rafmacalaba/datause-encoder-data huggingface dataset:
Overall, as seen in Figure 2, the employment impact of the pandemic and its gendered implications were not uniform across job types. As previously discussed, the bulk of job losses for both male and female workers was concentrated in the informal sector. What is of particular interest is the gendered impact of the pandemic among salaried workers in the private sector. In fact, while male employment increased, albeit marginally, during the first year of the pandemic, the opposite trend was observed among women.<sup>11</sup>

**_Figure 2: Changes in employment 2019-20/2020-21, by gender and type of job_**

<!-- Start of picture text --> 200000<br>100000<br>0<br>Informal Salaried private Salaried public Employer<br>-100000 sector sector<br>-200000<br>-300000<br>-400000<br>-500000<br>Men Women<br># of jobs<br><!-- End of picture text --> In order to better investigate whether the differential gendered impact of the pandemic is due to differences in pre-pandemic characteristics of employment, we take advantage of the rotating panel component of the LFS and restrict the sample to individuals who were interviewed at least one time pre and post March 2020 and who were employed before the pandemic.<sup>12</sup> In particular, we estimate the following model: where the dependent variable is the probability of being employed in 2020-21 for individual _i_ conditional on him/her holding a job _j_ before the start of the pandemic. The main variable of interest is the interaction term between a dummy indicating female workers and a categorical variable indicating the job type before the pandemic, which can be either informal (omitted category), salaried worker in the private sector or salaried worker in the public sector. Controls include a set of individual characteristics which includes age, age squared, education, marital status urban residence and province dummies. Moreover, ii XX in order to capture the fact that some sectors might have been disproportionately affected by the crisis, sector dummies for the job held in 2019-20 were included in the model. As in previous analysis, the

> 11 Salaried employment in the private sector increased by 133,000 units among male workers, while it declined by 242,000 units among female workers.

> 12 Due to the small sample size for female workers, individuals who were working as employers before the pandemic were excluded from the analysis.

9
issue fraudulent invoices (Keen & Smith, 2007). What particularly draws the ire of tax authorities is the fact that VAT fraud constitutes not only nonpayment of taxes due, but also theft of taxes paid by law-abiding taxpayers.

Despite the grave nature of VAT refund fraud, however, tax authorities in both the developed and the developing countries must often deal with the prospect of having to refund excess credits to taxpayers by the statutory deadline<sup>3</sup> before the legitimacy of the refund claims can be ascertained. The tax authorities of developing countries generally work to verify VAT refund claims before payment in accordance with their domestic laws and regulations, while those of developed countries employ riskassessment mechanisms to administer VAT refund claims without verification (Harrison & Krelove, 2005, p. 41). The discrepancy observed in VAT refund administration between the developed and the developing countries is striking but understandable. It is simply unrealistic for the developing countries to abandon their refund verification endeavors and replace them with unfamiliar risk-assessment approaches given the prevalence of refund fraud and the level of distrust that exists between taxpayers and tax authorities suffering from perceived corruption and weak governance.

Given the striking VAT refund practice gap between the developing and developed countries, Korea’s implementation of the electronic tax invoice (ETI) and ETIbacked early warning system (EWS) offers new technology-enhanced approaches and sheds new light on VAT enforcement and compliance. This paper reviews potential avenues to establishing more seamless VAT refund systems utilizing information and communication technology (ICT) and lessons from Korea’s experience with combating VAT fraud and improving tax compliance with ETI. Korea’s drive for a seamless VAT refund system remains a work in progress, but its significant success to date with integrating ICT into the tax administration system should provide important policy lessons to developing countries that are looking to apply ICT to their tax administration in order to improve tax services and compliance.

# **2. Literature Review**

Tax compliance has been a contentious subject in economic studies for the past four decades since Allingham and Sandmo (1972) analyzed tax evasion as utilitymaximizing behavior by individual taxpayers. Since then, many researchers have delved into what influences tax compliance. Bărbuţă-Mişu (2011) classified factors that contribute to tax compliance into economic factors including tax rates, tax audit, income level, and potential penalties for noncompliance, and noneconomic factors including attitude toward taxes, the personal, social, and national norms, and the perceived fairness of the tax system.

