rafmacalaba/datause-encoder-data huggingface dataset:
For manufacturing firms where information is available on the stock of capital investment and inputs, column 5 shows that the association between management practices and performance is less pronounced, although still positive and statistically significant. When accounting for capital stock and inputs, management practices of manufacturing firms are no longer associated with higher sales and value added. Conversely, manufacturing firms with better skills (workers and managers) are much more likely to perform better, even when controlling for capital, skills, and inputs (Table 6, columns 6 and 7). Jointly, firm capabilities are positively associated with performance for manufacturing firms.

**Table 6. Associations of firm capabilities with firm performance**

|Dependent variable|(1)<br>(Log) Sales<br>per employee|(2)<br>(Log) Sales<br>per employee|(3)<br>(Log) Sales<br>per employee|(4)<br>(Log) Sales|(5)<br>(Log) Sales|(6)<br>(Log) Sales|(7)<br>(Log) Value<br>added|
|---|---|---|---|---|---|---|---|
||All firms|All firms|All firms|All firms|Manufacturing<br>only|Manufacturing<br>only|Manufacturing<br>only|
|Management practices|0.490***||0.444***|0.457***|0.205*|-0.099|0.066|
||[0.106]||[0.114]|[0.119]|[0.108]|[0.087]|[0.120]|
|Skills||0.324***|0.137|0.146|0.317|0.231**|0.361*|
|||[0.103]|[0.113]|[0.124]|[0.241]|[0.109]|[0.209]|
|Log employees||||0.956***|0.941***|0.583***|0.729**|
|||||[0.127]|[0.299]|[0.161]|[0.330]|
|Log capital invest. (last year)||||-0.000<br>[0.024]||||
|Log capital (stock)||||||0.042|0.206***|
|||||||[0.044]|[0.052]|
|Log cost of inputs||||||0.481***<br>[0.072]||
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|647|647|647|644|286|232|221|
|R-squared|0.138|0.082|0.142|0.484|0.520|0.750|0.552|
|Adjusted R^2|0.128|0.072|0.132|0.476|0.510|0.741|0.537|
|P-value: Management<br>practices = Skills=0|||0.000|0.000|0.084|0.087|0.137|
|P-value: Management<br>practices > Skills|||0.951|0.952|0.333|0.016|0.133|

Note: Standard errors in brackets. Standard controls include sector, location, firm’s age, and owner’s age. *** p<0.01, ** p<0.05, * p<0.1 25
Figure 8: **mTurk Experiment: Self and Social Image** <!-- Start of picture text -->
-150 -100 -50 0 50 100 150<br>WTP for Armani relative to Old Navy<br>Self Affirmation Control<br>1<br>.9<br>.8<br>.7<br>.6<br>Cumulative Distribution<br>.5<br>.4<br><!-- End of picture text -->

Notes: cumulative distribution of the willingness to pay to receive a luxury brand ( _Armani_ ) gift card instead of a standard brand ( _Old Navy_ ) gift card for the control and the self-affirmation groups.

37
# **Table 2: Main population indicators and trends (1990-2035).**

||**1990**|**1995**|**2000**|**2005**|2010|2015|2020|2025|2030|2035|
|---|---|---|---|---|---|---|---|---|---|---|
|Total population|14.8|17.2|20.0|23.0|26.2|29.6|32.7|35.9|38.8|41.6|
|Active population (15-64 yrs)|7.5|8.8|10.4|12.3|14.4|16.8|19.4|22.2|25.0|27.5|
|Active population|50.32|51.3|52.2|53.7|54.8|56.8|59.3|61.9|64.4|66.3|
|(as % of total population)|||||||||||
|Youth population (15-25 yrs)|2.8|3.3|4.0|4.71|5.4|6.0|6.8|_7.5_|7.9|8.4|
|Youth population|18.9|19.2|20.0|20.5|20.6|20.3|20.8|20.9|20.4|20.2|
|(as % of total population)|||||||||||
|**Growth Rate**|**1990-95**|**1995-2000**|**2000-05**|**2005-10**|**2010-15**|**2015-20**|**2020-25**|**2025-30**|**2030-35**||
|**(in percent)**|||||||||||
|Total population|2.96|2.99|2.82|2.65|2.38|2.03|1.82|1.59|1.35||
|Active population|3.37|3.41|3.45|3.06|3.15|2.95|2.72|2.39|1.96||
|youth population|3.3|3.9|3.3|2.77|2.13|2.53|1.97|1.04|1.23||

