rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6. Mechanism: the impact of the program on households’ access to credit</u>**

||||Dependen<br>|t variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Borrow from<br>formal sources<br>(yes=1, no=0)|Borrow from<br>microcredit<br>sources<br>(yes=1, no=0)|Log of formal<br>loan (the<br>sample of<br>borrowing<br>households)|Log of<br>microcredit<br>(the sample of<br>borrowing<br>households)|Log of formal<br>loan (the<br>sample all<br>households)|Log of<br>microcredit<br>(the sample all<br>households)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|0.1353**|0.0903*|0.0047|-0.1188|1.1861**|0.9366*|
||(0.0657)|(0.0509)|(0.1413)|(0.0966)|(0.5540)|(0.4879)|
|(Poverty rate – 50) * Post-program|-0.0146**|-0.0126**|0.0062|0.0028|-0.1005*|-0.1304**|
|period|(0.0066)|(0.0057)|(0.0161)|(0.0121)|(0.0571)|(0.0545)|
|Program * (Poverty rate – 50) *|0.0119*|0.0129**|-0.0126|0.0019|0.0793|0.1346**|
|Post-program period|(0.0071)|(0.0062)|(0.0168)|(0.0126)|(0.0606)|(0.0596)|
|Age of household heads|0.0075***<br>(0.0017)|0.0029**<br>(0.0012)|0.0236***<br>(0.0058)|0.0156***<br>(0.0050)|0.0697***<br>(0.0127)|0.0323***<br>(0.0121)|
||-0.0001***|-0.0001***|-0.0002***|-0.0002***|-0.0010***|-0.0006***|
|Age squared of household heads|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0248**|0.0001|0.1504***|0.0609**|0.2377**|0.0068|
|female=0)|(0.0124)|(0.0098)|(0.0339)|(0.0241)|(0.0971)|(0.0974)|
|hi iii ih|-0.0199|0.0662***|-0.5917***|-0.2038***|-0.4173**|0.6141***|
|Etnc mnortes (yes=1, Kn=0)|(0.0222)|(0.0160)|(0.0656)|(0.0302)|(0.1848)|(0.1577)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2244***|0.0813*|9.1356***|8.8530***|2.1609***|0.6479|
||(0.0459)|(0.0411)|(0.1432)|(0.1182)|(0.3486)|(0.4011)|
|Observations|30,556|40,468|11,978|11,382|30,556|40,468|
|R-squared|0.101|0.104|0.349|0.326|0.204|0.107|
|The mean value of the dependent<br>variables before the program|0.201|0.183|9.629|3.693|9.326|1.705|

Note: This table reports district fixed-effect RDD-DD regressions of loans of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

45
| POLICY RESEARCH WORKING PAPER 2446

# Summary findings

Forecasting has long been a challenge and will remain so for the foreseeable future. But the analytical instrumetits and data processing capabilities available through the latest technology and software should allow much better forecasting than transport ministries or regulatory agencies typically observe.

Privatization brings new needs for demand forecasting. More attention is paid to risk under privatization thaln when investments are publicly financed. And regulators must be able to judge traffic studies done by operators and to learn what strategic behavior influenced these studies.

Many governments and regulators avoid good demand modeling out of lack of conviction that theory and models can do better than the "old hands" of the sector. This is dangerous when privatization changes the nature of business.

For projects amounting to investments of $100-200 million, a cost of $100,000-200,000 is not a reason to reject a reasonable modeling effort. And some private forecasting firms are willing to sell guarantees or insurance with their forecasts to cover significant gaps between forecasts and reality.

This paper-a product of the Governance, Regulation, and Finance Division, World Bank Institute-is part of a larger effort in the institute to increase understanding of infrastructure regulation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Pl¢ase contact Gabriela Chenet-Smith, room J3-147, telephone 202473-6370, fax 202-676-9874, email address gchenet@worldbank.org. Policy Research Working Papers are also posted on the Web atwww.worldbank.org/research/workingpapers. Antonio Estache may be contacted at aestachecLworldbank.org. September 2000. (35 pages) _The Policy Research Working Paper Series disseminates the findings of work itt progress to encourage the exchange of ideas about development issues. An objective of the series is to get the fihdings 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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 12

|High income (> $12,615)|1:200|1:50<br>no protection|
|---|---|---|
|Special case: Netherlands|1:10,000||
|Special case: 136 large coastal cities|taken from H|allegatte et al. (2013)|

Subsequently, protection levels evolve at five-year time steps following the Adaptation Strategies outlined below.

