rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2.4: Distribution of marketable debt by category of holders (1976-1990)**

|||_1976_<br>|||_1980_<br>|||_1985_<br>||
|---|---|---|---|---|---|---|---|---|---|
||Tbills|Bonds|Total|Tbills|Bonds|Total|Tbills|Bonds|Total|
|Banks|72.2|66.7|68.7|44.8|64.5|55.9|17.6|43.3|34.6|
|HHs|24.0|31.8|28.9|43.3|32.8|39.0|76.1|43.4|54.2|
|Others|4.0|1.5|2.4|11.9|3.7|5.1|6.2|13.3|11.2|
|Total|100|100|100|100|100|100|100|100|100|

*of which only 3% held by non residents; ** of which 1.4% held by  non residents; *** of which 4% held by non residents. Source: Bank of Italy, Annual Reports.

The various instruments launched in the 1980s served the purpose of attracting households and diversifying the investor base. However, they created a high degree of fragmentation, partly because of the wide variation in the characteristics of different instruments and partly because of the tendency of improvisation and short-lived experimentation, reflecting a pragmatic trial and error process. Several instruments were issued for relatively short periods, mainly because of weak investor response.

**Table 2.5: Net Spread differentials**

|Year|CCT averagerate (a)<br>BTP averagerate (b)|Differential(a-b)|
|---|---|---|
|1981|20.25<br>19.36|0.89|
|1982|20.78<br>20.21|0.57|
|1983|19.82<br>18.30|1.52|
|1984|16.98<br>15.60|1.38|
|1985|14.68<br>13.71|0.97|
|1986|12.41<br>11.47|0.94|
|1987|10.66<br>10.58|0.08|
|1988|11.25<br>10.54|0.71|
|1989|12.71<br>11.61|1.10|
|1990|12.31<br>11.87|0.44|
|1991|11.78<br>11.37|0.41|

Source: Bank of Italy, various Annual Reports.

The problems created by the fragmentation of marketable instruments were corrected in the 1990s when various measures were taken to promote the development of a liquid secondary market. However, the instruments used in the 1980s, and especially the variable-rate CCTs, faced severe difficulties in the late 1980s. In 1987, the market prices of CCTs suffered significant falls despite the fact that they carried variable coupons and were thus less exposed to the adverse impact of rising interest rates on the market price of debt instruments (see table 2.6 for spread differential between CCT and BTP).

Scarpelli (2001) suggested that some of the problems of CCTs were due to macro variables, including a “quantity effect” linked to the large volume of CCTs, a “maturity effect” emanating from the use of CCTs as the main instrument for lengthening the average life of debt<sup>10</sup> , and a “crisis effect” linked to the use of CCTs at a time of rising inflation and interest rates. Other authors suggested that some of the CCT problems originated in the approximate valuation system prevailing in the Italian capital markets, which did not differentiate sufficiently between variable and fixed-rate instruments (De Felice, Moriconi, Salvemini, 1992 and 1993).

> 10 The average life of CCTs toward the end of the 1980s was around 6 years, while that of BTPs was around 2 years.

15
economies, while public revenues exhibit similar volatility in small and large countries. The difference, however, is only significant for the fiscal balance.

Next, we assess the relationship between volatilities and size systematically, by estimating simple cross-country OLS regressions of volatilities on a continuous measure of country size. Specifically, we regress each (log) standard deviation or (log) relative standard deviation on (log) population. Appendix B presents the results with (log) labor force and (log) land area as alternative measures of country size. If smaller countries are characterized by higher volatilities, we expect the coefficient on the variable capturing size to be negative and significant. The results are presented in Table **3** .

