rafmacalaba/datause-encoder-data huggingface dataset:
These papers provide a framework to study sovereign defaults but do not provide reasons why do some countries default considerably more times than others given similar fundamentals.

Recent work by Hatchondo, Martinez and Sapriza (2008) and D’Erasmo (2008) take a political economy approach. In their models, international lenders face different types of borrowers. Policymakers of the borrowing country that have different degrees of impatience alternate in power. Lenders cannot directly observe a borrower’s type and, hence, have to infer from the borrower’s actions. My paper differs from these in two ways. First, in the model, default risk does not depend only on the policymaker in power. What is essential is the institutional environment in which these powerful groups take actions. Second, since I focus on explaining the cross-country difference in terms of sovereign default frequencies, I do not try to match with a particular country’s data, as the other papers do.

This paper is organized as follows. Section 2 provides details of the data used in the cross-country empirical analysis and shows the findings. Section 3 presents a two-period model with default option in period two with a closed-form solution. It shows that with weak institutions, default is more likely. Furthermore, default probability is increasing in polarization. In Section 4, the analysis embeds the two-period model into an infinite horizon framework. Section 5 compares simulation results with the data. Section 6 concludes.

# **2 Data and Findings**

This section documents the cross-country differences in terms of institutional quality and sovereign default frequencies, and establishes the facts the paper aims to explain using the models that developed in Sections 3 and 4. The data set covers more than 90 countries. It includes measures of quality of institutions, sovereign default history and economic indicators from 1960 to 2008.<sup>4</sup>

# **2.1. Data Description and Source**

The first key measure of government characteristics is government polarization (polar), which captures how different are the preferences of the governing parties on certain issues, as well as

> 4 Our main data set spans from 1960 to 2008. However we use sovereign default crisis data from 1800 to 1959 to construct a measure of default history.

5
engaged in corporate lending. In addition to government papers and receivables from the central bank, corporate loans were the most important contributors on the asset side (Chart 1).

**Chart 1. Asset structure of the Hungarian banking system (1993–2004)**

<!-- Start of picture text --> 100 Per cent<br>80<br>60<br>40<br>20<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Public sector Corporate credit<br>Household loans Central bank<br>Financial institutions Insurance companies, financial entities and others<br>Foreign assets<br><!-- End of picture text --> Source: Magyar Nemzeti Bank (MNB).

On the borrowers side, loans had/have a key role in funding firms. Owing to the underdeveloped capital market, the ratio of securities other than shares is still below 2 percent on the liability side of firms, and it has remained broadly unchanged since the transition. Accordingly, loans represent the main external liabilities. By the end of 2004, the share of loans on the liability side of the corporate sector rose to over 30 percent, compared to 18 percent at the beginning of the 1990s. Besides the domestic banking system, direct borrowings from abroad also contributed significantly to this growth. While they also represent a high share within net inflows, their magnitude should be handled with caution. Occasionally, the inflow of foreign funds may be related, for instance, to the liquidity management of foreign parent companies rather than the financing of business operations (Chart 2).

**Chart 2. Net change in loans outstanding to nonfinancial corporations**

<!-- Start of picture text --> HUF Bn HUF Bn<br>300 300<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>-50 -50<br>-100 -100<br>-150 -150<br>-200 -200<br>Loans from banks Loans from financial enterprises Total domestic lending<br>1996Q1 Q2 Q3 Q4 1997Q1 Q2 Q3 Q4 1998Q1 Q2 Q3 Q4 1999Q1 Q2 Q3 Q4 2000Q1 Q2 Q3 Q4 2001Q1 Q2 Q3 Q4 2002Q1 Q2 Q3 Q4 2003Q1 Q2 Q3 Q4 2004Q1 Q2 Q3 Q4<br><!-- End of picture text --> Source: MNB. Note: ―Banks‖ includes banks, branches, and cooperative credit institutions. Direct loans from abroad are not presented. The high volatility they typically exhibit is often unwarranted by real economic activity and merely reflects the internal liquidity management of the corporate group. The outstanding amount of direct foreign loans was marginally less than domestic loan volume.