# **3.7 Adaptation strategies**

In this report, we assess the costs for the following five adaptation strategies:

1. **Constant Protection Levels** -- maintain current protection levels as defined in Table 4. As population and GDP change with time in the socio-economic scenario, so the length and standard of protection will increase. Once an area is protected, defenses are maintained to 2100.

2. **Constant absolute flood risk** -- maintain average annual losses for protected areas as defined under Strategy 1 (similar to Hallegatte et al., 2013). This strategy raises the protection level with both rising sea levels and socio-economic development (population, GDP) in order to maintain the current (2015) flood risk level constant in monetary terms.

3. **Constant relative flood risk** -- maintain relative average annual losses for protected areas as defined under Strategy 1 **.** This strategy raises protection levels with both rising sea levels as well as socio-economic development in order to maintain the current flood risk constant in terms of percentage of local GDP (considered to be a socially acceptable loss). By local GDP we refer to the fraction of GDP that is produced within the low elevation coastal zone (LECZ -- which is the area below 10 m elevation) associated to a coast-line segment.

4. **Risk intolerance** -- keep relative average annual losses below 0.01% percent of local for protected areas as defined under Strategy 1. The GDP threshold of 0.01% is based on the losses in the cities of Amsterdam and Rotterdam in 2005 as calculated by Hallegatte et al. (2013) and applies this Dutch standard as a risk intolerant world.

5. **Cost-benefit analysis (CBA)** -- compute the optimum protection level minimizing the sum of residual flood damage to assets and protection costs to 2100, including capital and maintenance costs. This follows the methods of Lincke and Hinkel (2018).

Strategies 1 to 4 are scenarios of how protection might be applied globally and are based on expert judgement, while Strategy 5 is based on economic optimization and balancing the costs of protection to the avoided damages. Under Strategy 1, the defenses are raised with relative sea-level rise, while under the other strategies, the defenses are generally raised more than the rise in sea level. Strategies 1 to 3 take a positive approach, while Strategy 4 takes a normative approach which allows us to consider the adaptation deficit, relative to Strategies 1 to 3. The adaptation deficit can only be assessed with respect to a normative assumption as to what is desirable -- here we ask what is required to give all protected areas following Table 4 the same level of safety as Amsterdam and Rotterdam. Similar analysis can be conducted with Adaptation Strategy 5.
Rubashkina, Y., Galeotti, M., & Verdolini, E. (2015). Environmental regulation and competitiveness: Empirical evidence on the Porter Hypothesis from European manufacturing sectors. _Energy Policy_ , 83, 288-300. <u>https://doi.org/10.1016/j.enpol.2015.02.014</u> Ruffini, E., & Wei, M. (2018). Future costs of fuel cell electric vehicles in California using a learning rate approach. _Energy_ , 150, 329-341. <u>https://doi.org/10.1016/j.energy.2018.02.071</u> Saha, S., Nielsen, I., & Moon, I. (2017). Optimal retailer investments in green operations and preservation technology for deteriorating items. _Journal of Cleaner Production_ , 140, 15141527. https://doi.org/10.1016/j.jclepro.2016.09.229 Schulte, R. H., & Fletcher, F. C. (2021). Why individual electric utilities cannot achieve 100 % clean energy. _The Electricity Journal_ , 34(2), 106909. <u>https://doi.org/10.1016/j.tej.2020.106909</u> Shapiro, J. S., & Walker, R. (2018). Why Is Pollution from US Manufacturing Declining? The Roles of Environmental Regulation, Productivity, and Trade. _American Economic Review_ , _108_ (12), 3814-3854. https://doi.org/10.1257/aer.20151272 Simmons, R. A., Shaver, G. M., Tyner, W. E., & Garimella, S. V. (2015). A benefit-cost assessment of new vehicle technologies and fuel economy in the U.S. market. _Applied Energy_ , 157, 940-952. https://doi.org/10.1016/j.apenergy.2015.01.068 Sohag, K., Chukavina, K., & Samargandi, N. (2021). Renewable energy and total factor productivity in OECD member countries. _Journal of Cleaner Production_ , _296_ , 126499. <u>https://doi.org/10.1016/j.jclepro.2021.126499</u> Steckel, J. C., & Jakob, M. (2018). The role of financing cost and de-risking strategies for clean energy investment. _International Economics_ , 155, 19-28. <u>https://doi.org/10.1016/j.inteco.2018.02.003</u> Stucki, T. (2019). Which firms benefit from investments in green energy technologies? – The effect of energy costs. _Research Policy_ , _48_ (3), 546-555. <u>https://doi.org/10.1016/j.respol.2018.09.010</u> Suástegui Macías, J. A., Pérez Tello, C., Acuña Ramírez, A., Lambert Arista, A. A., Magaña Almaguer, H. D., Rosales Escobedo, P. F., & Ruelas Puente, A. H. (2018). Assessment of electrical saving from energy efficiency programs in the residential sector in Mexicali, 53
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Another interesting finding is that this scale effect varies over the two different periods. Tables 13 and 14 report that for the pulp and paper, cement, and aluminum industries, firms had larger scale economies over the period 2002-2004 than over the period 1999-2001.

