rafmacalaba/datause-encoder-data huggingface dataset:
social acceptance and the process of domestication of new technologies considering users’ priorities and problems are important in the adoption of technologies (Fouquet and Pearson, 2012). Barriers specific to wide spread adoption of ICS include absence of internationallyrecognized ICS standards and lack of testing capabilities, lack of information on health benefit of ICS and fuel interventions, and high initial cost of ICS (GACC, 2011). Apart from direct investments in energy access, Barnes et al. (2010) emphasize indirect investments including adequate generation and transmission for rural electrification, availability of LPG, in developing and implementing programs that can effectively address the barriers to assisting household to move toward better fuels and appliances. Based on the findings of CSI study in Indonesia, ASTAE (2013a) suggests strategies, such as centralized leadership, cross-sector cooperation and creation of sustainable market, to scale up the use of clean biomass stoves. The study finds the commercial market for ICS is quite limited in the country.

# **5. Household Cooking Energy, Health and Environment**

Incomplete combustion of household cooking fuels, mainly solid fuels in developing countries, emits substantial quantities of harmful air pollutants and contaminants. These include toxic air compounds, such as carbon monoxide (CO), polyaromatic hydrocarbons (PAHs), benzene and formaldehyde, and toxic contaminants such as ash, sulfur and mercury (Smith et al., 2012). There is mounting evidence that exposure to these toxic air pollutants and contaminants have adverse impacts on human health. Furthermore, CO2 emissions and black carbon emitted from household cooking fuels also threaten human health through change in global climate.

# 5.1. Cooking and human health

The problems related to solid fuels as an energy source for cooking has been an issue of concern for more than three decades. Although the amount and type of fuels used and the time of exposure to emission of toxic products vary, in general, they have adverse impacts on human health including child pneumonia, chronic obstructive pulmonary disease (COPD) and lung cancer (WHO, 2002; Smith et al., 2004; Dherani et al., 2008). Mainly women and young children are at risk, particularly in SSA and SA regions (UNDP and WHO, 2009). Based on the WHO health statistics, pneumonia is responsible for 2 million deaths mostly children and COPD 19
- (2) An aggregated food consumption module that corresponds to the “collapsed list” treatment arm in the SHWALITA study, and

- (3) A series of short, aggregated non-food consumption expenditure modules that were informed by the variants from the SHWALITA study but were refined in some instances to better align with the COICOP categories (United Nations, 2018), related, for instance, to education, health, and utilities expenditures.

The TA 3 collapsed food consumption module is slotted immediately after the core modules, covering all 12 broad food categories (including alcoholic beverages), and only asking the respondent to state the monetary value that the consumed quantity of total food in that category would have cost, had it been purchased.<sup>6</sup> TA3 non-food consumption expenditure modules are then slotted immediately after the TA3 collapsed food consumption module, and together, these sets of modules permit the estimation of Models 3 and 4 as presented in Dang _et al._ (forthcoming). The TA 3 sample consists of 698 households.

These data are in turn complemented with the data from the nationally representative TZNPS 2020/21 and 2019/20 rounds, which are used as base surveys to estimate the imputation models that are in turn applied to each treatment arm. The main results are based on the 2020/21 round, while Appendix A includes consistent findings based on the 2019/20 round, as discussed below. The TZNPS is a multi-topic, nationally representative longitudinal household survey that has been implemented by the NBS since 2008, with financial and technical support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) project. The questions for the poverty predictors required for the estimation of Models 1, 2, 8 and 9 are

> 6 TA 3 covers: cereals and cereal products; starches; sugar and sweets; pulses, dry; nuts and seeds; vegetables; fruits; meat, meat products, fish; milk and milk products; oil and fats; spices and other foods; alcoholic and non-alcoholic beverages.

9
# References

ALDERMAN, H. and C. Paxson, (1992), "Do the Poor Insure? A Synthesis of the Literature on Risk and Consumption in Developing Countries", World Bank Policy Research Working Paper, No. 1008, Washington, D.C.