**Source: World population projections 1994-95.**
# WPS4215

## Does Uncertainty Matter?

A Stochastic Dynamic Analysis of Bankable Emission Permit Trading for Global Climate Change Policy<sup>*</sup>

### Fan Zhang<sup>1</sup>

#### Abstract

Emission permit trading is a centerpiece of the Kyoto Protocol which allows participating nations to trade and bank greenhouse gas permits under the Framework Convention on Climate Change. When market conditions evolve stochastically, emission trading produces a dynamic problem, in which anticipation about the future economic environment affects current banking decisions. In this paper, I explore the effect of increased uncertainty over future output prices and input costs on the temporal distribution of emissions. In a dynamic programming setting, a permit price is a convex function of stochastic prices of electricity and fuel. Increased uncertainty about future market conditions increases the expected permit price and causes a riskneutral firm to reduce _ex ante_ emissions so as to smooth out marginal abatement costs over time. The convexity results from the asymmetric impact of changes in counterfactual emissions on the change of marginal abatement costs. Empirical analysis corroborates the theoretical prediction. I find that 1% increase in electricity price volatility measured by annualized standard deviation of percentage price change is associated with an average decrease in annual emission rate by 0.88%. Numerical simulation suggests that high uncertainty could induce substantially early abatements, as well as large compliance costs, therefore imposing a tradeoff between environmental benefits and economic efficiency. Policy implications for designing an effective and efficient global carbon market are discussed.

#### World Bank Policy Research Working Paper 4215, April 2007

_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._

* I thank the World Bank Research Committee for funding, and Dr. Zmarak Shalizi and Philippe Ambrosi for helpful comments and suggestions.

> 1 Author’s affiliation: Harvard University, Cambridge, MA 02138, USA. email: Fan_Zhang@ksgphd.harvard.edu

1
8 Building on this experience, the IFC has been providing technical expertise to the Agricultural and Agrarian Insurance Board (AAIB) of Sri Lanka since 2017 to expand the index insurance coverage to paddy as well. Given that rice is the staple food in Sri Lanka, its source - the paddy crop - is the main and biggest cultivation in the country, with approximately 1.8 million farm families engaged in its cultivation. About 2.7 million tonnes of rough rice are produced annually, which meet 95 percent of the domestic requirement. Rice provides 45 percent of the total calorie and 40 percent of the total protein requirement of the average Sri Lankan.<sup>19</sup>

# _Box 1: Institutional framework_

- Key institutions managing the national irrigation schemes are:  Irrigation Department  Irrigation Management Division  Mahaweli Authority  Department of Agrarian Services  Water Resources Board  Ministry of Agriculture

These institutions were crucial in providing the needed historical and live irrigation data to design and monitor the payouts of the index insurance product piloted in 2018.

A significant part of the cultivable paddy land relies on irrigation. Given this context, using an index insurance product based solely on rainfall posed challenges and led to higher basis risk. Thus, the project explored innovative approaches to adapt the index insurance product to suit the local context, incorporating irrigation.

The process was a complex one, as neither irrigation nor rainfall could be included in the index in isolation. Both variables had an interplay in the crop. The need was to design a suitable index insurance product which combined both variables. Such index insurance products based on irrigation water availability coupled with rainfall are an innovation in this space.

# **5. Characteristics of the Pilot Locations**

The relevance of irrigation varies by region throughout the country. This project was carried out in two districts, Vavuniya and Anuradhapura (refer to the map in Annex 1). Vavuniya is a district in the dry zone, more dependent on rainfall – only two of its eight Agrarian Service Centres<sup>20</sup> (ASCs) have access to irrigation from the only available major tank in the area, Pavatkulam, with a catchment area of less than 30,000 hectares.

> 19 http://doa.gov.lk/rrdi/index.php/en/crop/42-crop-rice-cultivation.