# **VII. Robustness tests**

To test the robustness of our results, we conduct a number of variations on our original estimation. Our previous estimations were conducted on a balanced dataset, which comprises firm-level data over the period 1999-2004. Using a balanced data set allows us to construct stocks of technology development which, due to difficulties associated with determining the correct time lag for flows of technology development, are better to use than flows. The shortcoming of using the balanced data set is that we lose many observations when we move from the unbalanced to the balanced data set. This results in a much smaller sample size which could affect estimation power and lead to sample selection bias since the only firms in the data set are those firms reporting in all six years.

To see how our use of a balanced data set affects the results, we run the regression on an unbalanced data set. The unbalanced data set is constructed by dropping firms that only have one year of observations over the period 1997-2004 in the original unbalanced data set. (Most firms with only one year of observations are reporting in year 2004 since this is a census year). However, since it is not the case that each firm in the unbalanced data set reports continuously over the period 1997-2004, we are not able to construct technology development stocks for each firm and we are therefore forced to use contemporaneous flows of technology development expenditures rather than a stock. This obviously raises possible endogeneity issues when using 25
# Figure 1. MODEL REPRESENTATION OF A COMMODITY MARKET

<!-- Start of picture text --> CoDmmodity<br>G emand .<br>S Ce:modit C.Lmso _ I sup~~~~~~)PITCommodity M sfoF<br>f xternal ><br>influences on Poucth<br>\ supply J Ja<br><!-- End of picture text --> Source: W. C. Labys and P. Pollak. Commodity Models for Forecastin-e and Policy Analysis. London: Croom-Helm Publishing Co., 1984.

5
**Table 2f.  Sector Value Added in SADC  (Percentage unless otherwise indicated)**

||<br>La|<br>bor|||<br>G|<br>DP|
|---|---|---|---|---|---|---|
||Skilled<br>labor|Unskilled<br>labor|Capital|Natural<br>Resources|USD in<br>billion|% of total|
|**Business Services**|||||||
|Air Transport|11.9|49.2|38.9|0.0|1.6|0.5|
|Communication|18.2|25.9|55.9|0.0|7.6|2.2|
|Insurance|24.3|34.3|41.4|0.0|12.7|3.7|
|Business Services nec|20.9|29.7|49.4|0.0|23.8|6.9|
|Financial Services nec|24.1|34.4|41.5|0.0|5.9|1.7|
|Transport nec|12.5|51.4|36.1|0.0|7.8|2.3|
|Water Transport|11.4|47.0|41.6|0.0|0.7|0.2|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|6.7|32.2|61.2|0.0|31.6|9.1|
|Energy and Minerals|2.5|14.7|48.9|33.9|42.4|12.3|
|Food Products|7.3|35.6|57.1|0.0|12.2|3.5|
|Petroleum and Coal Products|6.3|25.5|68.2|0.0|0.3|0.1|
|Other Manufacturing|7.9|37.1|54.9|0.0|13.6|3.9|
|Textile and Apparel|8.9|55.8|35.3|0.0|5.5|1.6|
|Wood and Paper Products|9.7|51.2|39.1|0.0|5.2|1.5|
|**CRTS**|||||||
|Agriculture and Forestry|0.4|41.7|52.2|5.6|21.4|6.2|
|Other Services|33.7|30.9|35.4|0.0|99.9|28.8|
|Trade|9.6|39.7|50.7|0.0|44.8|12.9|
|Utilities|12.2|22.9|64.9|0.0|9.4|2.7|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