4
there is variation in labor market participation across gender. Among working age (18 - 60 years) males, 49.8% of them reported to be paid employees and 70.9% of them reported participating in selfemployment, mostly subsistence farming. Among females, only 23.1% reported to be paid employees while 66.3% of them reported participating in self-employment. This is not surprising given the strong social and traditional family norms in Nepal, which discourage women from participating in paid-work, and where women mostly engage in taking care of children and household chores (CEDAW, 2003). However, with males migrating abroad, the authority over household consumption and investment might shift to female members. Women might become more involved in making decisions on labor market participation as well. Besides continuing to care for children and engaging in household chores, women in Nepal often take up men's roles in family farming and enterprises when male members are abroad (Nandini, 1999). Similarly, women play key roles in deciding the use of remittances and running bazaar economics when husbands are away (Brown and Conneil, 1993). The male-dominated international migration in Nepal may aect non-migrant male and female members in the household dierently.

# 3 Data and Descriptive Analysis

For this study I use the 2010-2011 round of the Nepal Living Standard Survey (NLSS III) as the primary data source. It is a nationally representative survey of households and communities that is conducted by the Central Bureau of Statistics (CBS) Nepal, with assistance from the World Bank. It was administered between February 2010 and February 2011. It has a panel component of 1,128 households. Half of the households were followed from the rst round and the other half from the second round. The cross-sectional sample has 5,988 households, which were selected in three stages.<sup>4</sup> The survey collected detailed information on multiple topics related to household welfare. The survey provides rich information on household consumption, sociodemographic composition of households, health and education attainment of the members, labor market outcomes of the household members, and the source of a wide range of household incomes. It contains detailed information on time-allocation for wage-employment and household production of every household member. Households were also asked to provide information on remittances received by the house-

> 4For detailed description of the sample design and the methodology, see http://cbs.gov.np/

6
# **-** **<u>Table 6: Majority FDI Firms, by Sector and Region</u>**

|Industry|Northwest|Central|South|Volga|Ural|Siberia|Far East|North Cau|
|---|---|---|---|---|---|---|---|---|
|AGRICULTURE,FORESTRY AND FISHING|3.0%|2.0%|8.1%|5.3%|1.5%|4.3%|11.8%|11.9%|
|MINING ANDQUARRYING|1.9%|1.0%|2.4%|4.0%|7.9%|8.6%|11.2%|2.8%|
|MANUFACTURING|16.7%|10.7%|15.2%|23.7%|16.6%|14.0%|9.7%|18.2%|
|ELECTRICITY,GAS,STEAM AND AIR CONDITIONING SUPPLY|0.8%|0.7%|0.9%|1.8%|2.9%|4.5%|0.0%|2.8%|
|WATER SUPPLY;SEWERAGE,WASTE MANAGEMENT AND REMEDIATION ACTIVITIES|0.7%|0.4%|0.9%|2.3%|1.3%|1.4%|1.0%|0.7%|
|CONSTRUCTION|7.8%|7.1%|8.9%|6.6%|6.7%|7.6%|7.1%|4.9%|
|WHOLESALE AND RETAIL TRADE;REPAIR OF MOTOR VEHICLES AND MOTORCYCLES|24.4%|30.1%|24.9%|23.4%|24.0%|24.5%|26.0%|21.7%|
|TRANSPORTATION AND STORAGE|8.5%|3.8%|12.0%|4.2%|5.3%|4.4%|9.2%|7.7%|
|ACCOMMODATION AND FOOD SERVICE ACTIVITIES|1.6%|1.1%|2.7%|1.3%|2.5%|1.6%|2.8%|4.9%|
|INFORMATION AND COMMUNICATION|4.5%|6.1%|2.4%|5.0%|3.4%|6.5%|2.2%|2.8%|
|FINANCIAL AND INSURANCE ACTIVITIES|3.8%|9.8%|3.7%|3.8%|5.7%|2.4%|1.3%|0.7%|
|REAL ESTATE ACTIVITIES|14.0%|12.4%|8.9%|8.0%|11.4%|7.6%|4.6%|11.9%|
|PROFESSIONAL,SCIENTIFIC AND TECHNICAL ACTIVITIES|7.7%|10.7%|5.4%|6.4%|6.7%|8.2%|7.9%|4.2%|
|ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES|3.4%|2.4%|1.5%|2.5%|2.0%|2.7%|3.6%|0.7%|

_Source:_ Ruslana.

0
risk is high.