> 20 Agrarian Service Centers (ASCs) are a network of offices under the Department of Agrarian Development of the Ministry of Agriculture of Sri Lanka, which handles all administrative and institutional matters related to agriculture development across the country, totaling approximately 550 centers.
Table 11: Fixed Effects OLS Dependent Variable Satisfactory Index (0/1)

|log(GDP per capita growth)|-0.03|-0.04|-0.04|-0.04|-0.03|
|---|---|---|---|---|---|
||[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|
|log(GDP per capita growth)*FCS|-0.02|0.02|-0.01|-0.01|-0.07|
||[0.05]|[0.05]|[0.06]|[0.06]|[0.06]|
|CPIA Rating|0.27|0.21|0.24|0.22|0.27|
||[0.11]**|[0.11]*|[0.20]|[0.21]|[0.22]|
|(CPIA Rating)*FCS|-0.11|-0.05|-0.07|-0.06|-0.09|
||[0.19]|[0.20]|[0.30]|[0.31]|[0.34]|
|TTL Quality||0.25|0.24|0.24|0.20|
|||[0.09]***|[0.12]**|[0.12]**|[0.12]*|
|(TTL Quality)*FCS||-0.26|-0.20|-0.20|-0.06|
|||[0.17]|[0.20]|[0.20]|[0.21]|
|log(Supervision Costs per Yr - $’000s)|||-0.06|-0.06|-0.05|
||||[0.05]|[0.05]|[0.05]|
|log(Supervision Costs per Yr- $’000s)*FCS|||0.02|0.02|0.03|
||||[0.10]|[0.10]|[0.11]|
|log(Total Staff Weeks per Yr)|||0.06|0.06|0.04|
||||[0.06]|[0.06]|[0.06]|
|log(Total Staff Weeks per Yr)*FCS|||-0.05|-0.05|-0.03|
||||[0.09]|[0.10]|[0.10]|
|log(No. of Consultant Days per Yr)||||0.01|0.01|
|||||[0.01]|[0.02]|
|log(No. of Consultant Days per Yr)*FCS||||-0.00|-0.00|
|||||[0.03]|[0.03]|
|log(Size of Country Offce)|||||-0.04|
||||||[0.06]|
|log(Size of Country Offce)*FCS|||||0.12|
||||||[0.12]|
|IRS Ratio of Country Offce|||||-0.29|
||||||[0.19]|
|(IRS Ratio of Country Offce)*FCS|||||0.58|
||||||[0.30]*|
|Constant|-0.08|-0.08|-0.06|-0.01|-0.10|
||[0.30]|[0.31]|[0.55]|[0.56]|[0.59]|
|_R_<sup>2</sup>|0.01|0.02|0.02|0.02|0.03|
|_N_|1,864|1,709|1,454|1,454|1,332|

42
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to measure. Banerjee and Duflo point to one way around this problem—exploiting an exogenous shock which is uncorrelated with entrepreneurial ability. But this approach has a downside, in that the sample is limited to firms or entrepreneurs exposed to the shock. In the case of Banerjee and Duflo’s estimates, the estimates apply only to those firms applying for credit. This resolves one problem at the cost of creating another: While estimated returns are less subject to ability bias among this subsample of firms, selfselection of the subsample suggests the returns likely overstate those for the full spectrum of firms. Low returns to capital are only one of several possible reasons entrepreneurs may fail to apply for loans. Thus, we should be interested in returns in the broader sample of firms.

The difficulty of obtaining an unbiased estimate of returns to capital across all firms in a sector is the motivation for the field experiment on which the data used in this paper are based. We use random grants of either cash or equipment to generate changes in capital against which changes in revenues and profits can be measured. Randomized experiments have quickly become an important part of the toolkit of development economics (Duflo, Glennerster and Kremer, 2006), although this is the first experiment we are aware of involving payments to firms, rather than households or schools. The random allocation of the grants ensures that the changes in capital stock are uncorrelated with entrepreneurial ability, demand shocks, and other factors associated with the differences in the profitability of investments across firms. However, a further condition for randomization to consistently identify the return to capital is that there are no spillover effects from treated to untreated firms. We use GPS location data to control for the number of firms in the same industry within a given radius of each firm. We find evidence for spillover effects of the treatment of firms in the bamboo industry, but not for the remaining firms.

We first use the data to estimate the impact of the random capital shocks on the profits of the firms, and estimate a real marginal return on capital for the average firm in our sample after controlling for spillover effects. We next use the two different sizes of treatment to test whether returns are linear, increasing, or decreasing in the size of the treatment amount. We estimate the average real return to capital to be around 5.7 percent 4
<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20 Domestic<br>0<br>St Kitts and Nevis Grenada Maldives Bhutan Belize Seychelles Cape Verde Dominica St Lucia The Gambia Guyana Djibouti Fiji Mauritius Samoa Montenegro Marshall Islands Guinea-Bissau Comoros Tonga Lesotho Kiribati Micronesia Namibia Vanuatu Solomon Islands Gabon Suriname Botswana Swaziland Equatoria Guinea Timor-Leste External<br>St Vincent and the ...<br>Antigua and Barbuda Sao Tome and Principe<br><!-- End of picture text --> Source: IMF World Economic Outlook October 2012 and IMF Article IV Consultation Reports Borrowing patterns are a reflection of access to external sources of capital and the extent of domestic markets. Currently 17 small states, out of the 35 that are the focus of this report, are assessed as not creditworthy for IBRD, or market-based sources of financing and therefore reliant on IDA and other concessional multilateral and bilateral sources for external financing. Twelve of these small states have GNI per capita that exceed the IDA cut-off (US$1,195 for FY13) but have access to IDA resources because of their lack of creditworthiness, including several under the small island exception policy.

