rafmacalaba/datause-encoder-data huggingface dataset:
The need to protect vulnerable assets against natural disasters is apparent and one of the first steps towards achieving this is the understanding of how vulnerable assets are exposed to hazards. In this report, we make use of an accurate dataset to identify the number and locations of Chinese wastewater treatment plants exposed to changing probabilities flooding in the context of climate change, as well as those plants that are exposed to significant earthquakes. We limit wastewater network impacts to treatment plant facilities, ignoring any conveyance infrastructure, overflows, and pumping assets located upstream. Further to this analysis, we calculate the number of users each WWTP asset serves, according to population and usage data. Only direct outage, i.e. those caused directly by the hazard are considered. Outages caused by dependent infrastructures, such as to electricity networks supplying the WWTPs are not considered herein.

# **2. Methodology and Data**

To examine how China’s WWTPs are exposed to flooding and earthquakes, we intersect their spatial locations with hazard and susceptibility maps. We present detailed methodology below.

# **2.1. Wastewater Treatment Plants and Users**

We firstly assemble a data set for wastewater treatment works (2,743 assets) from the Chinese Ministry of the Environment (Ministry of the Environment, 2013). The dataset contains full name of the treatment plant, daily capacity, actual amount of wastewater treated and city information. As we do not know the exact location for each plant, we resort to a thorough process of searching plant addresses online and geocoding these in Google Earth and/or Baidu to our database. We then validate this dataset with Google Satellite Images to verify their exact locations. Unfortunately, the location information of several of these treatment plants is either missing or inaccurate, with sometimes the stated plants locations being kilometers away from their actual locations as seen by satellite imagery. As such, we were able to locate 1,346 WWTPs, out of the orginial number 2,743. It is noted that the assets are represented as points in the centroid of the WWTPs, as opposed to the locations of control rooms or elsewhere. In addition, we only focus on complete facilities rather than individual assets within the plants and do not consider exposure differences in treatment stages within a plant nor do we taken into account of the type/quality of outlets which may affect the extent to which WWPTs are impacted post‐disasters. Further, WWTPs not in current operation are not considered.

Using the known WWTPs in the supplied dataset, we further estimate the population dependent on the _w_<sup>th</sup> plant, _Cw_ , as: where V�,� is the daily volume treated in 10,000 m<sup>3</sup> for waste treatment plant _w_ in county _b_ ; _Vb_ is the total waste water treated for county _b_ ; and P� is the population of county _b_ . In some cases, we do not have population data for counties therefore our user sample of WWTPs is reduced to 891. _Vb_ is calculated by equation (2) as shown below, where we aggregate all the waste water treated in any county _b_ : 5
25

**Table 3. Oaxaca-Blinder Decomposition for Sri Lanka**

||1998|1999|2000|2001|2002|2008|2011|2012|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|overall|overall|overall|overall|overall|overall|overall|overall|
|male|8.570***|8.588***|8.577***|8.575***|8.536***|8.712***|8.856***|8.933***|
||(0.011)|(0.012)|(0.013)|(0.015)|(0.013)|(0.006)|(0.007)|(0.006)|
|female|8.294***|8.367***|8.366***|8.375***|8.324***|8.462***|8.609***|8.698***|
||(0.016)|(0.017)|(0.018)|(0.020)|(0.019)|(0.011)|(0.013)|(0.011)|
|difference|0.275***|0.221***|0.210***|0.201***|0.212***|0.249***|0.247***|0.235***|
||(0.020)|(0.021)|(0.022)|(0.026)|(0.023)|(0.012)|(0.015)|(0.013)|
|explained|0.051***|0.036***|0.040***|0.036***|0.018*|-0.005|0.004|-0.014*|
||(0.008)|(0.008)|(0.008)|(0.010)|(0.009)|(0.007)|(0.008)|(0.007)|
|unexplained|0.224***|0.186***|0.171***|0.165***|0.194***|0.254***|0.243***|0.249***|
||(0.018)|(0.019)|(0.020)|(0.024)|(0.021)|(0.010)|(0.012)|(0.011)|

