rafmacalaba/datause-encoder-data huggingface dataset:
_China (2008-2013)_<sup>[47]</sup> , Chapter V Assessment on the Construction Industry in _The Fifth IPCC Assessment Report_ , as well as the summary and calculation of relevant literature.

# **Appendix References**

[1] National Bureau of Statistics. 2017. China Statistical Yearbook 2017. Beijing: China Statistics Press, 2017.

[2] Han S, Chen H, Long R, Cui X. Peak coal in China: A literature review. Resource Conserve Recycle. 2018 Feb 1; 129: 293–306.

[3] BP. 2017.BP energy outlook 2017,2017.

[5] China Electric Power Yearbook Editorial Board. 2010. China Electric Power Yearbook. Beijing: China Electric Power Press, 2010.

[6] China Electric Power Yearbook Editorial Committee. 2011. China Electric Power Yearbook. Beijing: China Electric Power Press, 2011.

[7] China Electric Power Yearbook Editorial Board. 2012. China Electric Power Yearbook. Beijing: China Electric Power Press, 2012.

[8] China Electric Power Yearbook Editorial Committee. 2013. China Electric Power Yearbook. Beijing: China Electric Power Press, 2013.

[9] National Bureau of Statistics. China Energy Statistical Yearbook 2006. Beijing: China Statistics Press, 2006.

[10] National Bureau of Statistics. China Energy Statistics Yearbook 2007. Beijing: China Statistics Press, 2007.

[11] National Bureau of Statistics. China Energy Statistical Yearbook 2008. Beijing: China Statistics Press, 2008.

[12] National Bureau of Statistics. China Energy Statistics Yearbook 2009. Beijing: China Statistics Press, 2009.

[13] National Bureau of Statistics. China Energy Statistics Yearbook 2010. Beijing: China Statistics Press, 2010.

[14] National Bureau of Statistics. China Energy Statistics Yearbook 2011. Beijing: China Statistics Press, 2011.

[15] National Bureau of Statistics. China Energy Statistics Yearbook 2012. Beijing: China Statistics Press, 2012.

[16] National Bureau of Statistics. China Energy Statistics Yearbook 2013 Beijing: China Statistics Press, 2013.

[17] National Bureau of Statistics. China Energy Statistics Yearbook 2014 Beijing: China Statistics Press, 2014.

[18] China Electricity Council, China Electricity Statistics Collection. 2011. Beijing: 2011 45
and leisure. Third, absent insurance, the increase in agricultural production caused by access to irrigation reduces the marginal utility from agricultural production relative to the marginal utility from consumption. In turn, this causes labor and input allocations on the most important plot to fall.

An implicit assumption we make that generates this result is the absence of functioning land markets. With _perfectly_ functioning land markets, shocks to the household’s land endowment, such as the sample plot shock, should not affect productive decisions on the household’s most important plot. Instead, both the sample plot and the most important plot would flow to the household with the highest willingness-topay for them. In practice, land transactions do occur; as discussed in Section 2.2.2, our survey tracks plots across transactions in land markets, so we are able to directly test the prediction that the sample plot shock does not affect the productive decisions on the most important plot.

Rejecting separation with the test suggested by Proposition 2 implies that the levels of irrigation adoption are inefficient and that land market failures contribute to this inefficiency. At the same time, this test does not allow us to test for which of the three constraints above interacts with land market frictions to generate separation failures.

To shed light on which other constraints generate separation failures, we consider how households with different characteristics should _differentially_ respond to the sample plot shock. We focus on two important household characteristics: household size, which determines the availability of household labor, and wealth, which determines the ability to purchase inputs.<sup>35</sup>

**Proposition 3.** _If input or insurance constraints bind, then the input and labor allocations on the most important plot of larger households (wealthier households) should be less (less) responsive to the sample plot shock._

The intuition for this result is that both insurance and input constraints are ultimately financial constraints, which causes household size and wealth to enter the problem symmetrically. Under insurance constraints, both household size (by increasing the amount of labor income) and wealth increase household consumption. If we additionally assume that risk aversion is decreasing sufficiently quickly in consumption, then the allocations of wealthier and larger households will be closer to those

> 35In Section 5.1, we further discuss the choice of household size and wealth as shifters of availability of household labor and ability to purchase inputs, respectively.