An aspect of hedging that does not receive much attention is the fact that hedging carries an opportunity cost in terms of foregone returns. Whether the hedger considers these costs reasonable or not depends upon the hedger's degree of risk aversion. We turn now to a discussion of these costs and the effect of risk aversion on the hedging decision.

We estimated ex-post optimal hedge ratios at different levels of risk aversion using the 1994 futures contract as an example. Table 3 reports optimal hedge ratios at different levels of risk aversion and associated return and risk levels. For values of X between 100 and infinity, the optimal hedge ratio is essentially constant implying that for these values of risk aversion the 1 speculative component is insignificant". Thus, it seems that the optimal hedging strategy is not significantly different for reasonable levels of risk aversion. At values of X equal to or lesser than .10, the results imply that Ecuador should buy rather than sell futures (i.e. negative values of h** imply a long position in futures). This is not surprising in view of the relation that existed between Ft and E(F,+1) over the life of the 1994 contract. Over the hedge period, the mean value of (Ft+1-Ft) was equal to .0508 (U$/barrel). Given that the expected futures price, on average, exceeds the current futures prices over the life of this contract, the recommendation is to go net long in futures at lower levels of risk aversion to profit from this price bias.

We calculated portfolio returns and variances for hedge (h) ratios between 0 and 1. These results are reported in Table 4 and graphed in Figure 2. Figure 2 is a mean-standard deviation portfolio opportunity frontier and depicts the return and risk trade-offs from hedging 11 This result is similar to Rolfo's (1980) result on optimal hedging for cocoa producing countries and Ouattara, Schroeder, and Sorenson's (1992) work on coffee hedging for C6te d'Ivoire.

13
**Table C2. Food items and budget shares in CPI price database (continued)**

|Consumption Survey Group|Budget Share|CPI Item|
|---|---|---|
|Sausage (all types)|0.000|Sausage|
|Grasscutter|0.002|Bushmeat|
|Game bird|0.000|(a)|
|Other meat|0.001|Dog meat|
|Fresh/frozen fish|0.015|Frozen fish|
|Shrimps|0.000|Shrimps|
|Snails|0.001|Snails|
|Crab|0.001|Crab|
|Smoked fish|0.058|Smoked river fish|
|Smoked herring/salmon|0.091|Fish and seafood|
|Fried fish|0.013|Fish fried|
|Dried fish|0.004|Dried fish - koobi|
|Other fish|0.009|Fish (salted)|
|Sardines/tuna in vegetable oil|0.004|Tuna in vegetable oil|
|||Tuna, canned, starkist|
|||Tuna, canned, geisha|
|||Sardines in vegetable oil|
|Mackerel in tomato sauce|0.004|Mackerel in tomato sauce|
|Fresh milk|0.001|Milk (fresh)|
|Powdered milk|0.004|Powdered milk|
|||Peak (sachet)|
|||Powdered milk (sachet)|
|Evaporated milk|0.007|Evaporated milk|
|||Cowbell (sachet)|
|Condensed milk|0.000|(b)|
|Other milk products|0.005|Fan milk products|
|Chicken eggs|0.009|Chicken eggs (fresh, single)|
|Margarine|0.000|Margarine|
|Coconut oil|0.000|Coconut oil|
|Groundnut oil|0.002|Oils and fats (ND)|
|Palm oil (red oil)|0.008|Palm oil (red oil)|
|Shea butter|0.005|Oils and fats (ND)|
|Palm kernel oil|0.000|Cooking oil|
|Other oils|0.021|Oils and fats (ND)|
|Coconut (fresh/dried)|0.002|Coconut (fresh)|
|Banana|0.005|Banana|
|Oranges|0.003|Oranges|
|Pineapple|0.001|Pineapple|
|Mango|0.001|Mango|
|Watermelon|0.003|Watermelon|
|Avocado pear|0.001|Avocado pear|
|Apples/sweet apple|0.001|Apples (foreign)|
|||Sweet apple|

43
_Africa’s Macroeconomic Story_ _Geography -- landlocked countries have outperformed coastal countries_ Trend growth in landlocked countries was well below that in coastal countries during the 1960s, but the relationship reversed in about the mid-1990s (Figure 3). Growth in landlocked countries has outperformed that in coasted countries by over 1.5 percentage points over the past five years.

**Figure 3: Trend Real GDP per Capita Growth - Landlocked versus Coastal Countries**

_Percent_ <!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>1962 1972 1982 1992 2002 2012<br>Coastal Landlocked<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ database.

