rafmacalaba/datause-encoder-data huggingface dataset:
13 Table 4: Analysis of Relative Irport Prices Paid by Madagascar Befcre anq Atter Adoption of Preshipment Inspection Requirements (Data correspond to reiative prices at deci.e imits)

|||Fran|ce|||Germar,<br>a<br>Ita|lyand<br>pa|_an|
|---|---|---|---|---|---|---|---|---|
|DeciLe|Cmroarator<br>1979-82|Industrial<br>1984-88|Comparator::<br>1979-82|eveioDp<br>1984-88|<br>Comparator:I<br>1979-82|<br> <br>ndustnai<br>1984-88|<br><br>Con atoror<br>1979-82|<br>Deve o inq<br>1984-88|
|01|-29.1|-43.7|-38.3|50.7|-39.5|-48.9|-40.8|45.4|
|02|-9.6|-24.4|-20.0|-39.8|_-20.5_|-30.0|-18.6|-26.6|
|03|6.3|-4.3|-3.3|-21.8|*5.2|-17.2|-3.3|-8.7|
|04|20.4|10.7|8.2|*6.0|11.8|3.1|12.0|8.1|
|05|36.9|26.1|19.2|9.7|31.0|21.5|28.6|22.3|
|06|51.6|42.0|33.7|25.3|59.3|40.5|51.8|41.7|
|D7|78.4|65.8|52.1|44.2|92.1|79.6|79.7|68.3|
|08|116.9|112.3|77.9|69.8|140.8|126.5|112.5|_112.5_|
|09|151.7|151.3|137.3|120.7|152.2|151.8|151.6|151.6|

Note: The deciLe Limits show the proportion of Madagascar's relative import prices that tie above or beLow certain values. For instance, during the 1979-82 period exactly half (50 percent) of Madagascar's import prices from France were 36.9 percent higher than those charged industriaL countries while half were lower. Similarly, 30 percent of Madagascar's prices were 78.4 percent higher.

for Germany, Italy and Japan's prices. The shifts that occurred were in the lower deciles (i.e., the entries corresponding to D1 through 03 ranges). **1**<sup>**5**</sup> The table also shows that some reduction cccurred in the median relative import prices--entries that correspond to the D5 values--due tc an increase in the number of products with an apparent price discount. However, tests on the mean prices (previously cited) indicate the reduction was not significant.

> **15A** Chi-square test indicates that the 1979-82 distribution of relative import prices was significantly different from the 1984-88 distribution at the **9S** percent confidence level. However, as Table 4 shows this is due to shifts in the middle and lower decile ranges and not to reduction in extreme **adverse price relatives,** i.e., those above the D9 limit.
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Tables 2 and Figures 1A and 1B show the changing correlation between household and firm credit and GDP per capita during different time periods. While there is variation across countries, there is substantial correlation between income level, trade openness, financial openness and credit growth relative to GDP, especially for firms. This correlation changes over time. For example, the correlation coefficient between NFC credit and GDP per capita during the period before the credit boom was 0.51 and had jumped to 0.69 during the period 2004-2013. At the same time, the correlation with trade and financial openness increased substantially for NFC credit, but not for credit to households. Clearly, other factors besides openness became more important in later years in terms of determining household access to credit;<sup>4, 5</sup> for example, a housing market boom or other factors increasing credit, such as a period of “excess” global liquidity.

**Table 2: Pearson’s Correlation Coefficients by Time Period, 1964-2013**

||**1964-2013**<br>|||
|---|---|---|---|
||Total Credit|HH Credit|NFC Credit|
|HH Credit|0.797*|||
|NFC Credit|0.916*|0.496*||
|GDP per Capita|0.740*|0.673*|0.629*|
|Trade Openness|0.352*|0.138*|0.416*|
|Financial Openness|0.461*|0.081*|0.628*|
||**1964-2003**|||
||Total Credit|HH Credit|NFC Credit|
|HH Credit|0.807*|||
|NFC Credit|0.907*|0.519*||
|GDP per Capita|0.673*|0.711*|0.506*|
|Trade Openness|0.211*|0.076*|0.260*|
|Financial Openness|0.249*|0.064|0.341*|
||**2004-2013**|||
||Total Credit|HH Credit|NFC Credit|
|HH Credit|0.759*|||
|NFC Credit|0.920*|0.445*||
|GDP per Capita|0.777*|0.631*|0.690*|
|Trade Openness|0.365*|0.063|0.465*|
|Financial Openness|0.537*|0.025|0.731*|

Total credit, HH credit and NFC credit are ratios to GDP. GDP per capita are in constant 2010 US$. * indicates significance at 5% level.