28
# 4. EXTENSIONS AND CONCLUSIONS

In this section we discuss extensions to the model we just analyzed and show that they reinforce the results obtained. We assumed that labor enters only in the production of the tradeable input and that consumption and labor were separable in preferences. These two assumptions imply that either leisure or consumption would remain constant after any of the shocks analyzed and made the discussion of the decentralization straightforward and simple. The model can be extended by relaxing any of these assumptions. For instance, if we introduce labor as an additional input in the production of the continuum of ...nal goods, a negative shock to the home-input technology A (st) can be shown to imply a devaluation of the exchange rate and an increase in the money supply.<sup>22</sup> These results contrasts with those in Table II, where the nominal exchange rate and the money supply do not change. We draw two conclusions from the discussion. First, that modelling details are important for the qualitative predictions of the model, and second, that the main message that it is not possible to characterize optimal policy in terms of simple rules based on the cyclical properties of the economy is a robust result.

Another key assumption in the model is the existence of taxes on foreign securities �<sup>�</sup> (s<sup>t</sup> ) : This instrument gives the planner the ability to manipulate the intertemporal prices faced by the domestic residents without violating the interest parity condition (18). The same allocation can be implemented if, instead of having the taxes �<sup>�</sup> (s<sup>t</sup> ), the government has access to a uniform consumption tax over varieties i 2 [0; 1] : If, however, the government does not have access to consumption taxes, the constraint (18) binds. The allocation will be di¤erent, and welfare will be lower. Further, even though the consumption aggregate c (s<sup>t</sup> ) is homogeneous of degree one in the individual varieties, a condition known to imply that the Friedman rule is optimal with a cash-in-advance constraint on a subset of varieties (Chari,

> 22This version of the model is analyzed in Hevia and Nicolini (2004).

26
<!-- Start of picture text --> A .<br>oe ss we<br>Pe pitt, 4 ey, cen ° “nse = ~ z<br>800.08<br>700.08<br>a4reEe 600.028<br>Boag 500.02<br>$2 a000a<br>= 300.08a—See— ——_<br>100.08 — ==><br>0.08<br>20012 20028 20038 20048 20058 20062 20078 20082<br>89.60 99.38 105.12 111.98 98.421 121.18 162.78 223.18<br>—fsoybean®) 180.728 201.38 241.38 288.52 238.68 234.80 326.98 474.70<br>| t= rapeseed 206.12 221.88 265.92 304.728 260.18 313.38 427.38 604.92<br>177.48 196.98) 200.92 244.58 290.58 311.28 334.50 697.52<br>129.78 150.88) 149.62) 161.30 157.88, 199.78 263.88 344.62)<br>Year®l<br><!-- End of picture text -->
# **References**

Aksoy, M. Ataman, (2005), “Global Agricultural Trade Policies”, in Aksoy, and Beghin, _Global Agricultural Trade and Developing Countries,_ Washington DC: World Bank.

Aksoy, M. Ataman and John C. Beghin (2005), _Global Agricultural Trade and Developing Countries_ , Editors, Washington DC: World Bank.

Anderson, Kym and Will Martin (2006), _Agricultural Trade Reform and the Doha Development Agenda,_ Editors, Washington DC: A co-publication of Palgrave Macmillan and the World Bank.

Diop, N, and S.M. Jaffee, (2005), “Fruits and Vegetables: Global Trade and Competition in Fresh and Processed Product Markets”, in Aksoy and Beghin, _Global Agricultural Trade and Developing Countries,_ Washington DC: World Bank.

Ayres, Wendy and Alex McCalla (1996), “Rural Development, Agriculture, and Food Security”, _Finance and Development_ , 33: 8-11, December.

FAO (2002), “Trade Liberalization and Food Security: Conceptual Links”, Chapter 3 in _Trade Reform and Food Security_ , Rome: FAO Document.

Hertel, T, R. Keeney, M. Ivanic and L. A. Winters (2007), “The Distribution Effects of WTO Agricultural Reforms in Rich and Poor Countries”, _World Bank Research Digest_ , 1(3), Spring.

McCalla, Alex (2001), “What the Developing Countries Want from the WTO”, _The Estey Centre Journal of International Law and Trade Policy_ , 2(1), 165-177.