_Commodity exporters have prospered but the gains are concentrated in just a few countries_ The surge in world commodity prices beginning in the mid-2000s (Figure 4) is generally thought to be a major factor sustaining high growth in the region. Resources dominate export revenues in 20 of 45 countries in the region, 7 of which are classified as major oil exporters (Angola, Cameroon, Chad, Equatorial Guinea, Gabon, Nigeria, and the Republic of Congo).<sup>5</sup> The other 13 resource-rich countries receive more than 25 percent of export receipts from other minerals and metals.

> 5 Defined as countries where oil revenues account for over 20 percent of total revenues (excluding grants) (IMF 2012a, Box 3.1).

12
# **5 Results**

Results will be presented for the discrete and continuous distance treatments, followed by a visual presentation of the results when the threshold value for the former is moved from 10km distance to the typhoon path to 200km in increments of 10km. With these results firmly established, robustness checks will look at placebo paths for the typhoon, at different income cutoffs for the sample used, and at whether exposure to the typhoon had a causal effect on beneficiary status.

## **5.1 Main results**

Tables 2 and 3 show the study’s principal results for households living within a 200km wide band on either side of the typhoon path. For the discrete distance treatment, presented in table 2, this implies that households within 100km from the path are considered treated, whereas those living at a 100200km distance act as the control group. The 100km cutoff was chosen for its virtue of being a round value. As will become clear below, the largest effects are found at cutoffs between 100-150km, so the results presented here can be thought of as conservative estimates. Moreover, results are presented for all six outcomes of interest with and without the inclusion of municipalbeneficiary fixed effects. The aim is to show that their omission does not change the results in any qualitatively important manner. The upshot is that the results are not an artifact of unobserved characteristics at the munici18
<!-- Start of picture text --> 100<br>e 8<br>80} ® e. % °<br>oe,” god, ee .4 © 6 e %%e<br>_ ¢owe?2Re e oe? co é ° 8 oe fo}<br>& 60 b of 4 @ ge e °<br>: a<br>5go she 8<br><!-- End of picture text --> <!-- Start of picture text --> g 4) S.%<br>eo @ °<br>oo @ e<br>e 8&8<br>20<br>@®e@ Without poverty traps<br>®®@ With poverty traps<br>°% 10000 20000 30000 40000 50000 60000<br>GDP per capita (USD)<br><!-- End of picture text -->
_δKt_ = _λ_ ( _Kt_<sup>_f−Kt−_1)</sup> (9) Here _Kt_ and _Kt_<sup>_f_representtheactualandoptimallevelsofcapitalattime</sup><sup>_t_,whilethe</sup><sup>_λ_</sup> parameter represents how much of the gap between these values is bridged in each time period. Lower values of _λ_ imply slower rates of adjustment and, thus, higher adjustment costs. As adjustment costs may differ for different levels of desired investment, Chow tests are conducted at different points along the investment function to test the continuity of its slope for each type of capital.

# **3 Data**

## **3.1 Overview**

We use a panel of firm-level data for manufacturing firms in the Republic of Ireland. These data are collected by the Irish Central Statistics Office (CSO) via the annual Census of Industrial Production (CIP). Response to the CIP is compulsory for firms operating in the Irish manufacturing sector with three or more persons engaged. The census collects data on various accounting measures such as sector of operation (at the NACE 4 digit level), location, sales, employment, intermediate inputs, capital acquisitions and trade. While all firms of three or more employees are surveyed, larger firms are asked to complete a more detailed questionnaire which includes, among other additional information, information on energy expenditure disaggregated by fuel type (smaller firms are asked only for aggregate energy expenditure). As we are interested in adjustment costs of fuel-using capital by type of fuel, we concentrate our analysis on these larger firms and for the period from 2004 to 2009, when these more detailed data were collected on an annual basis. Our final dataset contains approximately 8,600 firm-year observations.