> 3 According to an IMF survey, 33 out of 36 countries that were surveyed responded that they have a statutory deadline, generally within30 days, for payment of VAT refunds (Harrison & Krelove, 2005).

2
|Non-Agriculture<br>Investment<br>M4<br>M6<br>(7)<br>(8)<br>_−_0_._277_∗∗∗_<br>_−_0_._265_∗∗∗_<br>(0_._063)<br>(0_._046)<br>_−_0_._456_∗∗∗_<br>_−_0_._462_∗∗∗_<br>(0_._088)<br>(0_._088)<br>_−_0_._613_∗∗∗_<br>_−_0_._623_∗∗∗_<br>(0_._096)<br>(0_._074)|460<br>636<br>0.104<br>0.139<br> agriculture, and investment in<br>) refer to two sets of Mekong<br> and Vinh Long, and M6 adds<br>espectively.|
|---|---|
|uts<br>ulture<br>tment<br>M6<br>(6)<br>0_._097_∗∗_<br>(0_._048)<br>0_._213_∗∗_<br>(0_._104)<br>0_._385_∗∗_<br>(0_._182)|583<br>0.073<br>estment in <br>kong 6 (M6<br>Dong Thap <br> 1% level, r|
|ping on Inp<br>s<br>Agric<br>Inves<br>M4<br>(5)<br>0_._105_∗_<br>(0_._065)<br>0_._219_∗_<br>(0_._132)<br>0_._400_∗_<br>(0_._224)|399<br>0.100<br> of-farm, inv<br>M4) and Me<br>, Can Tho, <br>0%, 5%, and|
|Anti-Dum<br>g Province<br>Worked<br>arm<br>M6<br>(4)<br>_−_0_._018<br>(0_._023)<br>_−_0_._038<br>(0_._046)<br>_−_0_._065<br>(0_._072)|785<br>0.157<br>urs worked <br>Mekong 4 (<br>s An Giang<br>ifcant at 1|
|Impact of <br>Mekon<br>Hours <br>Of-F<br>M4<br>(3)<br>_−_0_._006<br>(0_._032)<br>_−_0_._014<br>(0_._060)<br>_−_0_._028<br>(0_._093)|560<br>0.175<br> catfsh, ho<br>es, etc.). <br> M4 include<br>, _∗∗∗_, sign|
|Average <br>fsh<br>ment<br>M6<br>(2)<br>_−_0_._248_∗∗∗_<br>(0_._045)<br>_−_0_._436_∗∗∗_<br>(0_._068)<br>_−_0_._595_∗∗∗_<br>(0_._076)|548<br>0.117<br>: investment in<br>ck, farm servic<br>sh production: <br>renthesis: _∗_, _∗∗_|
|Cat<br>Invest<br>M4<br>(1)<br>Low-Exposure<br>_−_0_._283_∗∗∗_<br>(0_._058)<br>Mean<br>_−_0_._464_∗∗∗_<br>(0_._080)<br>High-exposure<br>_−_0_._619_∗∗∗_<br>(0_._086)|Observations<br>411<br>R2 (within)<br>0.105<br>Note: Estimates of input choices<br>non-agriculture activities (livesto<br>provinces that specialize in catf<br>Soc Trang and Tien Giang.<br>Robust standard errors within pa|

46
**26**

The laws governing secured creditors will affect secured creditors only to the extent that the laws are enforced. Consequently, measures of the efficiency of the legal system in enforcing contracts are included from LLSV **(1998).** RULELAW is an assessment of the law and order tradition of the country that ranges from **10,** strong law and order tradition, to **1,** weak law and order tradition. This measure was constructed **by** International Country Risk Guide (ICRG) and is an average over the period **1982-1995.** Given the contractual nature of banking, higher values of the RULELAW are likely to positively influence banking development.

