rafmacalaba/datause-encoder-data huggingface dataset:
towards small- and micro-enterprises that would not have received credit otherwise, but that the nonperforming loan rate at many of these institutions is very high (Hanson 2002). Most of the research suggests that large concentrations of state bank ownership have some unfavorable economic consequences, such as reduced overall access to financing, increased likelihood of financial crises, or diminished financial system development (e.g., Barth, Caprio, and Levine 1999, Clarke and Cull 2002, La Porta, Lopez-de-Silanes, and Shleifer 2002). These unfavorable consequences may occur in part because of weak governance, lack of aggressive collection procedures that encourages a poor credit culture, or because some of the resources may be channeled for political purposes (e.g., Sapienza forthcoming). See Appendix I for examples of state-owned banks in developing nations.

# **2.2. Foreign-owned banks in developing nations**

Foreign bank participation has increased in emerging markets around the world, in part because of the removal of barriers to direct foreign ownership of financial firms, bank restructurings, financial crises, and stateowned bank privatizations. These banks may be able to overcome any disadvantages to SME lending due to size, distance, and differing home market conditions described above because they often step into markets where the supply of credit to SME from domestic banks may be lacking for various reasons. Whereas the foreign-owned banks may be disadvantaged in relationship lending to informationally opaque SMEs because of difficulties in using soft information, these banks may have advantages in transactions lending to some SMEs because of better access to information technologies for collecting and assessing hard information. For example, Citibank's "Citibusiness" initiative provides SME financing in 18 countries using industry-level data to make credit decisions. Credit is targeted to SMEs in the industrial segments of the nation identified as having growth potential, in some cases without historical credit information on the specific firms.<sup>7</sup> One study of foreign bank participation in over 80 countries finds that foreign banks have higher profitability in emerging markets, whereas the opposite is true in developed countries (Claessens, Demirgiic-Kunt, and Huizinga 2001). Thus, relative inefficiency of foreign-owned banks in developed nations reported above may be reversed in developing nations in which the competition from domestically-owned banks may not be as strong. Most other studies of lending by foreign-owned banks are also consistent with these banks performing relatively well. One study finds evidence that foreign banks in Argentina and Mexico may provide credit smoothing and

> 7 Another example is GE Capital's Budapest Bank, a former state-owned bank that was privatized. GE Capital offers new technology and U.S.-based training for Budapest Bank SME'loan officers, consistent with exploitation of advantages in transactions lending.

8
the number of firms. Establishment Size and Age variables are calculated similar to the formal manufacturing census described above.

# _B.1. Finance and Labor Market Institutions in India_

To take into account institutional differences that may affect firm life cycles, we focus on the level of financial development and the stringency of labor regulations across different states of India. We first describe India’s banking sector and the measures we construct followed by the stringency of labor regulation in India and the associated measures.

India has a rich and mature banking history with the first banks being established by the British East India Company toward the end of the 18<sup>th</sup> century. India’s largest bank today, the State Bank of India, originated in 1806 as the Bank of Calcutta. Following India’s independence, in <mark>1949, the Banking Regulation Act vested the Reserve Bank of India (RBI), India’s Central Bank, with extensive powers for supervision of banking in India. I</mark> n two waves of nationalization in 1969 and 1980, the Indian government nationalized the major private banks, running them as profit-making public sector undertakings that were allowed to compete and operate as commercial banks.<sup>17</sup> This increased the public sector banks’ share of deposits to 92% (Hanson, 2001). The government also experimented with a social banking program during this period to improve access to banking services for the poor, leading to a huge expansion in the number of bank branches (Burgess and Pande, 2005).

In response to a fiscal and balance of payments crisis in 1991, India went through a largescale financial liberalization. Following the recommendations of the Narasimhan Committee Report that suggested reforms in the Indian banking sector, the government allowed for the entry

> 17 In 1969, 14 banks with deposits over Rs 50 crores were nationalized. In 1980, 6 private sector banks were nationalized. A few of the private sector banks were not nationalized because of their small size and regional focus.

16
consistent sample across the two dependent variables, UCB is thus not included in the sample before its privatization. In robustness tests for the margin regressions, however, we test the sensitivity of our findings to this omission. The average interest spread in our sample is 18.1%, while the average wide margin (margin) is 10.9% (9.7%). The variation of spreads and margins across banks is about three times as large as the variation over time.

Both loan-weighted interest margins and spreads have shown an upward trend over the past years (Figure 4), although there are sub-periods with no or even a negative trend.<sup>14</sup> Regressions of both interest margins and interest rate spreads – averaged across banks and weighted by the market share of each bank in the loan market - on a time trend yield significantly positive coefficients. This positive trend is confirmed even when we control for inflation. We also notice that the margin defined as net interest revenue follows closely the wide margin that include fees and commissions, although the gap has somewhat opened over the past few quarters, consistent with claims that banks have substituted interest rate charges with non-interest charges.