The census does not ask firms to report a price for capital, therefore, the price of capital we use in our model is the market cost of capital as estimated for Irish manufacturing firms by ˇZnuderl and Kearney (2013). This cost is a function of the investment price and the nominal interest and depreciation rates. Additionally, fuel prices are not recorded in the census and, as such, a number of external sources are used. The prices of oil and coal are from the ESRI Databank (ESRI, 2012), while the prices of electricity and natural gas come from Eurostat’s price series for industrial users.<sup>4</sup> The Eurostat price data vary according to the quantity of fuel used. In Ireland firms face decreasing block pricing for electricity and gas, whereby prices are lower at higher consumption levels. However, as we do not observe the quantity used, firms are assigned to consumption-based price bands as follows: for each two-digit NACE sector we calculate the energy intensity of output in that sector by dividing total sectoral electricity and gas usage (based on aggregate data) by total sectoral output. This gives us an average, sectorlevel measure of energy-intensity of output separately for electricity and natural gas. Then, for

> 4http://ec.europa.eu/eurostat/web/energy/data/main-tables

8
For certification costs which will consist of inspection, timber tracking and monitoring costs, so far there are no reliable estimates. These costs will depend mainly on the availability of information on the forest inventory and adequacy of forest maps. In developed countries where expertise and information systems have been developed in the forestry field, the cost is estimated to range between US$0.30-$0.60 per hectare. In developing countries, rough estimates suggest that the certification cost will be in the range of 5-10% of existing logging costs. A significantly different cost estimate was reported by Septiani and Elliot (1994). SGS/Indonesia estimated a US$1.30 cost for tracking per cubic meter timber while SGS/New Zealand came up with US$7 per cubic meter.

In the context of costs for TC scheme, international mutual recognition and harmonization of principles, standard and criteria becomes a central issue to developing countries that export timber products. If each developed country that imports timber products were to impose its own requirements, or if each of them were to subscribe to different certification or accreditation systems, it is conceivable that the costs would become prohibitively high for developing country exporters to enter developed country markets.

At the national level, the eco-labeling and timber certification initiatives may also involve significant costs like foregone export earnings and/or opportunity costs in terms of the resources committed to develop the eco-labeling schemes. For example, the Indonesian Government recently announced its commitment to reduce the sustainable log harvest from 31.4 to 22.5 million cubic meter per year to be reached over the next five years. If this is considered to be a means of implementing the sustainable forest management, it will cost the country at least US$300 million per year in the form of foregone foreign exchange revenue in terms of phywood exports (see Ahmad, 1994).

The rough estimates presented in this section indicate that the combined costs of compliance, the certification costs, and the foregone export earnings could be significant. These costs further qualify the financial benefits of implementing TC.

29
would come from Mexico, a country that by 2003 had seen tariffs on its exports to the United States drastically reduced as a result of NAFTA.

We should point out that our forecasts are in line with other studies. For example, a United States International Trade Commission report (USITC 2004, Table 4-4) analyzing the potential impact of CAFTA on trade patterns estimates that US imports from the five Central American counterparts in the agreement (Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua) and from the Dominican Republic would increase by 26 percent, which falls between our range of forecast for the Caribbean and Central America. With regard to the FTAA, Hertel et al (2004) estimate that total US imports would rise by around 2.2 percent, whereas Watanuki and Monteagudo (2002) put that figure at 1.1 percent; in contrast, we estimate an increase of only 0.4 percent in aggregate US imports.

Our results highlight the importance of preferential trade between the United States and Latin America for boosting exports from the region. The flip side is a small reduction in exports from China and the rest of the world to the United States of around 0.3 and 0.1 percent, respectively. The largest declines, as expected, would occur in exports of leather, apparel and textiles, and in manufacturing in general.

# **_5.2.2 Elimination of textile quotas:_**

Table 10 presents a breakdown of US apparel imports by region. As we can see, China's share of US apparel imports rose from 13.2 to 18.6 percent from 2000 to 2004. During that same period, Latin America's share of the US market declined from 30.8 to 26.3 percent. China's increasing market share, and Latin America's loss, came despite the fact that tariffs on imports from the latter region declined more than those on Chinese goods. One potential explanation for the rising presence of Chinese apparel was the elimination, in 2002, of a number of import quotas on textile and apparel imports originally adopted under the Multi-Fiber Agreement (MFA). MFA quotas were binding for China and other Asian nations, limiting market access on apparel exports from those countries. During the Uruguay Round, countries agreed to dismantle such quotas gradually, removing them altogether by January 1st, 2005. Thus, the recent implementation of the final stage in the elimination of textile quotas in the United States and elsewhere has created widespread apprehension in Latin America that unfettered 15