APPELBAUM, E., and E. Katz, (1991), "The Demand for Children in the Absence of Capital and Risk Markets: A Portfolio Approach" Oxford Economic Papers, Vol. 43, pp. 292-304

> ARENAS DE MESA, Alberto and Hector Hernandez Sanchez, (2001), "Cobertura del Sistema de Pensiones de Capitalizaci6n Individual Chileno: Diagnostico y Propuestas", SOCIALIS: Revista Latinoamericana de Politica Social, No. 4, Abril, pp. 93 - 124

ARENAS DE MESA, Alberto and Mario Marcel (1999) "Fiscal Effects of Social Security Reform in Chile: The Case of the Minimum Pension", mimeo, presented that the APEC Second Regional Forum on Pension Fund Reforms, Vina del Mar, April 26 - 27

> ARENAS DE MESA, Alberto, (2000) "Cobertura Previsional en Chile: Lecciones y Desafios del Sistema de Pensiones Administrado por El Sector Privado", CEPAL Serie, Financiamiento del

> Desarollo, No. 105, Santiago

ATTANASIO, Orazio P. & James Banks, (2001), "The Assessment: Household Saving - Issues in Theory and Policy" Oxford Review of Economic Policy, Vol. 17, No. 1, pp. 1 - 19 AUERBACH, Alan J. & Laurence J. Kotlikoff, (1987), _LDnamic Fiscal Policy_ Cambridge: Cambridge University Press

> BARR, Abigail & Truman Packard, (2002) "Revealed Preference and Self Insurance: Can We

> Learn from the Self Employed in Chile", World Bank Policy Research Working Paper, No. 2754, Washington, DC

- (2000a), "Revealed and Concealed Preferences and Self Insurance: Can we Learn from the Self Employed in Chile" Oxford University, Department of Economics Discussion Paper Series No. 53

> (2000b), "A Tool for Measuring Risk Aversion and Time Preferences in the Field" World Bank, mimeo

BARR, Nicolas (1998), _The Economics of the Welfare State._ **3 rd** Edition, Oxford

- (2000), "Reforming Pensions: Myths, Truths, and Policy Choices", IMF Working Paper WP/00/139, Development Issues Division, Washington DC

(2001), _The Welfare State as Piggv Bank:,_ Oxford BARRIENTOS, Armando (1996) "Pension Reform and Pension Coverage in Chile: Lessons for Other Countries" World Development, Vol. 15, No. 3, pp. 309 - 322

- (1997) "Pension Reform and the Individual Capitalization Pension Scheme Stampede in Latin America", University of Hertfordshire Business School Working Paper, No. 17

- (1998a) "Pension Reform, Personal Pensions and Gender Differences in Pension Coverage" World Development, Vol. 26, No. 1 pp. 125 - 137

> (1998b) _Pension Reform in Latin America._ Ashgate

(1999a) "Retirement, Household Income, and Economic Vulnerability of Older Groups", University of Hertfordshire Business School Working Paper, No. 20 33
The goal of the rest of the paper is to estimate the relevant parameters that allow us to compare changes in Bangladesh to changes predicted by the models above. We begin with a description of the relevant data.

# 3. Data Description

Following McCaig and Pavcnik (2018), we start with labor force surveys and combine them with trade data. After describing these data, we present the empirical methodology that starts with the Bartik (1991) approach and extends with an approach to estimate the general-equilibrium predictions of the theory above. We first describe the trade data and trends in trade and production and then describe the labor market data and relevant labor market characteristics.

## Trade and Production

We begin our description of the changes in Bangladesh’s trade and production patterns using annual, bilateral commodity trade data from COMTRADE using the 4-digit International System of Industrial Classification (ISIC) Rev. 3.1. Our main focus is on United States and European Union imports from Bangladesh over the 1990-2016 period. The main point is that exports have expanded significantly, and apparel and textiles have played the leading role _._ Like many developing countries, Bangladesh followed an import-substitution-industrialization (ISI) strategy for much of the 20<sup>th</sup> century but turned and implemented liberalizing reforms in the 1990s. The government reduced the maximum import duty of 350 percent in 1993 to 32.5 percent in 2003 and 25 percent in 2005. Bangladesh also reduced the number of tariff bands from 15 in 1993 to 4 in 2016 _._ Between 1992 and 2008, the unweighted average tariff rate declined from a high of 70 percent to low of 12.3 percent. During the 1990s several measures were designed to reduce the cost of imported inputs, including reducing tariffs, subsidized interest rates on bank loans, cash subsidies, exemptions from value added and excise taxes, bonded warehouse facilities, a duty drawback facility, duty-free imports of machinery and inputs for export industries, an export credit guarantee scheme, and income tax rebates for exporters. The government established Export Processing Zones (EPZs) that included basic factory structures with dependable utilities, secured industrial areas, central monitoring of labor compliance, and one-stop facilities for exports.