|**Table 3. Regressions of vola**|**tilities on size:**<br>**Baseline**|**baseline and vario**<br>**w/**|**us controls**<br>**w/**|**w/**|
|---|---|---|---|---|
||**(i)**|**developing=1**<br>**(ii)**|**all controls**<br>**(iii)**|**types of FR**<br>**(iv)**|
|GDP|-0.06***|-0.06***|-0.10***|-0.09**|
|GNI|-0.17***|-0.17***|-0.16***|-0.15***|
|Employment|-0.01|-0.00|0.11|0.22|
|**Aggregate demand:**|||||
|Private cons|-0.08***|-0.09***|-0.06|-0.05|
|Gov cons|-0.01|-0.03|-0.07*|-0.07*|
|Aggregate inv|-0.06**|-0.06**|-0.08***|-0.09***|
|Priv investment|-0.06|-0.05|-0.09|-0.08|
|Gov investment|-0.04|-0.04|0.11|0.20|
|Exports|0.03|0.02|-0.00|-0.00|
|Imports|0.06***|0.06***|0.03|0.03|
|**External sector:**|||||
|Trade balance/GDP|-0.21***|-0.24***|-0.22***|-0.21***|
|Current account/GDP|-0.18***|-0.18***|-0.20***|-0.19***|
|Terms of trade|-0.04|-0.05|-0.04|-0.03|
|**Monetary/Fiscal policy:**|||||
|Inflation|0.09**|0.07**|-0.04|-0.02|
|Real int rate|-0.02|-0.01|-0.05|0.00|
|Fiscal balance/GDP|-0.12***|-0.12***|-0.06|-0.05|
|Public expenditures|-0.03|-0.08*|-0.06|-0.01|
|Public revenues|-0.00|-0.05|-0.04|-0.04|
|**Source:**|Table A1|Table A5|Table A9|Table A13|

Note: * p<0.10, ** p<0.05, *** p<0.01 Column (i) in Table 3 reports the OLS coefficients from univariate cross-country regressions of (log) volatility of various macroeconomic variables on (log) population. The results confirm the findings from the group comparisons:

1. Volatilities of GDP, GNI, trade balance, current account, fiscal balance, aggregate investment and private consumption all significantly decrease with country size.

2. Only volatility of imports and inflation significantly rises with country size.

19
submitted through non-traditional channels, impose lower minimum balances on consumer loans, and take less time to process SME loan applications. On the other hand, surprisingly banks in countries with better informational environments seem to charge higher fees on consumer loans and on international transfers. Banks in countries with poor systems of contract enforcement require higher minimum balances on savings accounts, charge higher fees on deposit accounts, require more documents to open accounts and impose higher minimum loan balances. The significant relationship between the efficiency of contractual and informational frameworks and lower barriers to banking thus matches the positive relationship between these institutions and aggregate financial development, established by the literature (Beck and Levine, 2005). We note, however, that it is mostly the barrier to deposit services that are significantly correlated with the contractual and informational framework rather than barriers to lending services, as one would have expected from the theoretical literature.

Theory does not suggest an unambiguous relationship between market structure and barriers to banking. Banks in more concentrated banking systems might either exploit their market power imposing higher barriers or alternatively, might face higher incentives to lend to smaller, more opaque borrowers such as SMEs as they can recover investment in the relationship in future periods (Petersen and Rajan, 1995). Further, the variation of barriers across countries might be affected by the dominance of government-owned or foreign-owned banks in a banking system; banks might impose higher or lower barriers in banking systems dominated by government-owned or foreign-owned banks, independent of what individual banks’ own ownership structure is. Specifically, competitive pressures or the lack thereof from a predominantly government-owned or foreign-owned banking system can push individual banks towards higher or lower banking barriers. We use data from Barth, Caprio and Levine (2004) to assess the relationship between ownership and market structure and barriers to banking.

24
lustrates that the rebels located disproportionately in high altitudes as part of their war strategy (FMLN, 1984) and that rivers often marked the boundaries of their territories.

# **III.C Development outcomes**

The long-term development impacts of guerrilla territorial control are measured using 2013 night light luminosity (as a proxy for local economic activity) and 2007 population and household census data.

_Night light luminosity_ . Data on night light luminosity comes from the Defense Meteorological Satellite Program Operational Linescan System. This data was obtained from the US National Oceanic and Atmospheric Administration (NOAA) web page. It has a resolution of 30 arc seconds _×_ 30 arc seconds (i.e., approximately 1 km _×_ 1 km) and spans 1992 to 2013. The main results use data for 2013 as it is the last year of data available from the Operational Linescan System (OLS) flown by the U.S. Air Force Defense Meteorological Satellite Program (DMSP). To study the durability of effects, we also used individual years between 1992 and 2013.