CONRISK is an assessment of the risk that a government will **-** and therefore can **-** modify a contract after it has been signed. CONRISK ranges from **10,** low risk of contract modification, to **1,** high risk of contract modification. Specifically, "modification" means either repudiation, postponement, or reducing the government's financial obligation. This measure was constructed **by** ICRG and is an average over the period **1982-1995.** Legal systems that effectively enforce contracts will tend to support banking activities.

**ENFORCE** equals the average of RULELAW and CONRISK. The empirical analyses focus on this aggregate index of the efficiency of the legal system in enforcing contracts, **ENFORCE,** and the aggregate index of creditor rights, CREDITOR.

Summary statistics on **ENFORCE** are given in Table **6.** As shown, there is substantial crosscountry variation in **ENFORCE,** where the maximum value is **9.99,** the minimum value is **_3.55,_** and the standard deviation is 2.2. The countries with very high values of enforcement, values of ENFORCE greater that **9,** are Australia, Austria, Belgium, Canada, Denmark, Finland, France, German, Japan, Netherlands, New Zealand, Norway, Sweden, and Switzerland. In contrast, countries where contract
- 24 -

(IMF) data tapes. The daily values of these two rates are realized in the financial markets and are not announced. They themselves respond to economic announcements as shown by Hardouvelis (1988). However, in this study, it is assumed that unexpected changes in the daily interest rate and exchange rate are exogeneous 1/ to the behavior of commodity prices. This is a reasonable assumption given the fact that these rates adjust very quickly to economic announcements. Hakkio and Pearce (1985) have showni that exchange rates adjust to economic announcements within 20 minutes, while Barnhart and PH have shown that commodity prices are somewhat sluggish in their reaction to economic news. Exchange rate and interest rate surprises have been calculated as the residuals from second order autoregressions of the daily series of these rates. 2/

# Business Cycle Data

41. The business cycle is measured as the spread between the actual natural log of industrial production and the trend natural log of industrial production. The trend was found by regressing the actual natural log of industrial production on a constant and a time index. Results of these regressions are shown in Figures 1 and 2 for the periods starting in January

- 1/ Empirical analysis of the impact of unexpected announcements of money stock, consumer, producer and industrial production indices, and unemployment rates on the residuals from autoregressions of daily exchange and interest rates did not indicate any statistically significant influence. Hence, this assumption is justified.

- 2/ This method for calculating surprises implicitly assumes that agents know in period t the underlying coefficients of their forecasting model in periods tel, t+2, .... However, this procedure is justified if the coefficients of the forecasting models have not changed significantly over time. This was the case.
Figure 6: Expected change in import-weighted tariffs on digital goods after implementation of the AfCFTA by 2030 (world trade) <!-- Start of picture text --> Cameroon<br>Central African Rep.<br>Nigeria<br>Senegal<br>Ghana<br>Mali<br>Gambia<br>Ivory Coast<br>Togo<br>Congo<br>Burkina Faso<br>Niger<br>Comoros<br>Benin<br>Ethiopia<br>Mauritania<br>Uganda<br>Kenya<br>Tanzania<br>Rwanda<br>Burundi<br>Africa average<br>Sierra Leone<br>Madagascar<br>Cape Verde<br>Mozambique<br>Egypt<br>Zambia<br>Angola<br>Malawi<br>Zimbabwe<br>Tunisia<br>Morocco<br>South Africa<br>Namibia<br>Botswana<br>Swaziland<br>Lesotho<br>Mauritius<br>Seychelles<br>0 5 10 15<br>Expected tariff by 2030 Current tariff<br><!-- End of picture text --> Notes: Tariffs by country are an import-weighted average by product. All exporting countries considered. Africa average is a simple average of these country-level tariffs. Authors’ calculations based on UNCTAD, and COMTRADE.

23
# **5. CONCLUSIONS**

This paper started with a simple question--what were the pathways out of poverty in Indonesia between 1993 and 2000? Two stylized pathways were put forward, improved productivity in agriculture and the growth of non-farm productivity; and two transitions to reach these paths were hypothesized, a sectoral transition from farm to nonfarm, and a geographical transition from rural to urban.