McCalla, Alex and John Nash (2007), _Reforming Agricultural Trade for Developing Countries_ , Editors, Washington DC: World Bank.

Mitchell, D., and M. Hoppe (2006), “From Marrakesh to Doha: Effects of Removing Food Subsidies on the Poor”, in R. Newfarmer, _Trade, Doha, and Development,_ Washington D.C: World Bank.

Ingco, Merlinda and John Nash (2004), _Agriculture and the WTO: Creating a Trading System for Development_ , Editors, A co-publication of the World Bank and Oxford University Press.

Panagariya, Arvind (2006), “Agricultural Liberalization and the Least Developed Countries: Six Fallacies”, _The World Economy: Global Trade Policy 2005,_ edited by David Greenaway _,_ Boston: Blackwell Publishers.

Thomas, Harmon (2006), _Trade Reform and Food Security: Country Case Studies and Synthesis_ , editor, Rome: FAO.

17
out firms that are (a) relatively small and sometimes informal and (b) likely to have different accounting standards.

For the analysis on country heterogeneity in Section 5, we use country-level data on economic, bank, and capital market development from the World Development Indicators database and the Financial Structure and Development database. Economic development is measured using gross domestic product (GDP) per capita in constant 2011 U.S. dollars. Bank development is the ratio of private credit by deposit money banks to GDP. Capital market development is measured as the sum of equity and corporate bond market capitalization to GDP. For the analysis on growth opportunities in Section 6, we use industry-level and country-level data. Appendix Table 2 lists the different variables and sources used for Sections 5 and 6.

The results reported throughout this paper are robust to a number of alternative sampling approaches. First, the results hold: (a) when excluding China or the United States, which together account for about 40 percent of world GDP; or (b) when simultaneously excluding Canada, Japan, and the United Kingdom, which together account for the largest number of issuers—23 percent of the total number of capital market issuers in the sample. Second, our findings are robust to (a) excluding utility companies (SIC codes 4900-4999) or (b) examining only manufacturing firms. Third, our findings also hold when excluding initial public offerings (IPOs), indicating that firms going public do not drive the results. Fourth, we observe a statistically significant increase in firm growth and investments in the year of issuance after excluding the years in which firms were acquirers in merger and acquisition (M&A) deals. This suggests that firms’ expansion comes from their own internal growth, in addition to any growth generated by the M&A activity. To conduct this last test and identify acquirer firms, we use the variable _Net Assets from Acquisitions_ from Worldscope. This variable represents assets acquired through pooling of interests or mergers. About 52 percent of the firms in the sample (32,629 firms) conducted an M&A transaction.

10
Between 2013 and 2015, there was a rapid rise in sovereign bond issuance with more Sub-Saharan African countries accessing international capital markets. These included Côte d’Ivoire, Senegal, Angola, Nigeria, Tanzania, Namibia, Rwanda, Kenya, Ethiopia and Zambia, generating commercial debt exceeding $17.5 billion. A total of 13 LICs and LMICs in the region accessed the bond markets during 2013 and 2015. In 2013, the largest sovereign bond issuance shares were made by Gabon ($1.5 billion), Nigeria ($1 billion), and Ghana ($0.75 billion). In 2014, the largest issuances were made by Kenya ($2.75 billion), with Ethiopia, Ghana, and Zambia accruing $1 billion each. The bulk of these bonds are due between 2021 and 2025 and they are expected to trigger an average repayment of about $4 billion annually over that interval.

**Figure 14. International bond issuances on select LICs and LMICs in Sub-Saharan Africa** _(US$ billions)_

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Angola Ethiopia Gabon Ghana Cote d'Ivoire Kenya Mozambique<br>Namibia Nigeria Cameroon Rwanda Senegal Tanzania Zambia<br><!-- End of picture text --> _Source: World Bank, Bloomberg. Published in: World Bank (2018) Africa’s Pulse, Volume 18. Washington, DC: The World Bank, October_ Sovereign debt issuance increased from an average of US$6 billion in the period 2013-15 to US$8.2 billion since 2017. Issuance has been on relatively favorable terms: the weighted average coupon is 6.5 percent and the average maturity is 20 years. Seven countries in the region account for more than three-fourths of the total bond debt issued: South Africa, Côte d’Ivoire, Ghana, Nigeria, Angola, Zambia, and Kenya. By 2018, 16 countries have issued bonds, several of them on a regular basis, with issuances of considerable size (Figure 4). Conditions for international bond issuances have been favorable, with high and steady demand from investors. In 2017, three countries (Côte d’Ivoire, Nigeria, and Senegal) raised US$ 7.6 billion. In the first half of 2018, six frontier economies in the region (Angola, Côte d’Ivoire, Ghana, Kenya, Nigeria, and Senegal) 15
- _4.3 Country-specific poverty estimates in LAC in 2011 PPPs and comparison with currentlyused poverty rates in 2005 PPPs_

