0.12|0.02|-0.09|
|Satisfied with quality of water|0.13|-0.01|0.02|0.10|0.10|0.01|0.08|0.04|0.04|0.07|
|Satisfied with health care|0.25*|-0.00|0.17*|0.17|0.17|0.18*|0.18*|0.15|0.14|0.24*|
|Corruption|0.02|-0.10|-0.06|-0.03|0.01|-0.08|0.03|-0.09|-0.04|0.00|
|Confidence in national institutions|0.26**|0.04|0.08|0.20|0.13|0.19|0.38**|-0.05|0.22|0.03|
|Approval of county’s leadership|0.19|0.07|0.07|0.16|0.25|0.05|0.01|0.24*|0.13|0.13|
|Satisfied with personal freedom|-0.06|0.14**|0.05|0.05|0.03|0.08|0.06|0.07|0.06|0.04|
|Social mobility is possible|-0.08|0.17*|0.24|-0.08|-0.10|0.15*|-0.02|0.10|0.12|-0.02|
|Regional GDP per capita|0.01|0.00|0.00|0.01|0.01|0.00|0.00|0.02|0.01|-0.00|
|Regional unemployment rate|0.02|0.00|0.01|0.01|0.01|0.01|-0.00|0.04|0.05**|-0.02|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|4,302|5,926|4,907|5,321|4,135|6,093|5,320|4,908|5,802|4,426|
|R<sup>2</sup>|0.201|0.182|0.201|0.180|0.180|0.181|0.207|0.178|0.196|0.189|

_Notes:_ * p<0.05, ** p<0.01. Unstandardized coefficients reported. The grey shaded cells indicate that the coefficients significantly differ between the two compared groups for pairs of coefficients (at 10% level) of which at least one of the coefficients is statistically significant.

21