_Source:_ Calculations based on Sri Lankan 1998–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note_ : Standard errors are in parentheses. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

**<u>Table 4. Oaxaca-Blinder Decomposition for Cambodia</u>**

||1996|1999|2004|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|overall|overall|overall|overall|overall|overall|overall|overall|
|male|11.498***|11.703***|11.850***|12.243***|12.156***|11.940***|12.326***|12.432***|
||(0.024)|(0.020)|(0.016)|(0.024)|(0.040)|(0.019)|(0.019)|(0.017)|
|female|11.388***|11.740***|11.727***|12.072***|12.032***|11.777***|12.043***|12.107***|
||(0.026)|(0.026)|(0.020)|(0.029)|(0.043)|(0.022)|(0.022)|(0.019)|
|difference|0.110***|-0.037|0.123***|0.172***|0.124**|0.164***|0.284***|0.325***|
||(0.036)|(0.033)|(0.025)|(0.038)|(0.059)|(0.029)|(0.029)|(0.025)|
|explained|0.068***|0.014|0.054***|0.115***|0.114***|0.083***|0.135***|0.122***|
||(0.013)|(0.014)|(0.011)|(0.019)|(0.031)|(0.016)|(0.016)|(0.015)|
|unexplained|0.041|-0.052|0.068***|0.057*|0.010|0.080***|0.149***|0.203***|
||(0.033)|(0.033)|(0.025)|(0.034)|(0.057)|(0.025)|(0.025)|(0.021)|

_Source:_ Calculations based on Cambodia Socio-Economic Surveys _Note_ : Standard errors are in parentheses. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
-23-

much harder for a country to achieve fiscal sustainability, at least in the short term, so that policies for internal and external balance need to be closely coordinated.

# **Fiscal Solvency and Sustainability in India**

58. India is also facing serious fiscal imbalances. The fiscal problems of India have been discussed in detail in various reports<sup>20</sup> and are analyzed here using the model presented above<sup>2</sup> l. There is no estimate of quasi-fiscal deficit available. Table 3 below presents the results of the model for the three fiscal years 1995/1996, 1996/1997, and 1997/1998 respectively<sup>22</sup> . The public sector encompasses the central government, state governments, and non-financial public enterprises. Detailed data used in the analysis are provided in Annex Table A2, summarized in Table 4 below. In 1997/1998, for instance, solvency for the public-sector would require a primary surplus of not less than 3.1 percent of GDP, but the actual primary balance was a deficit of 1.7 percent. The gap between the desired and actual figures, shown as fiscal solvency adjustment, rises quickly, indicating a more serious problem than the increase in the conventional fiscal deficit would suggest.

**Table 4: India: Public Sector Solvency and Sustainability (percentage of GDP)**

||**1995/96**|**1996/97**|**1997/98**|
|---|---|---|---|
|Public Sector Solvency||||
|Primary Balance|-0.9|-1.1|-1.7|
|Real GDP Growth|8.0|7.3|5.0|
|Domestic Debt|67.5|64.9|66.8|
|External Debt|30.9|30.3|30.5|
|Primary Balance for Solvency|-3.5|2.5|3.1|
|Public Sector Solvency Adjustment|-2.5|3.5|4.8|
|Public Sector Sustainability||||
|Grants|0.1|0.1|0.1|
|Sustainable Primary Balance|-2.2|2.9|1.6|
|Actual Central Government Primary<br>Balance|-0.9|-1.1|-1.7|
|Public sector Sustainability Adjustment|-1.2|4.0|3.3|
|Memorandum Items||||
|Conventional Deficit|-7.9|-7.8|-8.5|

_Source:_ World Bank staff calculation 20 See, for example, India Country Economic Memorandum, Report Number 15882-IN, August 8, 1996, Chapter 3.

21 The current version updated data from an earlier version of the paper, see Hinh Dinh (1995), but the empirical results are broadly in line with those, as well as with the India CEM (1996) and with the paper by Buiter (1994) although the last two papers used a slightly different methodology.