_2007 Population and Household Census_ . The General Directorate of Statistics and Censuses ( _Direcci ́on General de Estad ́ısticas y Censos_ , DIGESTYC) provided anonymous microdata from the 2007 census for this study. The data includes the socioeconomic characteristics of all households and individuals, including but not limited to labor market outcomes; educational attainment; material ownership (e.g., having a car, a TV, etc.); use of public services (electricity, sewerage, and others); migration; and other characteristics of all dwellings in El Salvador.

_2007 Census Cartography_ . DIGESTYC also provided maps of the tracts for the 2007 census. Each tract represents a small area with a fixed geographic perimeter. In 2007, the average tract in our sample included 131 households and 473 individuals. Small tract units facilitate accurate identification of guerrilla territorial control, which is approximated using the geographic coordinates of the tract centroids.

In sum, we explore the effects of FMLN control via night light luminosity,<sup>17</sup> human capital (measured as years of education and literacy rates), and a wealth index (constructed as suggested by the Demographic and Health Surveys program).<sup>18</sup> The wealth index is the first factor from the

> 17The challenge of night light luminosity data is the significant fraction of observations that take the value of zero and also the existence of extreme values in the right tail of the distribution (Michalopoulos and Papaioannou, 2013; Pinkovskiy and Sala-i Martin, 2016). To account for this concern, the outcome is transformed using the inverse hyperbolic sine transformation, which can be interpreted as a logarithmic dependent variable (Pence, 2006).

> 18Step-by-step instructions for constructing the index are available at: https://dhsprogram.com/topics/

17
Table 7 Electronically Traceable Payments as Percentage of Private Final Consumption Expenditure

|**Yea**|**r**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Priv<br>con<br>exp|ate final<br>sumption<br>enditure (1)|480.2|509.9|546.4|579.1|594.9|636.7|679.1|707.6|727.8|748.2|771.2|
||Credit card<br>(2)|190.5|214.8|241.1|279.3|303.9|350.7|398.5|451.3|441.7|453.9|503.6|
||Check card<br>(3)|7.8|12.7|18.8|27.9|36.7|51.5|66.9|82.8|92.7|112.7|131.5|
|ETP|<br>Cash receipt<br>(4)|18.6|30.6|50.2|61.5|68.7|76.0|80.9|82.4|85.5|91.7|96.6|
||National<br>taxes,<br>etc.<br>(5)|<br>∙∙|∙∙|∙∙|∙∙|∙∙|∙∙|8.1|19.2|18.9|22.6|46.3|
||Sum (➅=<br>2+3+4‐<br>5)|216.9|258.1|310.1|368.7|409.3|478.2|538.2|597.3|601.0|635.7|685.4|
|Rati<br>(➅|o<br>/1)|45.2%|50.6%|56.8%|63.7%|68.8%|75.1%|79.3%|84.4%|82.5%|85.0%|88.9%|

_Source:_ The Ministry of Strategy and Finance.

_Notes:_ ∙∙ = negligible.

a. MoSF, Korea, put together data from private final consumption expenditure (National Accounts, Bank of Korea), credit card and check card payments (the Credit Finance Association), debit card payments (Bank of Korea), and cash receipts (National Tax Service).

- b. Business card payments were subtracted from credit card and check card payments.

- c. National taxes and four other national insurance premiums paid by credit cards were subtracted from ETP.

32
We now examine how the effects of information disclosure on firms’ financial obstacles, corruption obstacles, and growth depend on the level of institutional development of the country in which the firm resides. We thus add to the equation _PropertyRights_ and its interactions with _Audit_ , and re-estimate the equation with both OLS and 2SLS regressions. The results on all three outcome variables are reported in Table 11.

Since the 2SLS is more defendable in terms of filtering out selectivity, we focus on the 2SLS results. First, the negative effect of _Audit_ on the incidence of moderate-and-more-severe financing obstacle is doubled when Property Rights increases by one SD. Thus, better property rights are associated with stronger benefits of information disclosure on access to finance. Second, the positive effect of _Audit_ on the incidence of moderate-and-more-severe corruption obstacles is slightly smaller, though this effect is not statistically significant. Third, the negative effect of _Audit_ on sales growth is significantly smaller. A one-SD improvement in the country’s property rights protection leads to 12.3 percentage points increase in the effect of auditing on sales growth rate; or, it reduces the negative effect of auditing on sales growth by 43 percent. The effects of property rights protection on access to finance and on sales growth are thus substantial.

