rafmacalaba/datause-encoder-data huggingface dataset:
treasury bills that were issued in physical form could be renewed at maturity). A sale commission was also paid on government bonds (Table 3.4).

**Table 3.4: Commissions on Treasury Bills and Government Bonds**

|Treasury Bills|Sale<br>Renewal|
|---|---|
||Commission<br>Commission|
|3 months|0.20%<br>0.35%|
|6 months|0.20%<br>0.35%|
|12 months|0.45%<br>0.40%|
|Government Bonds||
|1 year|0.45%|
|2 years|0.50%|
|3 years|0.60%|
|4 years|0.75%|
|5 years|0.95%|
|6 years|1.10%|
|Source: Bank of G|reece, Economic Bulletin, 7, 3/96:34|

Any opposition from commercial banks to the use of their branch networks for selling competing products was mitigated by the expectation that a better developed government bond market would enable the authorities to resume the liberalization of banking policies and especially to reduce and eventually abolish the abnormally high compulsory treasury bill investment ratio. In fact, the commercial banks’ compulsory investments in treasury bills were lowered from 40 to 10 percent of their drachma deposits in 1991 and were abolished in June 1993.

Commercial banks were given the option and chose to convert the greater part of their formerly compulsory treasury bill holdings into long term bonds that were earning higher yields and were freely marketable. Conversion bonds were introduced in 1990 but their use was intensified between 1991 and 1993. They were accompanied by the use of consolidation bonds to cover obligations arising from the non-servicing of government-guaranteed loans. The bulk of conversion and consolidation bonds was issued with a 7-year maturity and, in some cases, a 3- year grace period. They earned interest at a variable rate linked to the 12-month Treasury bill rate plus a spread that was set for each issue.

The level of central government debt increased further from 73 percent of GDP in 1985 to 119 percent in 1995 (Table 3.5). Non-marketable debt, which accounted for 61 percent of total debt in 1980, declined to 22 percent in 1995. The share of treasury bills initially rose from 39 percent in 1980 to 46 percent in 1990 but then fell to 26 percent of the total. The main increase took place in variable cost bonds, which include both variable-rate and foreign-currency-indexed bonds. They reached 47 percent of total debt in 1995. Foreign debt rose between 1980 and 1985 but then declined and accounted for one fifth of total debt. The total debt data for 1995 refer to central government debt, before deducting net holdings of government debt by other public 30
Following e.g. Mancini (2005: 9) I reran all the models with an alternative measure of horizontal inequalities , the group-based coefficient of variation (GCOV)<sup>24</sup> , weighted by group size for all the groups consisting of more than 1% of the population in each country (results not reported here). Most of the effects remained quite similar, but dropped below significance in many models. It is hard to say whether this means that horizontal inequalities may be less important than suggested here, or if it is just signaling that HIs are extremely complex phenomena and very sensitive to different measurement techniques. The implication of the latter seems to be that whenever possible, horizontal inequalities should be measured and analyzed at the subnational level. A handful of quantitative case studies of particular countries have done exactly this (see e.g. Mancini, 2005; Murshed & Gates, 2005; Tadjoeddin, 2003), but this of course requires disaggregation of the dependent variable and preferably the other independent variables (see also Østby, Nordås & Rød (2006) for a preliminary disaggregated analysis of inter- and intra-regional inequalities and civil conflict in 21 African countries). Despite the advantage of disaggregated studies of conflict, political variables such as regime type and electoral system (which are important indicators in this paper) are by definition country-level variables. Testing the combined effects of subnational and national variables could be carried out in a multilevel model, (see e.g. Goldstein, 1995).

The search for the underlying causes of civil wars is an on-going one, and few definitive answers can be expected. Yet, because of the heavy costs and human suffering that are continuously imposed by on-going civil conflicts, it is important to take some action on the basis of our current knowledge. The main policy implication that can be drawn from the results reported in this paper is the importance of addressing horizontal inequalities. Political institutions are not sufficient to ensure peace. The findings of this paper support Stewart & O’Sullivan’s (1998) conclusion drawn from case studies: The combination of two factors seems to be of utmost importance in mitigating conflict: First, the establishment of politically inclusive government which incorporates representatives from all the major identity groups at the political level, and second, the realization of a social system which widely spreads the benefits of progress, providing socioeconomic growth among all the significant regional, religious and ethnic groups in society. In other words, what seems to be required in order to ensure peace in developing countries is the combination of politically _and_ economically inclusive government.

> 24 Note that the weighted group coefficient of variation had already been proposed thirty years ago, by Williamson (1965: 11).