22 The Indian fiscal year runs from April I to March 31.
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Africa’s electricity and water subsidies are highly regressive**

<!-- Start of picture text --> a. Electricity b. Water<br>Nigeria Senegal<br>Gabon Cent. African Rep.<br>Gabon<br>Congo<br>Congo<br>Côte d'Ivoire Nigeria Kaduna<br>Cape Verde Togo<br>Togo Congo, Dem. Rep. of<br>São Tomé Nigeria FCT<br>Senegal Cameroon<br>Cameroon Côte d'Ivoire<br>Mozambique Niger<br>Ghana Chad<br>Cape Verde<br>Cent. African Rep. Burundi<br>Guinea<br>Malawi Blantyre<br>Burundi Ghana<br>Burkina Guinea<br>Chad Uganda<br>Malawi Malawi Lilongwe<br>Uganda Burkina<br>Rwanda Rwanda<br>0 0.5 1 0 0.2 0.4 0.6 0.8 1<br>Omega Omega<br><!-- End of picture text --> _Source:_ Banerjee, Wodon, and others 2008. _Note:_ Omega is a measure of distributional incidence that measures the share of subsidies received by the poor as a percentage of their share in the population. The higher the value of omega, the better the distributional performance of the subsidy. Values of omega below 1 denote a regressive subsidy; values of omega above 1 denote a progressive subsidy.

**Figure 14. Affordability in Cape Verde is much better than in other middle-income countries**

<!-- Start of picture text --> 100<br>95<br>90<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Cape Verde<br>middle-income countries<br>Average cost of power is $11.50/month per 50 kwh<br>Average cost of water is $19.44 month per 6 m3<br>Power cost-recovery tariff is $0.23/kwh<br>Water cost-recovery tariff is $3.24/m3<br>Source: Banerjee, Wodon, and others 2008.<br>Note: m3 =cubic meter; kwh = kilowatt-hour.<br>household budget<br>be less than 5 percent of the monthly<br>% of urban households whose utility bill would<br><!-- End of picture text --> 24
# **Tables and Figures**

## **Table 1: Summary of Responses to the Questions**

||Relatio<br>|ns with sup<br>|pliers(n=3,3<br>|50)<br>|||
|---|---|---|---|---|---|---|
||Not at all|Slightly|Moderately|Verymuch|Extremely|Total|
|Trust|8.3|8.0|14.4|41.8|27.6|100.0|
|Mutual interests|10.9|6.7|14.4|42.4|25.5|100.0|
|Paid private third-parties|61.9|15.4|13.9|5.6|3.1|100.0|
|Gov't officials|86.2|8.0|2.5|1.9|1.4|100.0|
|Other third-parties|81.5|12.5|3.8|1.7|0.4|100.0|
|Legal system|70.1|17.6|8.2|2.7|1.4|100.0|
||Relation<br>|s with cus<br>|tomers(n=3,3<br>|39)<br>|||
||Not at all|Slightly|Moderately|Very much|Extremely|Total|
|Trust|6.5|7.8|12.2|39.3|34.2|100.0|
|Mutual interests|9.0|7.1|11.5|42.3|30.0|100.0|
|Paid private third-parties|62.0|19.9|10.9|5.2|2.1|100.0|
|Gov't officials|89.5|6.2|2.3|1.2|0.8|100.0|
|Other third-parties|82.0|12.3|3.7|1.6|0.3|100.0|
|Legal system|72.5|16.1|7.6|2.4|1.5|100.0|

61
2 and (ii) attract investments by creating an enabling environment for private sector development. In the labor market policy arena, the paucity of reliable analyses of the labor market has resulted in an unfocused attention on employment generation and promotion (EGP) in both the Poverty Reduction Strategies (PRPSs) and the domestic policy debates.<sup>5</sup> Building on the work of Pontara (2007) and Rosanvallon (2006), this paper aims to redress the lack of information on the labor market in Mauritania – focusing in particular on gender issues, youth’s access to jobs, and earnings determinants. The aim is to provide policy makers with a starting analytical basis, which can be enriched by further analysis and work, as new data become available.

Section 1 briefly reviews the data sources for this analysis. Section 2 presents the data analysis of the main labor indicators, focusing on non-participation, unemployment, and employment patterns. Section 3 focuses on specific labor market issues including poverty, gender gaps, youth’s integration in the labor market, and earnings determination. The final section summarizes the results and highlights issues for further investigation.