# **VII. Conclusions**

Using the WBES data from 2006 to 2014 for more than 70,000 firms in 121 countries, we study the effect of financial information disclosure through auditing on firm level financial and corruption obstacles and on firm growth. We find that disclosure is a double-edge sword. On the one hand, disclosure leads to a somewhat lower level of financial obstacles, though the results are not robust. On the other hand, disclosure also leads to a significantly higher level of corruption obstacles faced by the firm. Our explanation is that our sample countries surveyed are largely developing countries with underdeveloped markets and institutions, and that, once firm information is disclosed, the threat of government expropriation is widespread. Information disclosure thus allows rent-seeking 24
Monte-Carlo tests with an HMR-based DGP, yields coefficient estimates above those of the other Poisson estimators, but still well below those for traditional estimators. The HMR estimator is an outlier in the other direction, with much lower coefficients on GDP, and somewhat lower coefficients on distance, than the PPML-based estimators.

The results for the Anderson-van Wincoop formulation are presented in Table 10 with model estimates when we control for firm heterogeneity à la Helpman, Melitz and Rubinstein. The results on the log of distance variable are very similar with this model to those for the traditional gravity model. The PPML model results for this coefficient are clustered around -0.8, while those for the traditional model are around -1.4. The HMR Model is again an outlier with a coefficient of -1.14.

# **VII. Reconciling Monte Carlo and Empirical Findings**

The contrast between the empirical results and those from the Monte Carlo analysis raises important questions. Why, in particular, is there so little difference between the results from the PPML model and the truncated PPML, and why are the truncated PPML and the ZIP model results identical? One possible explanation lies with the distribution of the continuous explanatory variable used in the Monte Carlo analysis. By using a standard normal variable, we have ensured that, with the unit coefficient used in the Monte Carlo analysis (and assuming a zero value for the 0-1 variable, a relatively small fraction of the Xiβ values have values near zero. For any given distribution of the error term this reduces the frequency of zero observations and requires larger adjustment terms such as the – _k_ in the ET-Tobit model to obtain any given frequency of these observations.

One simple approach—consistent with the Leung-Yu approach of ensuring that the distribution of the explanatory variables in a Monte Carlo analysis is consistent with the parameters of the empirical distribution being studied—is to examine the distribution of a composite, continuous explanatory variable consistent with the empirical data set. To do this for the SST empirical dataset of, we combined the three signature explanatory variables 18 of the SST empirical data set into a composite variable Log[(GDP_Exporter*GDP_Importer)/(Bilateral distance)<sup>1.3</sup> ], where the coefficients on GDP and on distance are based on averages of the estimates in SST (2006, p650). To allow

> 18 We performed the same experiment with the parameters of the PPML model and found qualitatively similar results.

24
In principle, the FVA calculated according to the world-level perspective can be used to address the following question: ‘what part of country _s_ ’s GDP enters other countries’ exports?’ In particular, it can be computed through equation (52) (or in (54)) by tracking a specific country of origin of the value-added _t_ in the exports of all the other exporting countries _s_ = _t_ toward all the bilateral partners _r_ . However, the same question can be addressed directly in the country-level sourcebased framework by subtracting the component of direct absorption from the total DVA of the exporting country itself (i.e. � _Gr_ = _s_<sup>**DVA**</sup><sup>_sourcesr−_�</sup><sup>_G_</sup> _r_ = _s_<sup>**DAVAX**</sup><sup>_sr_,</sup> see equations (16) and (20)). Notably, this is the numerator of the **GVC** _forward_ indicator of equation (27) computed for the total exports of a country (i.e. summed across bilateral importers).

Finally, it is worth noting that at world level all the FVA components are also recorded as DVA in the flows of other countries, meaning that they are already double counted GDP in exports. It is thus questionable to argue a certain item should be recorded as FVA only once in world trade flows in order to avoid double counting of the same production in trade.