20
# **IV. Methodology**

The macroeconomic impacts of the financial crisis are transmitted to households through the following: (a) financial markets via reduced access to credit, eroding savings and asset values; (b) labor markets via reduced employment, wages and remittances; (c) product markets via lower growth and production, and relative prices changes; and (d) government and non-governmental services such as public and private education, health and social protection services.<sup>6</sup> For Latvia, the dominant short-run crisis impact was expected to come from labor markets through reduced wages and employment. To measure the distributional impact of the financial crisis, aggregate shocks to employment and GDP must be linked to individual households, which can be done by combining aggregate information with household survey data.

Our base simulation assumed that workers were employed in the formal sector, or the informal (grey) sector. The expectation was that the formal sector would cut _both_ the size of the workforce and wage rates, but the informal sector would cut _only_ wage rates.<sup>7</sup> Our simulations used projected employment reductions, but wage reductions were computed such that the total sector GDP growth rates were as predicted above. An implicit assumption was that wage growth rates correspond exactly to GDP growth rates.

Abundant literature exists on the characteristics of workers most likely to be laid off when a sector contracts. In this note, we considered four layoff models. First, we postulated the determinants of employment, based on a range of observable worker-specific characteristics such as gender, education level, age (a proxy for experience), location of residence, and so forth. Then, for each worker we computed a propensity score  the likelihood that an individual with those observable characteristics would be employed. Next, the model assumed that the first workers to be laid off would be those with the least probability of being employed. The second and third models of layoffs were based on worker age; older workers might be selected for layoffs because they command higher wages, perhaps due to acquired rights or a specific set of productive characteristics. Younger workers may be selected for layoffs because there are fewer regulatory

> 6  See World Bank (2011) for a discussion of the transmission channels through which macroeconomic crises are passed to households.

> 7  Alternate scenarios are also tested. For example, in one scenario the formal sector only lays off workers, and the informal sector reduces wages. In another scenario, the we do not make a distinction between formal and informal sectors and hence, all workers can experience layoffs or wage reductions.

6
Table S.22: Impacts on children’s educational achievement - English: complementarity <u>(OLS)</u>

|Dependent variable:|||English: GPA|z-scores|||
|---|---|---|---|---|---|---|
|_X_:|No. of||School-|level ratio|of||
||students<br>in each<br>school-<br>grade|no. of<br>teachers<br>to<br>no. of<br>students|no. of<br>blackboards<br>to<br>no. of<br>students|no. of<br>benches<br>to<br>no. of<br>students|no. of<br>fans<br>to<br>no. of<br>students|no. of<br>toilets<br>to<br>no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|-0.150|-0.652***|-0.153|-0.316|-0.078|0.053|
||(0.160)|(0.203)|(0.281)|(0.241)|(0.067)|(0.080)|
|One product|0.144|-0.355|0.039|0.194|-0.127*|-0.140|
||(0.161)|(0.244)|(0.169)|(0.231)|(0.067)|(0.120)|
|All products _× X_|0.002<br>(0.002)|24.763***<br>(7.792)|5.024<br>(12.761)|0.593<br>(0.491)|5.228<br>(3.273)|-28.966<br>(18.708)|
|One product _× X_|-0.005*|9.281|-9.090|-0.748|-1.410|1.732|
||(0.003)|(10.820)|(9.168)|(0.512)|(8.282)|(21.644)|
|No. of students|-0.003|-|-|-|-|-|
|in each school-grade|(0.006)||||||
|R-squared|0.257|0.258|0.254|0.257|0.254|0.255|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.325*|0.249|-0.088|-0.329|-0.006|-0.040|
||(0.186)|(0.303)|(0.201)|(0.252)|(0.096)|(0.158)|
|One product|-0.348|0.587*|0.274|-0.129|0.063|-0.171|
||(0.257)|(0.330)|(0.328)|(0.361)|(0.093)|(0.148)|
|All products _× X_|0.006**|-11.904|2.726|0.671|-6.847|0.686|
||(0.003)|(12.544)|(10.811)|(0.533)|(4.111)|(28.945)|
|One product _× X_|0.008*|-22.696*|-12.091|0.390|-5.021|56.656**|
||(0.004)|(12.311)|(15.837)|(0.765)|(7.026)|(26.429)|
|No. of students|0.001|-|-|-|-|-|
|in each school-grade|(0.003)||||||
|R-squared|0.104|0.102|0.100|0.100|0.100|0.104|
|No. of obs.|845|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

67
Italy exports a great deal of intermediate services embodied in goods (machinery, vehicles, chemicals and petrochemicals, etc), as reflected in the earlier discussion on Table 3.