It is also key to analyze whether the new poverty rates in LAC countries differ considerably with respect to the current country-specific poverty rates based on 2005 PPPs. In this section we select a poverty line in Table 3 as an example to illustrate the value of the poverty rates in LAC countries. As we showed in Section 4.1, the difference in the number of subnational poverty lines by country, the presence of outliers, and the size of the subnational population have a material effect on the value of the poverty line. Therefore, the following analysis uses the extreme and moderate poverty lines that result from weighting all subnational poverty lines by the number of individuals residing in their corresponding urban subnational region to account for the size of the population and control for the different number of subnational poverty lines across countries. The analysis also considers the specification based on the _median_ of subnational urban poverty lines to account for outliers. The corresponding extreme and moderate poverty lines that result from those controls are $2.5 and $5.3 per person per day, respectively (first row of columns 3 and 4 of Table 3). This is the most conservative set of updated regional poverty lines. Therefore, poverty rates that emerge from these two lines can be seen as the lower bound of the changes resulting from updating the PPPs.

Table 4 presents the country-specific poverty estimates in 2013 based on these selected extreme and moderate poverty lines and the 2011 PPPs. Poverty headcounts vary considerably across countries in LAC. For instance, Chile has the lowest extreme and moderate poverty rate in LAC, with approximately 1.4 percent and 8.0 percent of the population living with a per capita income lower than $2.5 and $5.3 per person per day in 2013, respectively. On the other extreme, Honduras has the highest extreme and moderate poverty rate; approximately 27.3 percent and 56.6 percent of the population live with a per capita income lower than $2.5 and $5.3 per person per day in 2011, respectively.

The last row of Table 4 shows the extreme and moderate poverty rates in LAC as whole based on the $2.5 and $5.3 per person per day poverty lines and the 2011 PPPs. By 2013, approximately 7.8 percent of the population lived with a per capita household income lower than $2.5 per person per day in 2011 PPPs, representing 40.2 million individuals. Similarly, approximately one in four Latin Americans qualified as moderately poor in 2013, living with a per capital household income lower than $5.3 per person per day in 2011 PPPs.

16
201, Dresen 2014). In order to realistically promote the uptake of these stoves among people in developing nations, however, organizations have to meet the needs of the people who will use them. Traditions, social interactions, and family dynamics differ across cultures, but they play an integral role in people’s willingness to adopt and ultimately use a new technology. Therefore, it is important to understand which aspects of stoves are important to households. Though there are a large number of studies on the adoption of improved stoves, which we discuss in the literature review, most of these studies focus on a particular stove and are not able to identify preferences for attributes of stoves. There is currently limited information on preferences and willingness to pay for particular attributes of stoves in general and this information is important for policy makers working on designing effective stoves and increasing stove adoption and the information is also needed to conduct cost benefits analysis of stove programs.

We use a choice experiment study to fill this knowledge gap and identify preferences for attributes of improved stoves. We conduct the choice experiment in rural Ethiopia. Ethiopia provides an interesting context for the study of improved stove initiatives, as about 96% of the population’s household energy demand is fulfilled by wood, charcoal, branches, dung and agricultural residues, which all produce smoke and harmful emissions when they are burned (Bizzarri 2010, Beyene et al. 2015). Within the household sector, cooking compromises virtually 100% of energy demand (Practical Action Ethiopia 2015). Every year, nearly 150,000 hectares of land, 1.1% of forest area is deforested in an effort to collect wood (FAO 2010). Dissemination of efficient stoves is an important part of Ethiopia’s strategy for the energy and forestry sectors. For example, the country’s Climate Resilient Green Economy (CRGE) Strategy projects that reducing demand for fuelwood through efficient biomass stoves could reduce emissions by almost 35 Mt CO2e by 2030 and involve about 20 million households (FDRE, 2011). We focus on Ethiopia because the Ethiopian government is planning to disseminate around 11.45 million improved fuel‐saving cook stoves during the period from 2015/16 to 2019/20 (MoWIE 2013, Accenture 2013). The results of our study can therefore potentially inform these stove dissemination efforts. Most of the improved stoves to be distributed are designed to more efficiently cook _injera_ , which is the staple bread consumed in Ethiopia and is the end us of over half the primary energy consumed in the country (Bizzarri, 2010; Practical Action Ethiopia). The Mirt (i.e. “Best”) stove is 3
As shown in the table, the unbanked population in Mexico is somewhat less educated than the banked population, is less likely to be working, and has a lower household income. These patterns are broadly consistent with those found in the U.S. survey.