45
Table A.8: One-child families

|**Dependent Variable:**||**Househ**|**old per capit**|**a saving in **|**2007**||
|---|---|---|---|---|---|---|
||Saving in fna|ncial institutions|Plus cas|h saving|Plus inter|est earned|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Firstborn girl_ * _Expected dowry_|3366.41*|3506.17*|3528.10*|3528.16*|3714.13*|3717.74*|
||[1821.46]|[1882.99]|[1787.73]|[1996.05]|[1752.35]|[1939.27]|
|_Expected dowry_|-1608.42*||-2930.04*||-2892.84*||
||[898.37]||[1473.94]||[1457.44]||
|_Firstborn girl_|-16787.47*|-18631.00**|-19223.79**|-19646.58**|-21038.68**|-21556.61**|
||[7941.73]|[8321.80]|[7644.09]|[8976.05]|[7766.93]|[8754.37]|
|N|||671||||
|Dep var mean for Firstborn boy|8|40.38|628|.36|801|.81|
|Caste*State*YOB FE||x||x||x|

NOTES: This table reports the coefficients corresponding to specifications (1) and (2) estimated for households that have only one child. Each column is a separate regression. The dependent variable is total per capita household saving in financial institutions in columns (1) and (2), plus per capita cash saving in columns (3) and (4), and plus per capita interest earning in the last two columns. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Caste refers to indicators for SC, ST, OBC, and upper castes. YOB refers to the year of birth of the firstborn child. State refers to the state of residence at the time of survey. **X** _i_ controls for parents’ age, schooling, religion, and month of survey. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.
are upward sloping, showing expansion in immunization coverage over the period. The slope of the curves also shows that the rate of expansion was more rapid in the latest years than in earlier years. The figure also shows that expansion of the immunization coverage has been proportionally higher for the richest quintile.

<!-- Start of picture text --> Figure 1c. Trends in immunization services by wealth ranking, residence and mother's education<br>(2000-2011)<br>Full immunization Measles vaccination<br>100 Rich/Poor 100 Wealth Quintilerichestricher 100 Rich/Poor 100 Wealth Quintilerichestricher<br>80 top 60% 80 middlepoor 80 top 60% 80 middlepoor<br>bottom 40% poorest bottom 40% poorest<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014<br>Mother's Education<br>100 100 Mother's Education 100 Urban/Rural 100<br>Urban/Rural secondary+<br>80 urbanrural 80 secondary+ 80 ururbanral 80 primary n o educa tio n<br>primary<br>60 60 no education 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>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 1d presents trends for maternal health services. It shows that utilization of modern contraception expanded for all groups. However, expansions in the utilization of antenatal care visits and delivery assistance for a skilled professional have been largely for the better off, for women in urban areas and for women with some secondary education and above. The relatively better performance in contraceptive use 16
The cost-of-illness (COI) approach (mainly medical cost and value of time losses) was applied to obtain an estimate of the morbidity cost (see cost of morbidity in Table 3.4).

To summarize, the mean estimated annual cost of PM urban air pollution totals 1,103 billion Rs. or 1.7 percent of GDP in 2009. About 90 percent of the cost is associated with mortality, and 10 percent with morbidity (Table 3.4). Measured in terms of Disability Adjusted Life Years (DALYs)<sup>9</sup> about 54 percent of the cost is associated with mortality and 46 percent with morbidity (Table 3.3).