We use several bank-specific variables computed from balance sheet and income statements to explain variation in interest margins and spreads. Table 2 provides summary statistics and correlations for the employed variables. _Overhead_ costs are the costs for salaries, motor vehicles, fixed assets etc (depreciation excluded) over total assets and average 7.7% across banks and over time. Overhead costs for the sector have shown an increasing trend over the period 1999 to 2005. Banks’ recent investments in physical infrastructure such as increased outreach efforts and very high costs for power and telecommunication might explain the recent increase in operating costs; we will explore this issue in more detail below. Return on Assets ( _ROA)_ is defined as profits over total assets and averages 1.9% across banks and over time.

> 14 While we do not weight the observations in the regressions, we show the weighted interest spreads and margins in Figure 4 as to show the average spread and margin faced by customers.

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**Table 4.4. Balance of payments in 2012 and by simulation for 2013-2030 (Percent of nominal GDP)**

|||**2012**|**base**|**min+**|**inf+**|**hd+**|**geff+**|
|---|---|---|---|---|---|---|---|
|**Outflows**|**Crop imports**|0.5|0.5|0.4|0.5|0.5|0.5|
||**Food imports**|9.0|8.4|7.3|8.6|8.5|8.6|
||**Petroleum imports**|15.1|14.3|12.9|14.5|14.2|14.7|
||**Other industry imports**|43.5|29.9|28.0|31.1|30.1|30.7|
||**Non-competive imports**|20.0|5.4|7.6|5.3|5.4|5.4|
||**Service imports**|33.8|33.9|33.1|34.3|34.4|34.7|
||**Factor income mining**|1.9|4.7|7.0|4.6|4.7|4.7|
||**Other factor incomes**|6.7|5.5|4.9|5.5|5.6|5.6|
||**Interest**|0.1|0.2|0.2|0.2|0.2|0.2|
||**Total**|130.7|102.8|101.4|104.6|103.5|105.0|
|**Inflows**|**Trees exports**|8.4|7.1|5.3|7.2|7.2|7.1|
||**Mining exports**|7.7|28.6|44.5|27.6|28.4|28.3|
||**Other industry exports**|1.0|0.8|0.6|0.9|0.8|0.8|
||**Service exports**|20.8|29.2|20.3|32.9|30.3|32.1|
||**Private transfers (net)**|13.8|12.7|10.4|12.2|12.6|12.5|
||**Government transfers**|21.5|12.6|10.7|12.3|12.5|12.5|
||**UNMIL transfers**|25.4|2.0|2.0|2.0|2.0|2.0|
||**Government borrowing**|0.8|1.5|1.3|1.5|1.5|1.5|
||**FDI -- mining**|30.2|5.8|4.2|5.8|5.8|5.8|
||**FDI -- other sectors**|1.3|2.5|2.0|2.3|2.4|2.4|
||**Total**|130.7|102.8|101.4|104.6|103.5|105.0|

**Note:**

**Columns other than 2012 show averages for 2013-2030 by simulation.**

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Table 3: Decomposition Exercise: Firm Heterogeneity and Aggregation Bias

|DVAR of Total Exports|Number of ...rms in the sample|
|---|---|
|(1) Census<br>0.479 (0.021)|3419|
|(2) Original Sample<br>0.478 (0.023)|2623|
|(3) KWW (2012) Estimates<br>0.408|N/A|
|(4) Large Firms Only<br>0.453 (0.034)|123|

Notes: With the exception of (3), all numbers are calculated by the authors based on di¤erent samples.

(1) refers to the 2004 Census of Manufacturing Plants; (2) restricts the sample in (1) to the original survey dataset.

(3) is the IO table-based estimate from KWW (2012); (4) restricts the sample in (2) to only ...rms with total exports larger than 300 million RMB. Bootstrapped standard errors are reported in parentheses.

47
survey in 2001. This leaves us with 3614 observations of urban resident adults between the ages of 21 and 60 who were employed in November 2001.

By focusing on urban permanent residents, the survey excludes those with temporary residence permits or with no registration status, a group consisting primarily of rural migrants. China maintains a household registration ( _hukou_ ) system which discriminates against non-local residents in access to many public services and benefits as well as employment opportunities. Migrants tend to be much younger than urban residents, are likely to have attended schools of poorer quality in rural areas, are more likely to be selfemployed, and are a self-selected group who earn lower wages than urban residents even after controlling for observable characteristics (Meng and Zhang, 2002), either due to discrimination or other unobserved differences (e.g., school quality, ability, etc.). For all of these reasons, we focus only on the returns to schooling of urban permanent residents even though the CULS did include a separate survey of migrants in each city. According to the 2000 population census, registered urban households comprised 76 percent of those living in the five sample cities.

