rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2 provides preliminary information on the scope of the financial sector crisis in the seven countries by providing information on the peak level of non-performing loans in the financial system. Since the level of non-performing loans is a reflection of the performance of the real sector, this number can also be used as a rough proxy for the extent of corporate distress. Spain and the US were the only countries in the sample, where the extent of nonperforming loans in the system remained limited, i. e. below ten percent as a share of gross loans. In the Philippines and Finland, official NPLs reached substantial proportion accounting for over or close to 20 percent of financial system assets.'<sup>7</sup> In Ghana, more than half of banking system loans were non-performing. In the Mexican case, it should be noted that official numbers recorded the level of NPLs at around 11 percent of banking system loans. However, non-performing assets transferred to Fobaproa amounted to 23 percent of financial system loans or 17 percent of financial system assets at end 1996.

**Figure 2 Magnitude of Crisis and Resolution Costs**

<!-- Start of picture text --> (in percent of GDP) 1<br>Finland . ..--.<br>Ghana<br>Mexico<br>Philippines<br>Spain<br>Sw eden<br>USA I,<br>0 25 50 75 100 125 150<br>l Resoluton cost * Totalclairrs 0 Nonefonrningloams<br><!-- End of picture text --> _Source:_ Caprio and Klingebiel (1999) and IMF, International Financial Statistics.

# **1. Cross Country Experience with Rapid Asset Disposition Agencies'**<sup>**8**</sup>

Table 1 provides an overview of the main characteristics of the four country examples with rapid asset disposition vehicles. Two of the agencies-the Mexican and the Spanish AMCs-were housed in an existing public agency, the Deposit Guarantee Agency. Contrary to this, the Philippine and the US agencies were set up as stand alone agencies with a limited life

> 17 As the accounting conventions differ among the countries in the sample, these figures should be treated with caution, Among the countries, Spain, the US, Finland and Sweden have stricter classification regulation compared to Ghana, Mexico and the Philippines.

is It should be noted that the analysis of the Mexican rapid asset disposition agency Fobaproa reflects available information until the end of 1998.

10
them were used by firms located outside SEZs and other development areas in ‘open’ cities, and the remaining 17% were used by firms located anywhere in the rest of China (Figure 2). In sum, on the eve of WTO accession, duty exemptions benefited virtually all firms engaged in export processing in China, not just those located in SEZs and other development areas. This ability for enterprises outside the zones to engage in processing trade was critical for ensuring that this trade grew rapidly, rather than just benefitting a limited number of firms and workers in export processing enclaves.

**Table 1. Average tariff rates in China (percent)**

||All|products|Primary|products|Manufactur|ing products|
|---|---|---|---|---|---|---|
||Simple|Weighted|Simple|Weighted|Simple|Weighted|
|1992|42.9|40.6|36.2|22.3|44.9|46.5|
|1997|17.6|18.2|17.9|20.0|17.5|17.8|
|2001|16.6|12.0|21.6|17.7|16.2|13.0|
|After accession|9.8|6.8|13.2|3.6|9.5|6.9|

Source: Ianchovichina and Martin (2001). Authors’ calculations for tariff lines with imports in 1999 from COMTRADE; CDS Consulting for protection data for 1999-2001; and after accession data from China’s WTO final offer.

# _2.3 Reducing tariff and non-tariff barriers_

In addition to liberalizing processing trade, China made substantial progress in reducing both its own non-tariff and tariff barriers. The number of products subject to quotas and licenses fell from 1,247 tariff lines in 1992 to 261 in 1999 (Ianchovichina and Martin, 2001) and China’s average tariffs declined from above 40 percent in the early 1990s to substantially below 20 percent by 2001 (Table 1). Much of the liberalization during the 1990s was influenced by China’s desire to prepare for the trade regime required for WTO accession and to demonstrate its commitment to an open economy. The large protection cuts during this period are unlikely to have occurred without the prospect of accession to the WTO.