Table 4: Italy: value added composition of the economy, 2007

||value<br>added<br>total<br>economy|gross exports<br>share of<br>total value|value added<br>in exports:<br>forward<br>linkages|value added<br>in exports:<br>backward<br>linkages|
|---|---|---|---|---|
||A|B|C|D|
|primary sectors|0.03|0.01|0.04|0.02|
|processed foods, beverages|0.03|0.05|0.03|0.05|
|textiles and clothing|0.02|0.07|0.04|0.07|
|chemicals, petrochemicals|0.03|0.14|0.08|0.11|
|motor vehicles|0.01|0.07|0.02|0.06|
|other machinery|0.07|0.28|0.15|0.28|
|other manufacturing|0.10|0.21|0.20|0.22|
|utilities, construction|0.10|0.01|0.05|0.01|
|commercial services|0.42|0.15|0.36|0.18|
|public services|0.21|0.01|0.02|0.01|
|total|1.00|1.00|1.00|1.00|

_source: own calculations, using GTAP8 data._ While Italy is similar to other high income economies, in terms of service intensity, it is an outlier in terms of service sector productivity. We make this claim on the basis of estimates of absolute and relative productivity levels, using data from the EU-KLEMS database and following the methodology developed by Inklaar,Timmer and van Ark (2008).<sup>31</sup> On this basis, US multi-factor productivity (MFP) in market services (i.e. non-public) in 2007 was 145% of 1995 levels. At the same time, in 2007 Italy’s productivity was 84.8 percent of the US level for 1995. Italy, along with a number of other Eastern and Southern European states (and Japan) lag relatively far behind Northern Europe and North America in terms of service sector productivity.

As a follow-up to our discussed in Section 3 regarding emerging evidence that the cross-country pattern of productivity in services is linked to regulatory regimes, in Table 5 we have organized the data from Figure 1, related to productivity and regulation indexes, transformed for regression analysis. As such, all 31This involves first constructing detailed output (value-added) and input (labour and capital) price indexes, and then making productivity comparisons on the basis of Tornqvist indexes. The resulting data provide cross-country benchmarking of absolute productivity for selected years, implicitly linked by relative productivity growth rates for the intermediate years. See Francois, Manchin, and Norberg (2012) for further discussion. The data used as the basis for our calculations are the EU KLEMS database (November 2009 release) and the GGDC Productivity Level database (Inklaar et al. 2008).

29
- 22 -

There are three empirical remarks on the _Z_ ’s. First, the BEEPS database provides other measurements of public utility services in the water supply and telecommunications sectors. It is technically possible to incorporate those variables in our model. However, it has been found that the quality of water and telecommunications services would weakly affect firm production (Iimi, 2009); thus, the current paper adopts the electricity-related variables for _Z_ .

Second, in Equation (1), _Z_ ’s are specified as the composite cost function rather than the simple translog cost function (e.g., Kwoka, 2002). This aims at accommodating zero-quality values, i.e., no interruption of service delivery. In such a case, the number of days with service interruptions is zero. It follows, as a logical consequence, that the duration required to restore the service is zero hours. In our sample, a considerable portion of the observations have zero values for _Z k_ . A popular approach to this problem may be to replace zeros with a small positive value. However, this may cause severe bias in the estimates. Weninger (2003) shows that the Composite approach and Zero-output translog cost function have relatively low bias and the small standard deviation of the estimates. The small value and generalized translog cost function methods are largely biased.<sup>23</sup> In the current context, the composite approach has several advantages relative to other alternatives. It is expected to be less biased, as mentioned. It also preserves the linear homogeneity property in input prices, even after incorporating the quadratic form for quality measures.<sup>24</sup> Moreover, the Composite approach is computationally tractable and relatively easy to achieve convergence in the maximum likelihood estimator.<sup>25</sup>

> 23 With our data, it is also found that the small value translog cost estimator tends to sensitive to the choice of a small value.

> 24 The absence of the linear homogeneity is considered the principle limitation of this approach when it is applied to a multi-product cost function (Baumol _et al_ ., 1982; Kwoka, 2002). Fortunately, this is not the case in the current framework.

> 25 The current paper partly relies on the stochastic-frontier model, which is a maximum likelihood estimator.
and Demirguc-Kunt (2011), applying the recent advances made in the risk pricing literature<sup>6</sup> to an international sample of publicly traded banks in 63 countries. Finally, the cross-country nature of our dataset allows us to examine the impact of the institutional and regulatory environment on systemic stability as well as the relationship between competition and correlated risk taking behavior of banks, which are of particular interest for policy.