Bangladesh’s various trade liberalization efforts, along with structural adjustment initiatives, opened the economy to world demand. Export value increased in nearly every year from 1989 to 2015, rising from US$1.5 million to almost US$30 million. At the same time, imports (primarily industrial raw material and capital machinery) increased from close to US$4 million to slightly over US$40 million.

This trend of sustained expansion in trade reversed in 2008. When the global financial crisis hit in 2008, export growth declined significantly due to falling global demand. In fact, imports and exports which have been growing as a share of GDP since the 1970s saw a decline with the global financial crisis (Figure 5a).

11
<!-- Start of picture text --> Figure 2.7. MDG indicators (% or ‰)<br>100<br>80<br>60<br>40<br>20<br>0<br>Poverty rate (%) U5MR (‰)<br>Water access (%) Sanitation access (%)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2.8. MDG indicators (Index; 2012=100)<br>140<br>120<br>100<br>80<br>60<br>Poverty rate U5MR<br>Water access Sanitation access<br><!-- End of picture text --> 19
firms and emissions monitoring. According to Geert Hofstede’s website, long-term orientation (“ _ltowvs_ ”), expressed on a scale from 0 (least long-term oriented) to 100 (most long-term oriented), pertains to change. In a long-term oriented culture, there is a fundamental belief that the world is changing, necessitating preparation for the future. Conversely, in a short-term oriented culture, the world is perceived as static, with the past providing a moral compass that should be followed.

The second dimension we consider is individualism (“ _idv_ ”), where 100 represents the most individualistic country and 0 the least. Individualism measures the degree to which people feel independent, as opposed to interdependent as members of a larger whole.

For the social dimension, we use power distance (“ _pdi_ ”), which ranges from 0 (lowest) to 100 (highest). Power distance measures the degree to which the less powerful members of organizations and institutions accept and expect power to be distributed unequally. Additionally, we use uncertainty avoidance (“ _uai_ ”), which addresses a society’s tolerance for uncertainty and ambiguity, also ranging from 0 (lowest) to 100 (highest).

For the governance dimension, we use long-term orientation (“ _ltowvs_ ”) and masculinity (“ _mas_ ”). Masculinity measures the extent to which the use of force is socially endorsed, with higher scores (closer to 100) indicating more masculine societies.

Table 1 shows the list of countries in our sample, covering different world regions: Africa (AFR), East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MNA) and 9
# **Table 1B: Descriptive Statistics on Parents-Grandparents** **<u>Sample</u>**

|**Sample**|**Fu**|**ll sample**<br>||**Mean Value**<br>**Primary sc**<br>|**s of Variables**<br>**hool cut-off**<br>||
|---|---|---|---|---|---|---|
||**Mean**|**Standard**<br>**deviation**|**Farm**<br>**low edu**|**Off-farm**<br>**low edu**|**Farm**<br>**high edu**|**Off-farm**<br>**high edu**|
|**_Parents_**|||||||
|Parents' age|44.8|10.0|45.6|45.9|40.5|41.9|
|Parents' years of schooling|6.6|2.8|6.4|6.9|7.6|7.7|
|Dummy = 1 if parents more than primary schooling|0.6|0.5|0.5|0.6|0.7|0.8|
|**_Grandfathers_**|||||||
|Grandfather's age|74.5|12.3|75.7|76.4|68.3|70.1|
|Grandfather's years of schooling|2.9|2.7|2.0|2.9|7.2|7.3|
|Dummy = 1 if grandfather more than primary schooling|0.2|0.4|0|0|1.0|0.9|
|Dummy = 1 if grandfather in off-farming|0.1|0.3|0|1.0|0|1.0|
|**_Grandmothers_**|||||||
|Grandmother's age|71.8|11.8|72.9|73.4|66.2|68.1|
|Grandmother's years of schooling|1.2|1.9|0.8|1.1|3.2|3.0|
|Dummy = 1 if grandmother more than primary schooling|0.1|0.2|0|0|0.5|0.3|
|Dummy = 1 if grandmother in off-farming|0.0|0.1|0|0.2|0|0.2|
|**_Grandparents in general_**|||||||
|Grandparents' average years of schooling|2.2|2.1|1.5|2.1|5.5|5.5|
|Dummy = 1 if at least one grandparent more than primary schooling|0.2|0.4|0|0|1|1|
|Dummy= 1 if at least onegrandparent in in off-farming|0.1|0.3|0|1|0|1|

**_Notes_**

1) Observations: 14777 for full sample; 2) Observations for the groups in the primary school cutoff: farm and low edu (11571), off-farm and low edu (690), farm and high edu (2204), off-farm and high edu (312);