> 5 According to Rosanvallon (2006), until 1997, statistics on the main labor market indicators were not collected in Mauritania. Historically, therefore, policy makers have had scant data to formulate a sound labor market policy. Data scarcity on the labor market is not something specific to Mauritania, but to Sub-Saharan Africa (SSA) as a whole. Sender et al. (2005) maintain that data on labor supply and employment are difficult to obtain in Africa, and are less reliable than other socio-economic indicators.
- Morten, Melanie and Jaqueline Oliveira (2023). “The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City”. _American Economic Journal: Applied Economics_ forthcoming.

- Obhara, Yoshinori (1974). “Brazilian Economic Development Since 1956: A Study ̄ on Policies Reflecting the Stages of Growth”. _The Developing Economies_ 12.2, pp. 133–154.

- Pereira, Luiz AG and Simone Narciso Lessa (2011). “O Processo de Planejamento e Desenvolvimento do Transporte Rodovi ́ario no Brasil”. _Caminhos de Geografia_ 12.40, pp. 26–46.

- Perez-Sebastian, Fidel and Jevgenijs Steinbuks (2017). _Public Infrastructure and Structural Transformation_ . Policy Research Working Paper 8285. World Bank.

- Perez-Sebastian, Fidel, Jevgenijs Steinbuks, Jos ́e F ́eres, and Ian Trotter (2020). _Electricity Access and Structural Transformation: Evidence from Brazil’s Electrification_ . Policy Research Working Paper 9182. World Bank.

- P ́erez-Sebasti ́an, Fidel, Rafael Serrano Quintero, and Jevgenijs Steinbuks (2023). _Spatial Misallocation of Complementary Infrastructure Investment: Evidence from Brazil_ . Policy Research Working Paper 10650. The World Bank.

- Raiser, Martin et al. (2017). _Back to Planning: How to Close Brazil’s Infrastructure Gap in Times of Austerity_ . Technical Report 117392-BR. World Bank.

- Sandoval, Daylyne Maerla Gomes Lima (2014). “An ́alise das Implica ̧c ̃oes Log ́ısticas, Con ́abeis e Financeiras da Escolha do Servi ̧co de Transporte”. PhD thesis. Universidade de Bras ́ılia, Faculdade de Tecnologia.

- Stock, James H and Jonathan H Wright (2000). “GMM with Weak Identification”. _Econometrica_ 68.5, pp. 1055–1096.

- Szerman, Dimitri, Juliano Assun ̧cao, Molly Lipscomb, and Ahmed Mushfiq Mobarak (2022). _Agricultural Productivity and Deforestation: Evidence from Brazil_ . Working Paper.

- Tobler, Waldo (1993). _Three Presentations on Geographical Analysis and Modeling_ . Technical Report.

- Vagliasindi, Maria and Nisan Gorgulu (2021). _What Have We Learned about the Effectiveness of Infrastructure Investment as a Fiscal Stimulus? A Literature Review_ . World Bank Policy Research Paper 9796. The World Bank.

- Van Beveren, Ilke (2012). “Total Factor Productivity Estimation: A Practical Review”. _Journal of Economic Surveys_ 26.1, pp. 98–128.

27
# **3.3. Fertilizer and Seed Subsidy Programs**

Fertilizer and seed subsidies are implemented under two separated programs, the Fertilizer Input Support Program (FISP) by the Ministry of Agriculture and Cooperatives (MACO) and the Food Security Pack Program (FSP) by the Ministry of Community Development and Social Services (MCDSS).<sup>11</sup>

# **3.3.1. Farmer Input Support Program**

The FISP was designed in 2002 and was originally called the Fertilizer Support Program. Its aim was to improve small-scale farmers‘ access to agricultural inputs and to enhance the participation and competitiveness of the private sector in the supply and distribution of inputs (MACO, 2009). The program was originally targeted at providing fertilizer and corn (maize) seed: each farmer was to receive a standard input package—sufficient for cultivation of at least one hectare of maize –consisting of eight 50kg bags of fertilizer and one 20kg bag of maize seed at 50 percent of the market price.<sup>12</sup> Private sector firms were to be brought in to the program through their participation in both the supply of inputs and the marketing of produce, but with little progress obtained thus far the government remains the single largest player in the market. Table 6 below shows the program‘s budget and the enrollment targets.