# **5.2 Critical aspects in other methodologies**

Different approaches and perspectives can be used in the value-added accounting of gross exports, the choice depending on the specific phenomenon under investigation. However, it does not mean any alternative accounting decomposition should be considered completely correct or sound from an economic point of view. First of all, each component should _measure_ precisely what it is supposed to _measure_ . Then, since each approach is suited to gauging a certain phenomenon properly, every decomposition of trade flows should maintain an internal consistency (i.e. each component should be consistent with the specific approach adopted). Falling short of any of these requirements leads to inaccurate measures and/or impairs the possibility to answer a particular question accurately.

Here we discuss some critical aspects of other accounting frameworks based on a ‘country-level perspective,’ by comparing them with the decompositions presented in Section 3. We first consider Koopman et al. (2014) and then extend the analyses to other contributions. To make the discussion clearer in equation (D.2) in Appendix D, we re-write our bilateral breakdown so that the components match directly those proposed in Koopman et al. (2014) (see equation (D.1) in Ap38
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

||**III. Market Structure**|||
|---|---|---|---|
|**Focus area**|**Indicator**|**Data Source (e.g.**<br>**UK)**|**Source/reference**<br>**(example)**|
|_? Degree of vertical_<br>_integration_|**? Energy delivered by vertically integrated**<br>**firms (own generation as % of total demand)**|Questionnaire to<br>regulator||
|_? Number of firms_|**? Number of G, T, D firms**|||
|_? Ownership structure_|**? Private, local, government owned G, T, D**<br>**firms (size - MW/MWh, number of firms)**||Steiner (2001)|
|_? Wholesale market_|**? Is there a wholesale generation market?**<br>**(no/yes – type: e.g. single-buyer, cost-based**<br>**pool, voluntary or compulsory pool**<br>**participation, demand-side participation,**<br>**bilateral/forward contract market, balancing**<br>**market, IPPs**||As above|
|_? Wholesale market_<br>_concentration_|**? Market share of 1**<sup>**st**</sup>**-5**<sup>**th**</sup>**largest generators**<br>**(as % of total capacity).**<br>**? HHI of generation firms (index)**|||
|_? Access to networks_|**? Are there provisions for generators to**<br>**have access to transmission network? (no, yes**<br>**– rTPA, nTPA)**||Steiner (2001)|
|_? Retail market opening_<br>_? Interconnections_|**? Degree of market opening (% of total**<br>**consumption liberalised)**<br>**? Threshold level for retail customer choice**<br>**(MW, MWh)**<br>? Retail competition (no., % share of<br>independent suppliers in different market<br>segments)<br>? Degree of switching from incumbent<br>suppliers (no. and % of eligible customers)<br>? Interconnections with other systems<br>(capacity, % share of total market)||Steiner (2001)|
|_? Independent Power_<br>_Producers (IPPs)_|**? Significance of IPPs (no., share, and type -**<br>**long-term contract, merchant)**|||

21
-2 -

quality programs in selected U.S. locations.3<sup>3</sup> Recently, broad estimates of the health benefits of controlling ozone and particulate matter were provided for both the U.S. and for the ambitious control plans being considered in Southem Califomia. **4.6** The analysis reported here uses a similar approach to estixizt. health effects of criteria pollutants in Jakarta, with two improvemens: the most recent set of research fmdings are utilized and a full range of health endpoints are included. Dose-response funcions that relate various health outcomes to air pollution are taken from the available peer revI,ewcd literature. Estimates of selected health effects of air pollution are generated by applying these functions to ambient levels either observed from monitoring stations located throughout the city or estimated from available dispersion models. Using results from both time-series and cross-sectional epidemiologic analyses from the United States, Canada, and Britain, effects are estimated for such health outcomes as premature mortality, hospital visits and admissions, emergency room visits, restrictions in activity, acute respiratory symptoms, acute bronchitis in children, asthma attacks, IQ loss, and blood pressure changes. At this time,.however, because of uncertainties about the coverage and representativeness of the existing monitors in the city, and about the applicability of health studies undertaken in the U.S. to the developing world, the results should be viewed as providing only general estimates of the impacts of air pollution.

Following this introduction, there are four sections in this report. Section II describes the methodology for estimating the health effects associated with changes in air pollution. The section also details the data and baseline assumptions that are necessary fo: such estimations. Section 11 provides a brief review of the literature that quantitatively links changes in air pollutants with adverse health outcomes. From this review, dose-response functions, along with associated confidence intervals, are developed. Also, suspected health effects for which quantitative estimates cannot be provided, are