The rest of the paper is organized as follows. Section 2 describes the construction of the sample and variables. Section 3 presents the empirical results and discusses the implications. Section 4 concludes.

# **2. Data and Empirical Methodology**

# **2.1. Sample**

In this section we describe the data sources used in this paper. We obtain bank level financial information from Bankscope. We use stock market information from Compustat Global for international banks and stock market information from CRSP for U.S. banks. The Bankscope database reports detailed balance sheet and income statement information for both public and private banks and covers over 90% of the total banking assets in a given country. The Compustat Global database provides daily stock price information for both active and delisted companies accounting for 98% of the global stock market capitalization. CRSP is the standard source for stock price information of U.S. companies. Our final sample consists of 1,872 unique publicly traded banks in 63 countries from 1997 to 2009. Sample size varies across regression specifications because not all variables are available for all bank year observations.

# **2.2. Systemic Default Risk Measure**

We use the Merton (1974) contingent claim framework to measure bank default risk. 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-to-default measure has been shown to be good predictor of defaults outperforming accounting-based models (Campbell,

> 6 See Adrian and Brunnermeier (2009), Huang, Zhou, and Zhu (2009), Chan-Lau and Gravelle (2005), Avesani, Pascual, and Li (2006), and Elsinger, Lehar and Summer (2005).

5
high-saving countries identified the behavior of the real exchange rate as a key determinant of the saving rate?

The structure of the paper is as follows. In the next section we examine the empirical evidence on the correlation between the real exchange rate and saving in the international experience, to determine whether this correlation has arisen only in special cases or is a broader feature of that experience. Section 3 explores the effects on the saving rate of an exchange rate policy designed to sustain a depreciated value of the real exchange rate in the context of a standard intertemporal openeconomy model. Section 4 provides a brief review of the empirical literature on the determinants of cross-country differences in saving rates, as well as of that explaining saving behavior in the high savinghigh growth countries, to establish what -- i f any -- role has been accorded to the behavior of the real exchange rate in this literature. The final section summarizes the implications of our findings for the capital accumulation channel.

# **2. Savings and the Real Exchange Rate: International Evidence**

Aside from the episodic evidence mentioned earlier, is there more systematic evidence linking real exchange rates and saving rates? To determine whether the association between the real exchange rate and saving rate is a characteristic of the international experience, in this section we examine the correlation between the saving rate and the real exchange rate in a broad cross section of countries. The real exchange rate, defined as the relative price of traded goods in terms of nontraded goods, is measured by the (log) GDP deflator from the Penn World Tables, which captures each country<sup>’</sup> s national price level in dollar terms (relative to the U.S.). If the law of one price holds, the dollar price of traded goods will be equalized across countries, but the dollar price of nontraded goods will be lower where nontraded goods are cheaper -- i.e., where the real exchange rate is more depreciated. Hence the dollar price level, which is the weighted average of the dollar prices of traded and nontraded goods, should be lower where the real exchange rate is more depreciated. In turn, the saving rate is measured as the gross national saving rate.<sup>3</sup>

> 3 Gross national saving is defined as the difference between gross national disposable in-come (GNDI for short, equal to GNP plus net unrequited transfers from abroad) and total consumption expenditure. The saving rate therefore is the ratio of gross national saving to GNDI. See Schmidt-Hebbel and Servén (2000) for an overview of this and other measures of aggregate saving.

5
obtained from the United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems. This police data set consists of an unbalanced panel covering years between 2003 and 2008 for both developing and developed economies. Due to the unbalanced nature of the data set, only about 39 countries match between the ES firm level data and the UN country level police data. Accounting for issues such as missing questions and incorrect data, the sample size reduces to a cross-section of 27 developing economies.

Details of countries in the main sample and their respective survey years can be found in the first column of table A3 in the appendix. Data sources and descriptions of the variables can be found in table A1, with summary statistics in table A2.

# _2.1 Dependent Variable_

The main dependent variable utilized is annual losses due to crime as a percentage of annual sales. This variable is derived from the survey question: “In fiscal year [insert fiscal year], what are the estimated losses as a result of theft, robbery, vandalism or arson that occurred on the establishment’s premises calculated as a percent of annual sales?” In some instances the total value of losses is reported, and in that case the share of losses due to crime over total sales is manually calculated. Crime losses as a percentage of sales capture the intensity of crime. We make no distinction between a firm that has experienced no crime and a firm that has experienced crime but incurred no losses. Both firms get a zero value for the dependent variable. This adjustment may not affect our estimations much since we retain the same results when we examine the impact of per capita economic growth on the incidence of crime (see next paragraph). The crime losses variable averages 0.72% in the sample with a standard deviation of 8