4 The data on household credit shares were compared with that in a recent paper by Beck et al (2012) for the period 1994-2005. For the countries that overlap, the Beck et al figures are usually larger. However, the averages for the period 1994-2013 (used in this paper) are generally substantially larger for several countries than their averages, reflecting the fact that these ratios rose over time.

5 Note that only four countries (Italy, Japan, Republic of Korea, and United States) have data beginning in 1964. The correlations and regressions (not shown) are also done for the period 1994-2013.

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# **1. Introduction**

The impact of bank competition on financial fragility has always been a subject of active academic and policy debate. However public policy interest in this topic has intensified after the global financial crisis, with both academics and policymakers questioning to what extent the “dark side” of competition and the resulting financial innovations in search of higher margins were responsible for the crisis. While greater competition in the banking sector has no doubt led to greater innovation and efficiency, there is still no academic consensus on whether this competition has also led to greater fragility, with conflicted theoretical predictions and mixed empirical results.

In parallel, the financial crisis also led to a re-examination of risk assessment practices and regulation of the financial system, with a renewed interest in systemic fragility and macroprudential regulation. This requires a focus not on the risk of individual financial institutions, but on an individual bank’s contribution to the risk of the financial system as a whole. Hence, there is a growing consensus that from a regulatory perspective of ensuring systemic stability, the correlation in the risk taking behavior of banks is much more relevant than the absolute level of risk taking in any individual institution.

In this paper we address both sets of issues by re-examining the empirical relationship between competition and systemic stability. Unlike the extant literature which has focused on stand-alone bank risk, our focus in this paper is on systemic stability. Hence instead of looking at the absolute level of risk in individual banks, we examine the _correlation_ in the risk taking behavior of banks, measured as the total variation of changes in default risk of a given bank explained by changes in default risk of all other banks in a given country.

We follow Anginer and Demirguc-Kunt (2011) and use Merton’s (1974) contingent claim pricing framework to measure bank default risk and its contribution to systemic risk. Using a sample of 1,872 publicly traded banks in 63 countries over the period 1997 to 2009, we investigate the impact of bank concentration and competition, as measured by the Lerner index of bank market power. Our results suggest a positive relationship between competition and systemic stability, consistent with the view that greater competition encourages banks to take on more diversified risks, making the banking system less fragile to shocks. We also examine the impact of the larger institutional and regulatory environment on this relationship. Correlated risk 2
16 for 95 percent and, Uzbekistan-9 sectors accounting for 91 percent of Its exports."

# V. Trade Barrier Escalation and **FSU** Commodity Exports

The previous section showed that a relatively h!gh share of ISU exports to the OECD consists of unprocessed or semi-fabricated commodities, i.e., items in which the NISs could potentially experience important benefits from further processing.'<sup>2</sup> However, studies have argued that trade barrier escalation in major international markets is an important constraint to further processing in commodity exporting countries (see Balassa, 1968, for an early statement of this point, or later studies by Helleiner and Welwood, 1978, and Yeats, 1979). Trade barrier escalation is characterized by zero, or very low, tariffs (and nontariff barriers) on unprocessed commodities but with these restrictions increasing with the degree, of further processing, thus creating a bias against trade in processed commodities.

The concept of effective protection provides useful insights into the effects of escalating trade barriers over commodity processing chains (i.e., a processing chain identifies commodities at different stages of production with each successive stage representing a higher level of fabrication) in that it measures the influence of protection on value added in a production process (specifically, the effective rate shows the percentage reduction in value added foreign exporters of processed commodities must " Because of their heavy reliance on ferrous metals and cotton products, the central Asian Republics (CAR) -- pear t. bespecially affete by the EC discriminatori tariffs. This obsezvation must be qualified, however, _since FSU_ trade data combine raw cotton (which is largely free of duties) with cotton textiles and clothing. We have been unable to determine the shares of these differen **t** ypes of gocds in the reported total.