# **Literature Survey**

Studies related to small states are well represented in the economic literature. The reason for this attention is, in part, to be found in the general belief that being small in a “macro” world is a drawback and that due to their distinct characteristics, small states are particularly vulnerable because they cannot enjoy economies of scale, both in production and public administration and tend to be particularly vulnerable to natural disasters and economic shocks. Yet, there is no unanimity of opinion among researchers. Some regard small as an asset and find that small states have an advantage because the decision-making process can be faster when administrations are small and the populations largely homogenous. This, it is argued, enables small states to respond quickly to the adjustments required by a fast changing global economy.

Aiyar (2008)<sup>7</sup> enumerates a number of advantages enjoyed by small states including relatively homogenous populations, and therefore less ethnic tension; proportionally larger foreign investment and remittance flows; the opportunity to exploit niches such as military bases or offshore banking centers; proportionally larger trade preferences than other developing countries

> 7 Aiyar, S. _Small States: Not Handicapped and Under-aided, But Advantaged and Over-aided._ Cato Journal, Vol. 28, No. 3 Fall 2008

5
occupy the outside street for business purpose; 6. relocate the air conditioner so that the impact of the noise can be reduced. Some representatives of the residents and the government people also offered personal help in order to implement the suggestions. After several rounds of negotiations, the three parties - the restaurant, the government and the residents - reached an informal agreement which incorporated all the major suggestions made by the residents, and the representatives of the three parties signed on the agreement.

On December 19, 2007, the community leader organized a roundtable dialogue again with the same participants as before, to review the implementation of the agreement signed in the first meeting. Five of the six items on the agreement had been realized, except the one on replacing coal with gas, because of its significant financial implication – Junhong Noodle House had difficulty in negotiating a deal with the gas company. However, with help from the roundtable participants, the noodle house later got a deal with the gas company at a significantly reduced price. By December 31, 2007, all the major issues raised by the residents with regard to the pollution caused by Junhong Noodle House had been resolved. All parties involved felt happy about the final outcome.

Restaurant pollution has been a serious issue in almost all cities in China, and several roundtable dialogues have been organized in other cities. Another typical one can be found in Qinhuangdao, Hebei Province, where pollution and poverty were intertwined.

The residents in Hejianli Community of Qinhuangdao City suffered from serious pollution issues caused by two restaurants located in building #15 and #18 before 2008. In late 2006, a poor man, Mr. Ma, an unemployed worker with no technical skill nor capital, rented a room on the first floor of building #15 and started a small restaurant in order to survive. In early 2008, another poor, unemployed man, Mr. Sun, started another small restaurant at the first floor of building #18. Both restaurants produced tasty local food, but at the same time generated intolerable pollution: 1) noise, from the stove blower starting from early mornings such as 4 o’clock when most of the residents living upstairs were still sleeping; 2) smoke, and 3) human waste (there is no public latrine nearby). Complaints and even violent activities from residents upstairs took place against the operations of the two restaurants.

The community workers conducted a survey in early May, 2008 about the most serious issues concerned by the residents in the community, and found that the pollution generated by the two restaurants were among the top. On May 26, 2008, the community workers organized a stakeholder roundtable dialogue meeting, where government representatives, representatives of the local residents, the restaurant owners, and the lenders of the two restaurants sit together, discussing about the issues and looking for ways of 14
To expand the data coverage to the 19th century, we supplement this source with information from the National Center for Environmental Information, which lists events of earthquakes, volcano eruptions, floods, tsunamis, and storms going back until 1800. In addition, we make use of numerous historical and country studies on famines and epidemics during the 19th century, as listed in Appendix I.