The results presented above suggest that there is no single or simple answer to this question. There are many factors which affect movements out of poverty. The aggregate evidence from the SUSENAS data (when “corrected” for changing administrative borders) suggests that employment growth in the rural non-farm sector has played an important role in reducing poverty. When carefully analyzed, these SUSENAS data offer useful insights into the net flows of workers across sectors and locations. But the use of cross-sectional data to understand poverty dynamics does not account for how individual, household and geographical characteristics may shape the transitions that occur.

The IFLS panel data set allows us to explore precisely these transitions between 1993 and 2000. Among the poorest individuals who were aged 15+ and working in 1993, getting out of agriculture appears to be an important factor in increasing incomes. Almost a fifth of these individuals left agriculture and in doing so substantially increased their incomes. This said, getting out of agriculture is not the only route out of poverty. Indeed over 80% of individuals from poor households working in rural agriculture in 1993 were still working in rural agriculture seven years later, but more than half of the households from which they came managed to exit from poverty. Thus boosting agricultural productivity is also an important pathway out of poverty.

Rural-urban migration, by contrast, would appear to have a relatively small role, although this period in Indonesia’s modern economic history, from 1993 to 2000, was a particularly difficult one for successful rural to urban migration. Only a handful of those working in rural agriculture in 1993 were able to migrate to urban areas. Most of those who migrated to non-farm jobs in urban areas were already doing non-agricultural jobs in rural areas and tended to be among the better off non-farm rural workers. Thus it may be that the rural non-farm sector acts as a stepping stone between rural agricultural work and more productive non-farm employment in urban areas.

44
- 42 -

References: Backus D. and J. Stephenson, "Inflation and Reputation.", A.E.R., 75(3) (June 1985), pp. 530-8.

Barro, R. and D. Gordon, "A Positive Theory of Monetary Policy in a Natural Rate Model.", J. of Pol. Econ., 91(4) (August 1983), pp. 589-610.

Barro, R. and D. Gordon, "Rules, Discretion and Reputation in a Model of Monetary Policy.", J. of Mon. Econ., 12(1) (July 1983), 101-121.

Giugale, M., "Real Quasi-Fiscal Deficit, Real Domestic Debt and Inflation under Alternative Macroeconomic Scenarios: A Quantitative Simulation for Argentina 1989-94.", mimeo, World Bank, Washington, D.C., October 1989.

Persson, M., T. Persson, and L. Svensson, "Time Consistency of Fiscal and Monetary Policy.", Econometrica, 55(6) (November 1987), pp. 1419-31.

Robinson, D. and P. Stella, "Amalgamating Central Bank and Fiscal Deficits.", in M. Blejer and K. Chu (eds.), "Measurement of Fiscal Impacts: Methodological Issues.", (IMF Occasional Paper 59, June 1988, Washington, D.C.), pp. 20-31.

Sargent, T. and N. Wallace, "Some Unpleasant Monetarist Arithmetic.", Federal Reserve Bank of Minneapolis Ouarterly Review, 1982.