8
# **Table A.11. Comparison of utility expenditure variables in the BIHSs 2010/11 to 2018/19**

|**Item name**|**2010/11**<br>**BIHS**|**2015**<br>**BIHS**|**2018/19**<br>**BIHS**|
|---|---|---|---|
|**_Module P1 (monthly recall)_**||||
|1. Firewood|+|+|+|
|2. Cow dung/cakes/bhushi/wood-powder|+|+|+|
|3. Jute stick|+|+|+|
|4. Kerosene|+|+|+|
|5. Agriculture by-products used for fuel: paddy, hag, pressed<br>sugarcane, and dried com plants|+|+|+|
|6. Gas (natural, bio-gas) or liquified petroleum gas_(_LPG)|+|+|+|
|7. Electricity|+|+||
|8. Pit coal, char coal, wood coal|+|+|+|
|9. Other fuels and light (e.g., matches and candles)|+|+|+|
|10. Electricity (national grid)|||+|
|11. Electricity (generator)||+|+|
|12.Electricity (solar)||+|+|

Notes: The inconsistent variables are marked in red. The sign “+” indicates the variable is included in the specific survey round. For item 8, very few households (8 households in 2015 BIHS and 1 household in 2018/19 BIHS) reported use of “Pit coal, char coal, wood coal”.

57
- 28 -

to confirm the generally perceived life span of 15 to 20 years during which a shipping asset could perform economically. When developments in the seatrade markets after 1980 suggested that fundamental changes in the structure of demand for ocean transport and volume growth rates were taking place, many shipowners became more cautious. They started to order fewer newbuildings and relied on their existing shipping assets. As the market contracted further, they resorted to tonnage lay-up, rather than sending their ships for scrapping. Even when the seatrade markets revived in 1986 they kept most of their existing tonnage, addressing growing demand more through re-commissioning previously laid-up vessels than through the acquisition of newbuildings.

49. As a result of these practices, the average age of the world merchant fleet increased continuously. The proportion of vessels older than 15 years grew from 27.5 percent in the mid-1980s to 56.8 percent in 1992. The average vessel age in 1992 was 16.6 years and is expected to increase by five percent per annum, if no corrective measures are taken. The tanker segment is most overaged with almost 65 percent of all vessels older than 15 years, followed by general cargo ships with about 60 percent. The dry bulk fleet is on average younger--with only 42 percent of the fleet older than 15 years-, but in some of its specialized categories, such as OBO carriers, more than 70 percent of **all** units have been trading for more than 15 years. Only the fleet of dedicated container vessels is young, with 72.5 percent less than 15 years of age. See Figure 6 below for details.

<!-- Start of picture text --> The World Merchant Fleet<br>Age Distribution in mid 1992<br>dwt share of respective Fleet Segment<br>80% - . - bOS<br>40% - - 40%<br>30% -- 30%<br>20% -- 20%<br>10% 10%<br>0% 0%<br>less than 5 5-9 10 - 14 15 - 19 20 -25 more than 25<br>number of years<br>z Oil Tankers l=] Dry Bulk Carriers M Container Vessels<br>Gen. Cargo Ships Averge IFntlri Merhant Fleat<br>Source: Lloyd's Register of Shiping Figure 6<br><!-- End of picture text -->
improve the perception of security of tenure and the real property market financing and to attract foreign investors, including a change of the urban land concept provided by the current Constitution.

# **Conclusion**

In the international context, the econometric analysis in this article shows that, although ownership has recently received less attention in global policy debates, according to our econometric analysis of productivity in 55 countries, half of which are transition economies, private ownership is an equally or more important determinant of productivity than other components of the investment climate. We thus consider privatization, bankruptcy, competition and restructuring polices to be complementary to investment climate reform and therefore policy makers need to be advised to consider them in tandem. Unfortunately, the investment climate literature has focused almost exclusively on regulatory burden, access to finance, corruption and taxation. The latter may be due to the fact that surveys usually point out to managers’ complaints about “what hurts,” rather than “what to do”.