## **Table** **<u>3.4: Estimated Annual Cost of Health Impacts (Billion Rs.)</u>**

|**Health categories**|**Total Annual Cost***|**Percent of Total Cost***|
|---|---|---|
|||**(Mean)**|
|**_Mortality_**|||
|Adults|1,018|92.2%|
|Children under 5|13|1.2%|
|**_Morbidity: _**|||
|Chronic bronchitis|1|0.1%|
|Hospital admissions|3|0.3%|
|Emergency room visits/Outpatient hospital<br>visits|8|0.7%|
|Restricted activitydays(adults)|46|4.2%|
|Lower respiratoryillness in children|14|1.3%|
|Total cost of Morbidity|72|9%|
|TOTAL COST (Mortality and Morbidity)|1,103|100 %|

* Percentages are rounded to nearest percent. _Source:_ Staff estimates.

# **_3.2 Lead Exposure_**

The problem of lead pollution used to be quite serious in India. A number of Indian studies report on a mean level in blood (BLL) at about 10 ug/dL a level that the U.S. Center for Disease Control considers requiring urgent action (see for example <u>http://www.cdc.gov/nceh/lead/faq/about.htm). Health effects are quite uncertain at blood levels</u> of 5-10 ug/dl.

The main pathways for lead exposure in India are the following<sup>10</sup> :

- i. Food traces of lead are found in almost all food. Airborne lead falls onto crops or soil and is absorbed by plants.

> 9 The sum of years of potential life lost due to premature mortality and  the years of productive life lost due to disability (www.who.int).

> 10 Source: Toxics Link Handout on Lead (http://www.toxicslink.org)

14
# **Table 1. SDG indicators related to gender (N=181 countries)**

|**Goal**|**Indicator**|**Tier**|**Description**|**Sub-Indicator (if any)**|**Sex**<br>**Disaggregation**<br>**Available**|**Any Data**<br>**Available**|
|---|---|---|---|---|---|---|
||16.1.3|2|Proportion of population subjected to (a)<br>physical violence, (b) psychological violence<br>and (c) sexual violence in the previous<br>12 months|Any data: Proportion of population subjected to<br>physical violence in the previous 12 months, by<br>sex (%); AND/OR Proportion of population<br>subjected to robbery in the previous 12 months,<br>by sex (%); AND/OR Proportion of population<br>subjected to sexual violence in the previous 12<br>months, by sex (%)|9.4%|19.9%|
||16.2.2|2|Number of victims of human trafficking per<br>100,000 population, by sex, age and form of<br>exploitation||0.0%|59.1%|
||16.2.3|2|Proportion of young women and men aged 18-<br>29 years who experienced sexual violence by<br>age 18||19.9%|19.9%|
||16.7.1|2|Proportions of positions in national and local<br>institutions, including (a) the legislatures; (b) the<br>public service; and (c) the judiciary, compared<br>to national distributions, by sex, age, persons<br>with disabilities and population groups|Any data: Ratio for female members of parliamen<br>(Ratio of the proportion of women in parliament i<br>proportion of women in the national population w<br>the age of eligibility as a lower bound boundary),<br>Lower Chamber or Unicameral; AND/OR Ratio f<br>female members of parliaments (Ratio of the<br>proportion of women in parliament in the proporti<br>women in the national population with the age of<br>eligibility as a lower bound boundary), Upper<br>Chamber|NA|0.0%|
|Note:|16.7.2<br>Percent of co|2<br>untries|16.7.2 Proportion of population who believe<br>decision-making is inclusive and responsive, by<br>sex, age, disability and population group<br>with any reporting on the indicator in the five|years (2016-2020). NA indicates that the indica|0.0%<br>tor is not relevant|0.0%<br>in regards|

Note: Percent of countries with any reporting on the indicator in the five years (2016-2020). NA indicates that the indicator is not relevant in regards to sex disaggregation. Source: UN SDG Global Database. https://unstats.un.org/sdgs/dataportal 20