Tanzi, V., M. Blejer and M. Teijeiro, "Effects of Inflation on the Measurement of Fiscal Deficits.", in M. Blejer and K., op. cit., pp. 4-19.
||`(1.74)`|`(2.89)**`|`(0.06)`|`(0.41)`|
|---|---|---|---|---|
|`judirate`|`-0.101`|`0.000`|`-0.050`|`0.111`|
||`(0.33)`|`(0.00)`|`(0.39)`|`(0.83)`|
|`centgov`|`0.074`|`0.193`|`0.055`|`0.006`|
||`(0.20)`|`(0.58)`|`(0.37)`|`(0.04)`|
|`parl`|`-0.401`|`0.368`|`0.024`|`0.105`|
||`(1.19)`|`(1.16)`|`(0.18)`|`(0.71)`|
|`cbank`|`0.096`|`-0.012`|`-0.013`|`0.131`|
||`(0.31)`|`(0.04)`|`(0.09)`|`(0.82)`|
|`police`|`-0.047`|`-0.285`|`-0.082`|`-0.123`|
||`(0.18)`|`(1.14)`|`(0.67)`|`(0.94)`|
|`govine`|`-0.096`|`-0.192`|`0.032`|`0.123`|
||`(0.40)`|`(0.85)`|`(0.28)`|`(1.04)`|
|`ginter`|`-0.111`|`0.051`|`0.038`|`0.159`|
||`(0.43)`|`(0.20)`|`(0.35)`|`(1.34)`|
|`corobs`|`0.258`|`-0.042`|`0.204`|`0.347`|
||`(0.88)`|`(0.15)`|`(1.33)`|`(2.16)* `|
|`corry`|`0.430`|`0.521`|`-0.022`|`0.321`|
||`(1.59)`|`(2.00)* `|`(0.14)`|`(2.05)* `|
|`corqb `|`-0.341`|`-0.393`|`-0.056`|`-0.003`|
||`(1.10)`|`(1.33)`|`(0.26)`|`(0.01)`|
|`infr`|`-0.347`|`-0.276`|`-0.044`|`-0.309`|
||`(1.13)`|`(0.96)`|`(0.27)`|`(1.79)`|
|`roads`|`0.243`|`0.420`|`0.103`|`0.064`|
||`(1.04)`|`(1.92)`|`(0.92)`|`(0.53)`|
|`post`|`0.239`|`0.215`|`0.295`|`-0.087`|
||`(0.77)`|`(0.74)`|`(1.36)`|`(0.36)`|
|`elec`|`-0.166`|`-0.175`|`-0.163`|`0.001`|
||`(0.59)`|`(0.66)`|`(0.90)`|`(0.00)`|
|`water`|`-0.175`|`0.041`|`-0.153`|`-0.215`|
||`(0.62)`|`(0.16)`|`(0.98)`|`(1.29)`|
|`crimest`|`0.322`|`-0.190`|`-0.129`|`-0.150`|
||`(0.98)`|`(0.61)`|`(0.84)`|`(0.93)`|
|`crimeorg`|`-0.694`|`-0.678`|`0.118`|`-0.017`|
||`(2.12)* `|`(2.19)* `|`(0.71)`|`(0.10)`|
|`sale1`|`1.836`|`1.529`|`0.489`|`0.407`|
||`(3.57)**`|`(3.69)**`|`(3.14)**`|`(2.44)* `|
|`sale2`|`2.725`|`1.465`|`1.316`|`0.849`|
||`(4.98)**`|`(3.13)**`|`(8.33)**`|`(4.94)**`|
|`owind`|`0.567`|`0.373`|`-0.074`|`-0.009`|
||`(1.66)`|`(1.19)`|`(0.52)`|`(0.06)`|
|`owfam`|`1.078`|`0.591`|`0.131`|`0.172`|
||`(2.75)**`|`(1.63)`|`(0.71)`|`(0.85)`|
|`owork`|`1.135`|`0.936`|`-0.360`|`-0.101`|
||`(2.64)**`|`(2.22)* `|`(1.79)`|`(0.47)`|
|`owsta`|`0.118`|`0.264`|`-0.550`|`-0.154`|
||`(0.30)`|`(0.71)`|`(3.26)**`|`(0.83)`|
|`Constant`|`-0.538`|`-0.995`|`0.526`|`-0.715`|
||`(0.67)`|`(1.34)`|`(2.11)* `|`(2.57)* `|
|`Observations`|`788`|`788`|`3316`|`3316`|

``` Source: BEEPS. Absolute value of z statistics in parentheses; * significant at 5%; ** significant at 1% ``` 46
**Table 4: OLS Analysis of Gross Transfer Incidence (from Children to Parents)**

|||(1)|(2)|(3)|(4)|
|---|---|---|---|---|---|
|**_Parent Characte_**|**_ristics_**|||||
||Age|0.006|(0.034)|0.009|(0.034)|
||Age square/100|-0.011|(0.024)|-0.012|(0.024)|
||Father|0.020|(0.035)|0.017|(0.035)|