Evidence based on surveys of firms in PICS-BEEPS countries and in Serbia itself shows that private ownership, FDI and export-orientation are all strongly associated with firm productivity, investment and growth. The surveys also show that firms with the characteristics associated with strong firm performance are the firms that complain the most about investment climate constraints on the operation of their businesses. Reducing investment climate constraints will benefit primarily the firms that can generate growth. Although we find that the new private firms are better performers, their scope for growth, and the possibility of entry by new entrepreneurs, are both severely limited by the investment climate and crowded-out by the unrestructured enterprises.

In view of these findings, it is important that Serbia accelerate its enterprise reform program. The development of the private sector still has a long way to go. The share of the private sector in GDP, circa 50 percent, compares unfavorably to other countries in the area<sup>25</sup> . FDI per capita in Serbia was also lower than in the neighboring countries<sup>26</sup> . Almost all FDI in Serbia in the first period 2001-2003 was related to privatization. Although such brownfield investments potentially could play a positive role in encouraging greenfield investors, this requires that current investors find the investment climate acceptable and they are treated fairly. While experience so far is limited, the treatment of investment at times has been uneven.<sup>27</sup> Closing the productivity gap between Serbia and its near neighbors will require further privatization and new business development as well as improvements in the investment climate for firms.

17
**Table 1: The distribution of various charges in the data set**

||Mean|10thpercentile|90thpercentile|
|---|---|---|---|
|monthlyconsumption|166.3 kWh|24.7kWh|374.5kWh|
|monthly consumption<br>per capita|39.7 kWh|4.4 kWh|87.6 kWh|
|energycharges|₹1729|₹181|₹4086|
|fixed charges|₹348|₹180|₹480|
|electricityduty|₹138|₹20|₹310|
|tariff subsidy|₹-15.1|₹ -66.3|₹0|

Notes: Total number of observations in the sample are 7,615. The distribution was weighted by sampling probabilities.

**Table 2: Comparing the consumption distribution of sampled households to billing data set**

|Percentiles|Admin billing data<br>(all HH)|Admin billing data<br>(surveyed HH)|
|---|---|---|
|1%|1|1|
|5%|15|14|
|10%|30|28|
|25%|62|58|
|50%|115|119|
|75%|240|263|
|90%|452|436|
|95%|645|562|
|99%|1283|762|
|Mean Value|208|189|

Notes: This table compares the distribution of consumption for all households in the two district of Rajasthan in the administrative data to the consumption distribution obtained from the survey using sampling probabilities. The sample period for both data sets is restricted to January and February 2017.

**Table 3: Socioeconomic profile of the households**

|Variable|Proportion of households|
|---|---|
|Proportion ofgeneral caste|29%|
|Pukka Wall|97.1%|
|Pukka Roof|99%|
|Pukka Floor|44.4%|
|Tapwater connection|63.4%|
|TopIncome Source: Farming|15.8%|
|TopIncome Source: Livestock|3.4%|
|TopIncome Source: Own Business|19.5%|
|TopIncome Source: Casual Labour|23.2%|
|TopIncome Source: Salaried Work|36.2%|
|TopIncome Source: Remittances|2.8%|
|Livingin a tented house|5.5%|
|Ownershipof Below PovertyLine Card|18.2%|

46
# **Figure 4**

# **Growth change under alternative infrastructure development scenarios**

- A. Catching up: halving the infrastructure level gap with other regions

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>West Africa LICs East Africa LICs South Africa LICs Central Africa LICs SSA UMCs<br>Infrastructure Stock Infrastructure Quality<br>Change in growth per capita (%)<br><!-- End of picture text -->

- B. Keeping up: matching the rate of infrastructure growth of other regions

<!-- Start of picture text -->
1.8<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>West Africa LICs East Africa LICs South Africa LICs Central Africa LICs SSA UMCs<br>Infrastructure Quantity Infrastructure Quality<br>Change in growth per capita (%)<br><!-- End of picture text -->