- 3) For primary school cut-off, "High edu" stands for more than primary schooling and "low edu" stands for primary schooling or less;

.
Figure 2. Digital platform firms are controlling the pie at both global and regional/country markets Market Share by Company <!-- Start of picture text --> Global cloud services Global digital advertising E-commerce in Latin America<br>* Amazon Web = Mercado Libre<br>Services = Google: = B2W<br>= Microsoft = Facebook<br>Other Other / = OLXOther<br>Ride hailing in Indonesia Search engines in Russia Mobile money in Kenya<br>.<br>aa « Go-jek = Yandex | = M-Pesa<br>= Google = Equitel<br>= Other Other Other<br><!-- End of picture text -->

Sources: Canalys.com; Communications Authority of Kenya; Industry.co.id; Marketing-Interactice.com: Mercado Libre: Radar: Statista.com,
Policy ReseaRch WoRking PaPeR 4765

# **Abstract**

The price of catastrophe risks is viewed by many to be too high and/or too volatile. Catastrophe risk practitioners point out that, contrary to standard insurance, such as automobile insurance, catastrophe re-insurance is exposed to infrequent but potentially very large losses. It thus requires keeping a large amount of capital in hand, generating a cost of capital to be added to the long-term expected loss. This paper pulls together data from about 250 catastrophe bonds issued on the capital markets to investigate how catastrophe risks are priced. The analysis reveals that catastrophe risk prices are a function of the underlying peril, the expected loss, the wider capital market cycle, and the risk profile of the transaction. The market-based catastrophe risk price is estimated to be 2.69 times the expected loss over the long term, that is, the long-term average multiple is 2.69. When adjusted from the market cycle, the multiple is estimated at 2.33. Peak perils like US Wind are shown to have a much higher multiple than that of non-peak perils like Japan Wind, revealing the diversification of credit from the market.

This paper—a product of the Global Capital Market Non Banking Financial Institutions (GCMNB) Division, Finance and Private Sector Development Vice Presidency—is part of a larger effort in the department to support clients in the financial management of disaster risks. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at omahul@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
DFITS measure which captures changes in the OLS slopes as individual observations are dropped from the sample (see Belsely, Kuh and Welsch (1980) for details).

Looking at the top panel of Table 4, it is apparent that dropping individual countries has relatively little impact on my results. In nearly all cases, my first‐stage regressions are reasonably strong, with first‐stage F‐statistics greater than 10. There are some fluctuations in the estimates of the multiplier, but these are moderate, and in no case do I find an estimated multiplier that is significantly different from zero. Removing influential observations, as is done in the bottom two panels of Table 4, affects the results somewhat more. In nearly all cases, the estimated multiplier is smaller when influential observations are dropped from the reduced‐form and first‐stage regressions. However this difference is in most cases small. Interestingly, in several cases, dropping influential observations strengthens identification relative to the benchmark results: in five out of eight specifications, the first‐ stage F‐statistics are greater than those reported in the default specifications in Table 3. Overall, this first set of robustness checks based on eliminating influential observations is broadly consistent with the benchmark estimates: the multiplier is small, in most cases reasonably precisely estimated, and generally the estimates are smaller as successively more credibly exogenous measures of disbursements are used as instruments.

Table 5 presents three further sets of robustness checks, that are designed to address potential concerns about the exclusion restriction. In the top panel of Table 5, I consider the possibility that shocks to growth are persistent over time. If World Bank project approvals also are correlated with contemporaneous shocks to growth, then subsequent disbursements on these projects would also be correlated with shocks to subsequent growth, in violation of my exclusion restriction. As noted above, the most straightforward way to address this concern is simply to control for lagged GDP growth. In all four specifications, I find that lagged growth is significantly correlated with contemporaneous growth. However, its inclusion has almost no effect at all on my estimates of the multiplier, which are virtually unchanged from those in Table 3. Controlling for lagged growth also does not appreciably weaken identification: the first stage F‐statistics for the excluded instrument are above 10 in all four specifications.

Another possible problem is that changes in spending might only affect output growth with a lag. If these lagged changes in total spending are correlated with contemporaneous changes in disbursements, perhaps because disbursements themselves are persistent over time, this too would lead to violations of the exclusion restriction. In the middle panel of Table 5, I also include lagged 17
A Duration Analysis of CONALEP WPS3327

**A Duration Analysis of CONALEP**

**(Mexico’s National Technical Professional School)**

**Gladys López-Acevedo**<sup>**1**</sup>

# **JEL Classification:** I28; J24 and N36.

**Key Words:** Technical education, duration analysis.

# World Bank Policy Research Working Paper 3327, June 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Economist, World Bank, <u>gacevedo@worldbank.org. This research was completed as part of the “Training</u> Mechanisms Reform” Project at the World Bank. I am particularly grateful to Indermit S. Gill who provided valuable advice.