The CULS survey instrument asks detailed questions about workers’ educational histories. It records the year in which each level of schooling began (primary, lower secondary, upper secondary, vocational high school, college, vocational college, three-year college, graduate school) and the years of schooling completed at each level, including information on repeated grades. From this detailed information, we calculate multiple education disruption variables discussed in section 3 below.

The questionnaire also included questions about the schools attended at each level, which reflect both school quality and early assessments of individual ability that would be otherwise unobserved. These questions include location (city, county, town, village); the 7
draws on a diverse set of household consumption or expenditure surveys from 91 countries as of 2010, covering a range of topics including transport by region, country, and area.

The data standardization methodology of this database is of great significance to the harmonization of transport expenditure data from LSMS and other household surveys after 2010. The first step is annualizing each household’s expenditure data for each commodity, according to the “recall period”, on which the household is asked to report by recalling their expenditures. For example, if the transport expenditure data is collected for the past week, then it would be multiplied by 52, while monthly data would be multiplied by 12. The second step is mapping the transport products and services in each survey to a standard classification of products according to International Comparison Program (ICP) classification, equivalent to the Individual Consumption According to Purpose (COICOP) as shown in Annex 8. Finally, all expenditure data would be converted to a common reference year, 2010, taking account of inflation and real growth in household consumption, then converted from local currencies into U.S. dollars based on the 2005 round of ICP.

Based on the most up-to-date harmonized consumption data provided by the Data Analytics and Tools team in the Development Data Group, covering 105 countries between 2000 and 2018 (in which 73 countries have data after 2010), we can analyze the share of household transport expenditure by rural or urban setting and expenditure quintile<sup>7</sup> at the global level and different income levels. The details of the transport subcategories are recorded in the COICOP classification. The country coverage of each subcategory varies as listed in Table 8.

**Table 8. Country coverage of the transport subcategories**

(105 countries are included in the standardized consumption database)

|**Category**|**Number of countries**|
|---|---|
|Motor cars|94|
|Motorcycles|89|
|Bicycles|97|
|Animal-drawn vehicles|29|
|Fuels and lubricants forpersonal transport equipment|105|
|Maintenance and repair ofpersonal transport equipment|103|
|Other services in respect ofpersonal transport equipment|87|
|Passenger transport byrailway|78|
|Passenger transport byroad|105|
|Passenger transport byair|100|
|Passenger transport bysea and inland waterway|80|
|Combinedpassenger transport|52|
|Otherpurchase transport services|85|

> 7 Quintiles were generated using <mark>weighted per capita expenditure, which was calculated</mark> by taking household total expenditure divided by household size.

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|Basic Education Reform Project|Cynthia Hobbs|
|---|---|
|Basic Education Reform Project|Cynthia Hobbs|
|Basic Education Reform Project|Cynthia Hobbs|
|Basic Education Reform Project|Cynthia Hobbs|
|Basic Education Reform Project|Cynthia Hobbs|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|SICHUAN URBAN ENVIRONMENT PROJECT|Hiroaki Suzuki|
|Transition Assistance Credit|Wei Ding|
|Structural Adjustment Loan(SAL)|Marcelo Giugale|
|Structural Adjustment Loan(SAL)|Marcelo Giugale|
|Coal Pilot Project|Heinz Hendriks|
|Coal Pilot Project|Heinz Hendriks|
|Coal Pilot Project|Heinz Hendriks|
|Coal Pilot Project|Heinz Hendriks|
|Coal Pilot Project|Heinz Hendriks|
|Coal Pilot Project|Heinz Hendriks|
|Strucutral Adjustment Loan(SAL)|Mansour Farsad|
|Strucutral Adjustment Loan(SAL)|Mansour Farsad|
|Economic Reform & Development Loan(02)Project|Christian E. Petersen|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Cartagena Water Supply,Sewerage and Environmental Manageme|nGregJ. Browder|
|Second Structural Adjustment Loan|Mark S. Davis|
|Second Structural Adjustment Loan|Mark S. Davis|
|Second Structural Adjustment Loan|Mark S. Davis|
|Technical Assistance for Economic Reform Project|Kouassi Soman|
|Technical Assistance for Economic Reform Project|Kouassi Soman|
|Technical Assistance for Economic Reform Project|Kouassi Soman|
3 The effectiveness of AL programs must in the end be investigated empirically. There are two parts to<sup>any such investigation. First, the</sup> performance of recipient countries must<sup>be compared to that of non-recipient</sup> countries: this requires a methodology<sup>for correcting for the external</sup> environment that I will explain below.<sup>Second, if a significant difference in</sup> behavior is found it could be due<sup>to the policy reform or to the additional</sup> funding linked to the AL program; to differentiate between these it is useful to investigate whether policy in recipient countries<sup>diitered significantly</sup> from that of non-recipient countries.1 In this paper I outline a methodology for assessing the success<sup>of World Bank adjustment lending and</sup> implement it for a 75-country panel<sup>of data for the period 1976-1986. The</sup> evidence suggests that there is a significant<sup>association between</sup> participation in a World Bank adjustment<sup>lending program and more rapid real</sup> economic growth, an improved current<sup>account as a percentage of gross national</sup> product (GNF), and higher inflation.<sup>The first two are positive features of a</sup> structural adjustment package, but<sup>the third calls into question the long-run</sup> sustainability of the efforts. This association does<sup>not appear to be "luck"</sup> or simply a response to added financing;<sup>similar statistical analyses indicate</sup> that participation in a SAL program<sup>is significantly</sup> correlated with both real depreciation of the country's exchange<sup>rate, reduction in current government</sup> expenditure as a share of GNP and financial<sup>deepening in dcmestic capital</sup> markets.