44
tion. Low-skill migrants include all of those with a level of schooling up to and including an upper-secondary education.<sup>4</sup> As shown in Table 1, the OECD data allow us to characterize the education level, origin and destination of about 59.3 million migrants in 2000 and 42.5 million migrants in 1990. About 18.5 million of the 20.9 million high-skill migrants in the OECD countries are concentrated in only six destination countries: the U.S. (10.3 million), Canada (2.7 million), Australia (1.6 million), Israel (1.5 million), the United Kingdom (1.2 million) and Germany (1.2 million).

# **2.2 Migration Data for Non-OECD Countries**

We further supplement our expanded data collection of our 34 OECD destinations with 66 non-OECD countries in 2000 and 27 countries in 1990, adhering to the same methodological principles and definitions as in DLM. The data sources for these destination countries, together with the total number of migrants and the total number of highly skilled migrants for both 1990 and 2000 are presented in Table A.1. In 13 cases, data are obtained directly from the relevant destination countries’ national statistical offices. In 23 cases, data are taken from IPUMS-International or the United Nations’ Economic Commission for Latin America and the Caribbean (ECLAC) databases, two of the largest archives of publicly available census samples. They are based on samples of at least five percent of the whole population.

> 4Note that DLM disaggregated low-skill migrants into two categories, those with upper-secondary education and those with less (including low-secondary, primary or no schooling). In this paper, we aggregate these two categories for estimation purposes.

11
- 19 -

resources are spent in order to raise tax revenue, and a larger administration is a substitute for higher tax rates. (Assuming, of course, that we are on the increasing side of the Laffer curve).

Assume that tax rates are optimally chosen. Then the marginal cost of public funds is at least 1.2 (that is for each dollar of gov'ernmental outlay, one has to add .1 for administrative cost and .1 for efficiency loss. In addition, one has to add compliance costs). If taxes are not optimally chosen, which is probably the case, then the marginal cost of public funds can be lower or higher than 1.2. However, since efficiency losses are hidden, while administrative costs are under the scrutiny of the budgeting process of the goverpment, we conjecture that it is reasonable to assume that the marginal excess burden is higher than the marginal administrative costs. In this case 1.2 is a lower bound of the social cost of public funds.

Table 1: The Expected Return Per day

|Type of Tax Return|Return Per Day 1/|
|---|---|
|Self-employed,<br>single-sided<br>bookkeeping (SEO)|158|
|Self-employed,<br>double-entry<br>bookkeeping (SED)|416|
|Corporations (COR)|64|

1/ The return is calculated in New Israeli Shekels. The exchange rate in the middle of the period of investigation is US$1 = NISO.71.
2010 accounted for 44 percent of the increase in consolidated government expenditure at the national level.

The aggregate stock of states’ debt is less than half the stock of central debt (figure 4). Despite higher levels of fiscal deficit in 2009 and 2010, the ratio of states’ debt to GDP did not rise, thanks to the high nominal growth of GDP; deceleration in real output growth was more than compensated for by acceleration in inflation, which crossed 10 percent in 2010. The debt relief mechanism prescribed by the 12th Finance Commission, conditional on adherence to rule-based fiscal consolidation, helped states contain the ratio of their aggregate debt to national GDP well below the target of 30.8 percent recommended by the 12th Finance Commission for 2009. This ratio peaked in 2005 at 26.8 percent. It has declined to 23.5 percent in 2010.

# **Figure 4 Outstanding Liabilities of Central and State Governments in India relative to National Gross Domestic Product, 2000–10**

<!-- Start of picture text --> Percent of national GDP<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>State Governments Central Government<br><!-- End of picture text --> Source: Indian Public Finance Statistics, 2011-12; Government of India, Ministry of Finance.