1TeU potential benefits may be of .fficient importance that some economists have argued that 'natural resource based industrialization strategies' can provide a significant stimulus to overall industrialization and growth. Among the benefits cited are: avoidance of the purported deterioration in the terms of trade for primary commodities; increased employment opportunities associated with the production and export of manufactures; achievement of important linkages with other sectors of the economy; improvement of human capital through 'leamning effects'; and more stable prices of processed as opposed to primary commodities. See Roemer (1979) for a discussion of resource-based industrialization strategies.
|**Table 5: Total Employment an**|**d Total Em**<br>Number <br>of<br>Firms|**ployment Impac**<br> Administrative<br>Report of<br>Employment|**t in Winni**<br>Round1<br>in<br>Tota|**ng Firms**<br>Round2<br>Winning Fi<br>l Employm|Round 3<br>rms<br>ent|Round1<br>E<br>Treatm|Round2<br>mployme<br>ent Effect|Round 3<br>nt<br>on Total|
|---|---|---|---|---|---|---|---|---|
|Randomly selected winners|729|11940|4588|7183|6858|1051|3411|3579|
|New Firms|451|7487|2289|4209|4099|645|2711|2359|
|Existing Firms|278|4453|2299|2974|2759|406|701|1220|
|National and Zonal winners|475|11780|4439|6762|5870|1444|3366|3448|
|New Firms|118|3783|744|1712|1273|320|1317|827|
|Existing Firms|357|8009|3695|5050|4597|1125|2049|2620|
|All winners|1204|23781|9027|13945|12728|2495|6777|7027|
|Notes:<br>Treatment Effect are experim<br>Administrative Report was em|ental estim<br>ployment|ates for random<br>reported to the|ly selecte<br>program a|d winners,<br>t around t|and PSM e<br>he time of|stimates f<br>the Round|or other w<br>2 survey.|orkers.|

42
- 40 -

potential technical inefficiency is predicted to exceed 0.8 percent of total costs. Mining is considered the most efficient industry in our sample.

Table 13. Average predicted technical inefficiency by country and by sector (% of total costs)

||Obs|Mean St|d.Dev.|Min|Max|
|---|---|---|---|---|---|
|All sample|990|0.53|0.94|0.000|10.01|
|By country:||||||
|Albania|118|0.38|0.45|0.001|2.63|
|Bosnia & Herzegovina|79|0.49|0.81|0.004|4.55|
|Bulgaria|141|0.93|1.45|0.001|6.67|
|Croatia|172|0.26|0.44|0.000|2.71|
|Macedonia|89|0.57|0.76|0.002|4.35|
|Romania|269|0.58|1.05|0.003|10.01|
|Serbia|122|0.48|0.84|0.002|5.01|
|By industry:||||||
|Mining|16|0.21|0.32|0.004|1.21|
|Construction|94|0.27|0.36|0.002|2.09|
|Manufacturing|403|0.40|0.66|0.001|5.56|
|Transport|72|0.37|0.74|0.003|5.56|
|Trade|226|0.56|0.81|0.000|5.56|
|Real estate|65|0.87|1.52|0.006|6.67|
|Hotels & restaurant|67|0.83|1.14|0.003|4.55|
|Other services|47|1.52|2.00|0.016|10.01|

Figure 18. Average predicted technical inefficiency by country <!-- Start of picture text -->
1.0 0.93<br>0.9<br>0.8<br>0.7<br>0.6 0.53 0.57 0.58<br>0.49 0.48<br>0.5<br>0.38<br>0.4<br>0.3 0.26<br>0.2<br>0.1<br>0.0<br>(% of total cost)<br>Average predicted technical inefficiency<br>All Albania Bulgaria Croatia Romania Serbia<br>Bosnia &  Herzegovina Macedonia<br><!-- End of picture text -->

By size of firms, not surprisingly, small and medium enterprises are found relatively inefficient (Table 14). The average firm’s technical inefficiency is estimated at 1.66 percent of total costs, if its annual sales are less than US$199,000. But for large firms whose outturn exceeds US$2.5 million, technical inefficiency may account for only 0.02 percent of all operating costs. By labor intensity, enterprises relying more on labor input tend to be less efficient. The evidence seems to be consistent with a common view that labor issues, such as
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of which roughly a half of the coal and natural gas consumers and a third of the petroleum product consumers are OECD countries with relatively insignirlcant subsidies<sup>4</sup> . We define subsidies on fossil fuels as the difference between domesJic fossil fuel prices a-id their (private) opportunity cost evaluated at end-user prices. When fuels are traded internationally border prices serve as opportunity cost, which is the case for petroleum products for all sample countries. Opportunity cost at end-user level would be border prices plus a mark-up for distribution. U.S. pre-tax end-user prices of petroleum products by sector are used as proxies for opportunity cost at end-user level, although unit distribution costs may lo some extent vary across countries. Natural gas and coal are traded less frequently than oil/petroleum products and natural gas markets are primarily regional in character. Border prices plus distribution costs are used if these fuels are imported or there exist export markets as for the formner Soviet Union in the case of natural gas and to a lesser extent coal<sup>5</sup> . In the case of China, an approximation to the opportunity cost of coal is the unit price for the proportion of coal traded in the free domestic market. In the remainder of the paper opportunity cost is referred to as world pricA for sake of convenience. Official exchange rates reported in IMF's International Financial Statistics have been used to convert domestic prices into dollars. Implicit subsidies stemming from overvalued exchange rates are ignored and subsidies are thus underestimated relative to an 4The Environment Directorate (OECD) is currently undertaking a study on producer and consumer subsidies and other "supports" to the energy sector. There are significant producer coal subsidies in Germany, but end-user prices of coal in Germany are well above border pric-s due to import restrictions on coal.