Table 6: Benefits and Beneficiaries of FISP

|Season|Budget (ZMK<br>Billion)|Fertilizer<br>(Metric Tons)|Seed (Metric<br>Tons)|Target number<br>of beneficiaries|
|---|---|---|---|---|
|2002/03|100|48,000|2,400|120,000|
|2003/04|114.5|60,000|3,000|150,000|
|2004/05|112.6|50,000|2,500|125,000|
|2005/06|140|50,000|2,500|125,000|
|2006/07|252|84,000|4,200|210,000|
|2007/08|150|50,000|2,500|125,000|
|2008/09|492|80,000|4,000|200,000|
|2009/10|430|100,000|5,000|530,000|
|2010/11|485|178,000|8,800||

_Source: Authors compilation from MACO and budget speeches_

> 11 As of September 2011 the Ministry of Agriculture and Cooperatives and the Ministry of Community Development and Social Services have been renamed the Ministry of Agriculture and Livestock and the Ministry of Community Development, Mother and Child Health, respectively.

> 12 MACO FISP Implementation manuals:  Each package includes 20kg of seeds, 4 50 kg bags of Compound D (basal) and 4 50 kg bags of Urea (top dressing).

13
countries covered in our analysis. Tables 1 and 2 present the key characteristics of our panel of firms. Given some of the heterogeneity related to the differences in country samples, implementation strategy, and timing of the surveys, we introduce different controls in the analysis. Unless stated otherwise, we include dummies for size (small, medium, and large based on employment), sector (four sector dummies), and country-fixed effects in the analysis. We weigh our results using the stratification weights available at the country level. We also weigh by the initial size of the firm to allow for differential weightage to firms of different sizes in the economy so that we track the aggregate evolution of the sales/employment variable more closely. Table 1 provides further details on panel firms that we use in our analysis.

**Table 1: Sample Characteristics (Number of Observations) in the Panel**

|||**Country **<br>|**name**<br>||
|---|---|---|---|---|
||Kyrgyzstan|Tajikistan|Uzbekistan|Total|
|**Sector**|||||
|Agriculture|141|141|81|363|
|Manufacturing|165|159|129|453|
|Retail|183|240|162|585|
|Other Services|183|195|231|609|
|**Size**|||||
|Micro (0-4)|318|273|243|834|
|Small (5-19)|246|312|231|789|
|Medium (20-99)|93|147|129|369|
|Large(100+)|15|3|0|18|
|**Wave**|||||
|Aug-Sept2020|224|245|201|670|
|May-July2021|224|245|201|670|
|March-May2022|224|245|201|670|

**Table 2: Descriptive Statistics for Sales and Employment**

||**Mean**|**p25**|**Median**|**p75**|**SD**|**Min**|**Max**|**N**|
|---|---|---|---|---|---|---|---|---|
|**Kyrgyzstan**|||||||||
|Log of sales|12.611|11.002|12.429|13.816|2.011|6.908|23.194|347|
|Log of employment|2.047|1.099|1.946|2.708|1.148|0|6.908|598|
|% change in sales during the past 30 days|-3.044|-30|0.000|20|47.486|-100|200|377|
|% change in employment in the last 30 days|94.986|-1.923|0.000|66.667|470.697|-100|6150|372|
|**Tajikistan**|||||||||
|Log of sales|10.732|9.393|10.463|11.875|1.849|6.908|16.907|468|
|Log of employment|2.121|1.386|2.079|2.89|1.038|0|5.193|677|
|% change in sales during the past 30 days|-3.769|-20|0.000|10|45.027|-100|200|424|
|% change in employment in the last 30 days|59.272|0|0.000|37.5|239.036|-92|3500|427|
|**Uzbekistan**|||||||||
|Log of sales|17.915|16.524|17.728|19.114|1.942|12.722|23.862|388|
|Log of employment|2.083|1.386|1.946|2.996|1.061|0|5.858|554|
|% change in sales during the past 30 days|4.181|-30|0.000|20|58.502|-100|200|341|
|% change in employment in the last 30 days|66.956|-16.667|0.000|39.231|247.435|-100|2900|344|

5
a proxy much older or much younger, matters most for younger workers’ labor statistics.