Based on information available for 18 states, the Reserve Bank of India estimates the outstanding guarantees of state governments, the main form of contingent liabilities, at 2.8 per cent of GDP by end-March 2010, only marginally higher than the 2.7 per cent at endMarch 2009 (Reserve Bank of India 2012). The 12th Finance Commission had recommended that all states impose a limit on these contingent liabilities through their Fiscal Responsibility 32
country) with data on at least 2 of 70 4-digit ISIC industries. The average number of industries per country is 54. The criteria used to select the sample and the effect of this criteria on the results are discussed in appendix B.

Two different measures of volatility, which capture two different aspects of fluctuations, were built for each industry in every country: the standard deviation of real value added growth ( _overall volatility),_ and the minimum growth rate of real value added observed during the period ( _depth of the cycle)_ .

The growth rate of real value added, on which the two measures above are based, was computed using data on nominal value added from UNIDO and the following three deflators: (i) the Producer Price Index from the International Financial Statistics; (ii) the Index of Industrial Production, also from IFS; (iii) the ratio of nominal to real manufacturing value added from the World Development Indicators. Following Rajan and Zingales (1998), (i) was used as the main deflator, and (ii) was used for high inflation countries.<sup>22</sup><sup>_,_23</sup>

# **5.2 Financial Development**

Three measures of financial development are used throughout this paper. The main measure is private credit as a fraction of GDP, which includes the credit by banks and other financial institutions but excludes the credit allocated by the Central Bank. The reason to exclude the latter is that the credit allocated by the Central Bank is likely to be determined by political rather than economic considerations. The level of private credit captures the development of financial intermediaries and it is closely related to the mechanism emphasized in this paper because of the important role played by financial intermediaries in the provision of liquidity. Data for this measure was obtained from Beck et al. (2001a).

The two other measures of financial development are the quality of accounting standards and the stock market capitalization. These measures are less related to the particular mechanism of this paper, but are included in the analysis for two reasons: (i) to explore the different channels by which financial development affects sectorial volatility, especially the role of information and market structure (banks versus markets); (ii) to check the robustness of the results to the use of measures that capture different dimensions of financial development.

The quality of accounting standards, obtained from La Porta et al. (1998), captures the quality of the information available to outside investors, and it is probably correlated with the characteristics of the contracting environment in which firms develop. This measure should not only be relevant for market based systems, as it is reasonable to expect that the monitoring costs incurred by a bank should be decreasing in the quality of the information typically produced by firms.<sup>24</sup>

> 22Within the final sample of countries and period of analysis, the only case of hiper-inflation corresponds to Peru.

> 23 Deflators (ii) or (iii) were also used for countries lacking information on (i).

18
Figure 2, however, we see that these are exactly the groups of women for whom participation rates declined.

**Figure** **<u>3: Log real wages for women age 25-54 by education level, urban India</u>**

<!-- Start of picture text --> 7.0<br>6.5 Graduate +<br>6.0<br>Secondary<br>5.5<br>Middle<br>5.0<br>Primary<br>4.5<br>4.0 Literate < primary<br>3.5 Illiterate<br>3.0<br>1987 1993 1999 2004 2009 2011<br><!-- End of picture text --> Note: Wages are average total weekly earnings for casual and regular employees. Earnings are spatially deflated and in 1987-88 Rupees, based on the Labour Bureau Consumer Price Index for Industrial Workers and Deaton (2003). _Source_ : NSS Employment and Unemployment Survey Wages may have little impact on FLFP, despite rising returns to higher education, if employment growth in the activities appropriate for educated women is limited. As shown in Figure 4, the distribution of female workers across industries changes substantially with education. The distribution is shown separately for women below secondary education and those with secondary or higher education. Women with lower education work mainly in agriculture, manufacturing, and domestic services (included in “other services”). Access to white-collar services sector jobs is mostly confined to women with at least secondary education, but declined steeply over time. By and large, this pattern is consistent with the U-shape in education suggesting that only with at least secondary education, women gain access to jobs that are not subject to social stigma. It could also account for the declining participation rates of highly educated women whose supply increased 13