5It is recognized that border prices plus distribution costs as defined here are not likely to be equivalent to the opportunity cost of energy, but are used in the estimations of subsidies because data on marginal cost of energy and domestic distribution cost are rather scarce. Given that the United States have the lowest pre-tax energy prices of the OECD countries, subsidies as calculated are likely understated rather than overstated. _5_
**Table 1: Summary Statistics for the Explanatory Variables**

|Variable|Mean|Standard<br>deviation|
|---|---|---|
|Initial conditions|||
|Landlocked-ness [0−1]|0.13|0.34|
|Logarithm of country’s land area (hectares)|19.3|1.67|
|Terrain ruggedness index|119.59|96.20|
|Logarithm of GDP per capita at appraisal (constant 2010 US$)|7.25|0.66|
|Project design and characteristics|||
|Quality of theory of change [1−5]|1.87|0.95|
|Private sector consultation [0−1]|0.53|0.50|
|Private sector involvement [0−1]|0.38|0.49|
|Degree of connectivity increase [1−3]|1.85|0.93|
|Investment at border [0−1]|0.11|0.31|
|Geographic scope [1−4]|2.61|0.82|
|Complementary policies and institutions|||
|Openness at approval [(Imports + Exports)/GDP]|0.50|0.22|
|Any development policy operation in past 5 years|0.49|0.51|
|Any development policy operation in past 5 years: industry-trade-services|0.40|0.50|
|Any development policy operation in past 5 years: public administration|0.43|0.50|
|Any development policy operation in past 5 years: transport|0.13|0.34|

# **3.3. Initial Conditions and Complementary Policies and Institutions**

The initial conditions in the country are captured through the country area, country gross domestic product (GDP) per capita<sup>10</sup> at time of loan appraisal, terrain ruggedness, and whether the country is landlocked. All initial conditions, except terrain ruggedness, are measured at the country level. Terrain ruggedness— measured at the level of our geographic unit—was extracted from the data set created by Nunn and Puga (2012). Corridors located in areas with greater terrain ruggedness may find it more difficult to generate and spread WEBs along the corridor than corridors where the surrounding area is flatter. And richer countries may not see their local economic activity boosted by as large a margin as poorer countries that still have far to go in income convergence.

The complementary policies and institutions are captured using measures of trade openness<sup>11</sup> of the country at the time of project appraisal and indicators on whether there were policy and institutional reforms in the five years preceding the signing of the loan agreement for the project. We use World Bank Development Policy Operations (DPOs) as a proxy for policy and institutional reforms in the country. Development policy operations support a program of policy and institutional actions that promote growth and enhance the well-being and increase the incomes of poor people. For example, some DPOs aim to improve the investment climate, diversify the economy, and create employment (World Bank 2004). Clearly, not all policy and institutional reform programs are supported by the World Bank, so the use of DPOs is an

> 10 From the World Development Indicators, World Bank.

> 11 Measured using imports, exports, and GDP data from the World Development Indicators, World Bank.

9
year after year. Over the entire 14-year period, it has increased by about 15 percent. The table also shows the recalculated top decile share of world income. It too increases steadily in every benchmark year, going from 51.4 percent of global income in 1988 to 57.5 percent of global income in 2002. With the “old” PPP data, the top decile of individuals controlled about one-half of total income (not shown in Table). Thus, this indicator of inequality has increased too.

Table 3 shows the “new” global decile shares for the benchmark year 2002. We have already seen that the top decile receives more than 57 percent of global income. The top 5% of the population gets almost one-third of global income. It is also noticeable that not even the eighth decile receives its population share, that is, the average income among the people belonging to the 8<sup>th</sup> decile is still below the world mean.

Figure 2. Global Gini 1988-2002 calculated with new and old PPPs <!-- Start of picture text --> With new PPPs<br>With old PPPs<br>1985 1990 1995 2000 2005<br>year<br>72<br>70<br>68<br>66<br>64<br>62<br><!-- End of picture text --> Note: The one-standard deviation confidence interval around the new Ginis displayed with dsashed line.

12