# B.  Where do the unbanked get financial services?

Individuals who do not use banks still need financial services. If they have any financial savings, they need a means to safeguard their savings. If they receive income in the form of a check, they need a means to convert the check into cash. They need to pay bills. Finally, they many need to borrow periodically. This subsection draws on the survey data to examine where and how the unbanked obtain these financial services.

Table 14 presents data on the forms in which the unbanked keep financial savings and contrasts this with the banked. As noted in Table 13 above, 74.9 % of the unbanked said that they had no formal sector financial savings. The survey also asked about the use of informal means of financial savings, such as holding cash, money orders, uncashed checks, jewelry that can be sold, etc. As shown in Table 14, 18 percent of the unbanked respondents said that they use such means; only 1.1 % of the banked did. Allowing for both formal and informal means of financial savings, 56% of the unbanked had no financial savings and 0.7% of the banked had none.

|**Savins methods or sort of**<br>**Table 14. Use of inform**|**al savings meth**|**ods**|
|---|---|---|
|**g**<br>**savings**|**Banked**|**Unbanked**|
|**Informal savings**|**1.1%**|**18.7%**|
|**Formal Savings**|**59.4%**|**15.8%**|
|**Both kinds of savings**|**38.9%**|**9.3%**|
|**Without savings**|**0.7%**|**56.2%**|
|**Source: ENDESFI**|||

Table 15 examines how people receive incomes and convert their incomes into cash. Among banked individuals, 54% received a deposit into either their savings or checking account, 32 percent received a check, and 11.5% were paid in cash. Among the unbanked, 85.6% were paid by check, 7.8% were paid in cash, and 6.3% received a deposit to a savings or checking account that is controlled by an employer.

40
This paper differs from the existing literature in three respects. First, our empirical analyses cast a wider net than the existing literature which focuses only in a particular region or a country and covers a shorter time period. Second we examine time series trends in codependence and test for structural changes over time. Finally, we examine cross-country differences in co-dependence and link the differences to measures of financial and economic openness and regulatory frameworks in different countries.

Policymakers may be able to draw important implications from our analysis. Codependence in bank default risk has important consequences for systemic stability. We find increasing co-dependence in banks located in different national jurisdictions. Although we do find that strong banking supervision tends to reduce co-dependence in a given country, our results call for banking supervisory co-operation at a global level. This is especially true for larger banks which have grown more interconnected over the past decade.

The rest of the paper is organized as follows. Section 2 describes the data sources and describes the construction of the Merton (1974) default risk measure. Section 3 presents the empirical results, and finally Section 4 concludes.

# **2. Data Sources and Credit Risk Measure**

The key variables for our analysis come from _BANKSCOPE_ which provides bank-level balance sheet information, and _DATASTREAM_ which provides information on stock prices, market capitalization and stock volume. We use weekly market data and annual accounting information in creating our credit risk measure. All data items are in US dollars to make comparisons across countries possible. We compute default probabilities implied from the structural credit risk model of Merton (1974). This approach treats the equity value of a company as a call option on the company‟s assets. The probability of default is computed using the “distance-to-default” measure, which is the difference between the asset value of the firm and the face value of its debt, scaled by the standard deviation of the firm‟s asset value. The Merton (1974) distance-todefault measure has been shown to be good predictor of defaults outperforming accountingbased models (Campbell, Hilscher and Szilagyi 2008; Hillegeist, Keating, Cram, and Lundstedt, 2004; Bharath and Shumway, 2008). Although the Merton distance-to-default measure is more 4