1
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles year GDP from 53% and 55% from 60% for civil servants and teachers respectively. The impact of price versus wage indexation on the IPD is compared in Figure 3.

**Figure 51: Projected implicit pension debt* (% of GDP)**

<!-- Start of picture text --> civil servants teachers<br>50% 60%<br>wage<br>40% wage 50%<br>price<br>30% price 40%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0% 0%<br>200320082013201820232028203320382043204820532058206320682073 200320082013201820232028203320382043204820532058206320682073<br><!-- End of picture text -->

**Notes: *** liabilities accrued by the system at a given point in time **Source** : WB calculations based on PROST

- 108 -
Table 5.1

# Abatement Cost Function Estimation Results

# 5.1a: Variable Descriptions

LTRT = Log (Volume of Wastewater Treated) LTSS = Log (Effluent/Influent) for TSS LCOD = Log (Effluent/Influent for COD LBOD = Log (Effluent/Influent) for BOD LOTH = Log (Effluent/Influent) for Other Pollutants

# 5.1b: **Regression Results (I)**

||**Constant E**<br>**Coefficient**|**lasticity**<br> <br>**t**|**Quadratic**<br>**Coefficien**|Scale<br>**t**<br>**t**|**Partial Int**<br>**Coefficient**|**eractions**<br>**t**|**Full Transl**<br>**Coefficient**|**og**<br> <br>**t**|
|---|---|---|---|---|---|---|---|---|
|Intercept|0.884**|4.90|0.849**|4.68|0.930**|4.86|0.913**|4.51|
|LTRT|0.408**|12.39|0.343**|6.84|0.333**|6.43|0.332**|5.37|
|LTSS|-0.214**|3.96|-0.208**|3.86|0.205|1.38|0.135|0.86|
|LCOD|-0.272**|3.08|-0.281**|3.19|-0.484**|2.54|-0.447*|1.92|
|LBOD|-0.330**|2.91|-0.331**|2.93|-0.222|0.83|-0.19|0.50|
|LOTH|-0.108**|2.21|0. 109**|2.24|-0.042|0.40|-0.038|0.33|
|LTRT<sup>2</sup>|||0.015*|1.70|0.016*|1.90|0.020**|2.06|
|LTSS<sup>2</sup><br>|||||0.076**|3.12|0.088**|3.28|
|LCOD<sup>2</sup>|||||0.031|0.46|0.030|**0.42**|
|LBOD<sup>2</sup>|||||0.070|0.65|0.071|0.66|
|LOTH<sup>2</sup>|||||0.010|0.62|0.011|0.66|
|LTSS*LCOD|||||-0.095|1.54|-0.090|1.38|
|LTSS*LBOD|||||0.194*|1.92|0.189*|1.83|
|LTSS*LOTH|||||0.093|1.24|0.105|1.37|
|LCOD*LBOD|||||-0.137|1.63|-0.132|1.53|
|LCOD*LOTH|||||-0.017|0.33|-0.031|0.44|
|LBOD*LOTH|||||-0.164|1.43|-0.163|1.41|
|LTRT*LTSS|||||||0.033|1.22|
|LTRT*LCOD|||||||-0.009|0.23|
|LTRT*LBOD|||||||-0.005|0.09|
|LTRT*LOTH|||||||-0.006|0.24|
|FOOD|-1.074**|3.63|-1.060**|3.60|-1.025**|3.38|-1.019**|3.31|
|TEXTILES|0.481*|1.87|0.469*|1.83|0.375|1.440|0.363|1.38|
|OIL REFINING|1.428**|4.84|1.366**|4.51|1.124**|3.690|1.044**|3.27|
|CHEMICALS|0.350*|1.70|0.355*|1.73|0.328|1.610|0.282|1.35|
|IRON &|-2.252**|4.63|-2.224**|4.58|-2.090**|4.340|-2.072**|4.28|
|STEEL|||||||||
|# OF OBS.|327||327||327||327||
|R<sup>2</sup>|0.51||0.51||0.53||0.53||
|CRITICAL F|||2.88||2.16||1.69||

15