Table 1. Number of disaster events in different eras: 1800-2015

|1800 - 1913<br>Long 19th Century|1914 - 1945<br>Inter-War Period|1946 - 1973<br>Bretton Woods|Modern Era<br>1973 - 2015|
|---|---|---|---|
|Financial crises<br>254|235|164|416|
|Severe natural<br>disasters<br>44|28|20|16|
|Military conflict<br>157|70|104|154|
|Total number of<br>disaster events<br>455|333|288|586|

_Sources:_ See text and Appendix I for details.

The resulting dataset identifies several thousand natural disasters since 1800. Given our focus on disasters that warrant the provision of international financial assistance, we focus only on the most severe events. To do so, we match our list of natural disasters with population data from Mitchell (2007) and include only those natural disasters that result in casualties of at least 0.1 percent of the population. This yields 108 severe natural disaster events over the course of the past 200 years. While the timing of the disaster event is straight-forward with respect to earthquakes, floods or hurricanes, it can be difficult to accurately measure the duration of famines and epidemics. For these episodes, we consider the initial two years of the outbreak.

Table 1 summarizes the number of disasters and their frequency across different eras. Figure 11 also plots the incidence of disasters but takes a different perspective. It shows the share of independent countries that experience war, enter financial crises or suffer from severe natural disasters in any given year since 1800 based on our sample. Figure 10 reveals that the time-variation in the number of disasters is primarily driven by the waves of financial crises and military conflict that are spatially correlated. In contrast, the occurrence of severe natural disaster events is less cyclical and fairly constant over time.

Figure 11. Incidence of wars, crises and natural disasters 1800 – 2010 21
# **BOX  1:       Structure of Simulated Scenarios**

### **TABLE 1    Ten Policy Scenarios simulated (except BAU)**

|**Climate policies applied by LCT**<br>**Pioneers**|**Climate policies**<br>**Applied by the**<br>**FFDCs**|**Trade**<br>**policies**<br>**applied by LCT**<br>**pioneers**|**Diversification strategies of**<br>**FFDCs**|
|---|---|---|---|
|||N bd dtt|1. Traditional diversification|
|**Unilateral**<br>**carbon**<br>**taxes**<br>**to**<br>**~~s~~tabilize climate at 2-deree**||o orer ajusmen|2. Asset diversification|
|**g**<br>**Celsius target**|Baseline carbon|Border adjustment tax<br>|3. Traditional diversification|
|||||
|(baseline carbon prices in LCT<br>pioneer countries until 2025 anda|prices in FFDCs|ase on caron content o<br>imports|4. Asset diversification|
|rapid ramp-up until 2030 and<br>beyond;||Flat 10 percent import|5. Traditional diversification|
|||tariff (ala Nordhaus)|6. Asset diversification|
|**Cooperative climate policies to**|Cooperative carbon||7. Traditionaldiversification|
|**stabilize climate at 2-degree**<br>|tax on CO2emissions||8. Asset diversification|
|**Celsius target**(smoothlyincreasing<br>uniform carbon prices in all countries|Cooperative<br>llhd b|No border adjustment|9. Traditionaldiversification|
|since 2015)|weea caron tax<br>on fuelproduction||10. Asset diversification|

_Note:_ FFDC = fossil fuel–dependent country; LCT = low-carbon transition; n.a. = not applicable.

For convenience, color and line style coding are used for different scenario subsets in all figures in this paper:

- **Orange** : Unilateral climate response measures _without any border adjustment taxes_

- **Grey:** Unilateral climate response measures with traditional border carbon adjustment tax based on _carbon content of imports_

- **Red:** Unilateral climate response measures with _Nordhaus border tariffs_ (ad valorem 10 percent import duty on all imports from nonparticipating FFDCs)

- **Green:** Cooperative _traditional carbon taxes_ on carbon dioxide emissions within the national borders

- **Blue:** Cooperative _wellhead carbon taxes_ on carbon content of fuels produced

To distinguish between diversification options, in all dynamic figures illustrating changes of parameter values over time, the following apply: ■ Solid lines (.........) = traditional diversification (TD)

- Dashed lines (- - - - -) = asset diversification (AD)

## **Design of the Climate and Trade Policy Scenarios**

In several possible cooperative and noncooperative versions of the future, different “climate clubs” implement different levels of ambition of domestic climate policies, represented by economy-wide carbon taxes (figure 2). Some LCT pioneers, mainly high-income OECD countries, are assumed to ramp-up their unilateral carbon taxes faster and steeper than other LCT pioneers (middle income fuel importers, including China and India), although they all converge their carbon price trajectories by 2050.

7