1 AL programs represent a small proportion<sup>of the total international</sup> indebtedness of most recipients. However, AL disbursements represent a larger percentage of total international disbursements: for all recipient countries<sup>AL</sup> flows represented over five percent of<sup>total international flows on average</sup> during the period 1986-1988, while for Sub-Saharan Africa the comparable<sup>figure</sup> is over 12 percent. These and other<sup>figures on the magnitude of AL financing are</sup> found in World Bank (1990).
The business environment has not helped Brazil weather stronger competition in global markets. The logistics infrastructure, which is widely recognized as inefficient and costly to Brazilian exports, as well as many other factors, such as a very complex and costly tax system, have taken a toll on Brazilian firms. This diagnostic reinforces the importance of resuming the agenda of microeconomic reforms, increasing the investment-to-GDP ratio, and advancing toward a better-skilled human capital base. Promoting and rewarding productivity gains in a competitive economy, including the service sector, are the only options to accelerate growth and overcome possible middle-income growth traps (Agenor and Canuto 2012).

# **References**

- Agenor, P.-R., and O. Canuto. 2012. “Middle-Income Traps.” World Bank Policy Research Working Paper 6210, Washington, DC.

- Bernard, A. B., and J. B. Jensen. 1999. “Exporting and Productivity.” National Bureau of Economic Research Working Paper 7135.

- Besedes, T., and T. Prusa. 2006. “Ins, Outs and the Duration of Trade.” _Canadian Journal of Economics_ . 39 (1): 266–95.

- Bonelli, R., and A. C. Pinheiro. 2012. “Competitividade e Desempenho Industrial: Mais Que só Câmbio.” XXIV Fórum Nacional Rumo ao Brasil Desenvolvido.

- Cebeci, T., A. M. Fernandes, C. Freund, and M. D. Pierola. 2012. “Exporter Dynamics Database.” Banco Mundial, 22 de maio, preliminar.

- Clerides, S., S. Lach, and J. Tybout. 1998. “Is Learning by Exporting Important? Micro-Dynamic Evidence from Colombia, Mexico and Morocco.” _Quarterly Journal of Economics_ 113 (3): 903–47.

- Corden, W., and P. Neary. 1982. “Booming Sector and De-Industrialization in a Small Open Economy.” _Economic Journal_ 92 (December): 825–48.

- Davis, S., and J. Haltiwanger. 1992. “Gross Job Creation, Gross Job Destruction and Employment Reallocation.” _Quarterly Journal of Economics_ 107(3): 819–63.

- Gaulier, G., D. Taglioni, and S. Zignago. 2012. “Export Performance in the Wake of the Global Crisis: Evidence from a New Database.” Mimeo.

- Hausman, R., J. Hwang, and D. Rodrik. 2007. “What You Export Matters.” _Journal of Economic Growth_ 12: 1–25.

- Helpman, E., M. Melitz, and Y. Rubinstein. 2008. “Estimating Trade Flows: Trading Partners and Trading Volumes.” _Quarterly Journal of Economics_ CXXIII: 2.

- Krugman, P. 1996. “Making Sense of the Competitiveness Debate.” _Oxford Review of Economic Policy_ 12: 17–25.

- Malouche, M., J. Reis, and S. Waglé, 2012. “The Character of Export Growth in Developing Countries 1998-2008”. Mimeo.

- Melitz, M. J. 2003. “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity.” _Econometrica_ 71 (6): 1695–1725.

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