Labor statistics may also be affected by the differences in gender between proxies and subjects. We test whether these differences are significant by comparing male and female reports on the labor activities of women in the household (see Table 11b, columns M-F and column F-F), as well as female and male reporting on men in the household (F-M and M-M). We find that male proxies for women and female proxies for men both report greater LFP and weekly hours worked than same-gendered proxy respondents. A male proxy for a female within the household yield a LFP rate 12 percentage points higher and reports 5 more weekly hours worked than a female proxy. For a female proxy reporting on a male subject, the female proxy also reported a 13 percentage point difference in the LFP rate than a male proxy and 8 hours more worked per week. Given that proxy reporting is associated with lower LFP relative to self-reporting (as shown in Table 4), this implies that same gender proxies are further from self-reported LFP (for example, self-reported male LFP is 87 percent, while M- M proxy report is 65 percent and F-M is 78 percent, which is closer to the selfreported rate, a similar pattern holds for women). If we assume that reports from selfrespondents are closer to the “truth”, this suggests that opposite gender proxies do better. This is likely to be driven by the importance of the spousal relationship, since in 50 percent of the cases when proxy and respondent are from a different gender, they are spouses.<sup>18</sup> The result suggests that spouses may know better than others in the household about the employment status of their partner, although the differences

> 18 Of the proxy responses in the sample, 33 percent of responses are from male proxies reporting for female subjects, 34 percent of responses are from female proxies reporting on male subjects, 20 percent of responses are female proxies reporting on other females within the household, and 13 percent of responses are men reporting on other men in the household.

29
# FIGURE 4 Synchronization in output growth and inflation

A. Contribution of the global factor to output growth and inflation variation, 1970-2017 <!-- Start of picture text --> Percent Median<br>30 Mean<br>20<br>10<br>0<br>GDP Inflation GDP Inflation GDP Inflation<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> C. Contribution of the global factor to output growth and inflation variation: Advanced economies <!-- Start of picture text --> Percent Median<br>40<br>Mean<br>30<br>20<br>10<br>0<br>GDP Inflation GDP Inflation GDP Inflation<br>1970-85 1986-2000 2001-17<br><!-- End of picture text --> E. Contribution of global and group factors to output and inflation variation, over time B. Contribution of the global factor to output growth and inflation variation, over time <!-- Start of picture text --> Percent Median<br>40<br>Mean<br>30<br>20<br>10<br>0<br>GDP Inflation GDP Inflation GDP Inflation<br>1970-85 1986-2000 2001-17<br><!-- End of picture text --> D. Contribution of the global factor to output growth and inflation variation: EMDEs <!-- Start of picture text --> Percent Median<br>30<br>Mean<br>20<br>10<br>0<br>GDP Inflation GDP Inflation GDP Inflation<br>1970-85 1986-2000 2001-17<br><!-- End of picture text --> F. Contribution of global and group factors to output and inflation variation, by country group <!-- Start of picture text --> Percent Group<br>40<br>Global<br>30<br>20<br>10<br>0<br>GDP Inflation<br>Full 1970-85 1986-2000 2001-17 Full 1970-85 1986-2000 2001-17<br><!-- End of picture text --> <!-- Start of picture text --> Percent Group<br>40<br>Global<br>30<br>20<br>10<br>0<br>All AEs EMDEs All AEs EMDEs<br>GDP Inflation<br><!-- End of picture text --> Notes: Contribution of global and group factors to the variance of real output growth and inflation in 99 economies (25 AEs and 74 EMDEs, including 16 low-income countries), based on a two-factor dynamic factor model. AEs = advanced economies; EMDEs = emerging market and developing economies; GDP = gross domestic product.

20