|_Demographics_|Widowed|-0.036|(0.033)|-0.029|(0.033)|
||Number of children|0.030**|(0.013)|0.029**|(0.013)|
||Zhejiang|-0.024|(0.106)|-0.077|(0.111)|
||Urban|0.059|(0.038)|0.051|(0.039)|
||Living withotheradult children|-0.180**|(0.075)|-0.177**|(0.074)|
||House owner|-0.060|(0.043)|-0.061|(0.043)|
||_Education (Illiterates omitted)_|||||
||Less than primary education|0.009|(0.039)|-0.002|(0.041)|
||Primary school|0.002|(0.049)|-0.006|(0.049)|
||Middle school|0.068|(0.063)|0.043|(0.066)|
|_SES_|High school and above|0.050|(0.081)|0.020|(0.080)|
||P-value for education|0.828||0.958||
||Pre-transfer income (000s)|||||
||For the lowest 1/3 income group|0.016***|(0.005)|0.017***|(0.005)|
||For the middle 1/3 income group|-0.010|(0.011)|-0.008|(0.011)|
||For the highest 1/3 income grou|p -0.002|(0.002)|-0.002|(0.002)|
||P-value for pre-transfer income|0.017||0.008||
||Health Poor|||0.054|(0.037)|
||CES-D|||-0.009***|(0.003)|
||ADL or IADL disability|||-0.045|(0.038)|
|_Health_|Cognition score in [8, 11)|||-0.029|(0.045)|
||Cognition score in [0, 8)|||0.001|(0.056)|
||Major illness|||0.010|(0.033)|
||P-value for health|||0.089||
|**_Children Chara_**|**_cteristics_**|||||
||Age|0.043***|(0.011)|0.045***|(0.011)|
||Age square/100<br>|-0.037***|(0.011)|-0.039***|(0.011)|
||Oldest son|0.008|(0.035)|0.009|(0.035)|
|_Demographics_|Youngest son|0.030|(0.032)|0.032|(0.032)|
||Daughter|0.016|(0.030)|0.016|(0.030)|
||Married|0.086*|(0.050)|0.077|(0.049)|
||# of children (age<16)|0.013|(0.011)|0.012|(0.011)|
||_Education (Illiterates omitted)_|||||
||<br>Primary|0.118***|(0.035)|0.119***|(0.035)|
|_SES_|<br>Middle School|0.108**|(0.043)|0.111***|(0.043)|
||High school and above|0.229***|(0.045)|0.225***|(0.044)|
||P-value for SES|<0.001||<0.001||
|County Dummies||Yes||Yes||
|Observations||2,202||2,202||
|R-squared||0.133||0.142||

Note: 1) The sample includes those who are no younger than 25 and with at least one parent no younger than 60.

- 2) Parent characteristics are from main respondents.

- 3) Clustered standard errors at family level are in parentheses.

- 4) *** p<0.01, ** p<0.05, * p<0.1

27
52

# TABLE 6 CHANNEL FOR FIRST DIRECT EXPORTS, BY INITIAL SIZE OF RATTAN FIRM AND ETHNIC GROUP

||||Initial Si|ze (Number of W|orkers) .||
|---|---|---|---|---|---|---|
|Channel||1-50|,<br>51-100|101-250|>250|All|
|Collective:<br>Fair Outside<br>Indonesia|r<br>_Ph_<br>NPC|3<br>2<br>I||||3<br>2<br>1|
|Fair Inside|T|1||1||31|
|Indonesia|p<br>NP|1|1|1||2<br>t|
|NAFED|T|2|2|||4|
||P<br>NP|2|2<br>1|||3<br>1|
|AllColective|T|6|3|1||10|
||P|4|2|1||7.|
||NP|2|1|0||3|
|Private:<br>.luyerof Foreign|T|1|2|2||5|
|<br>Agent in Indonesia|<br>p||1|1||2|
||<br> <br>NP|1|1|1||3|
|Buyer/Business|T||2|2|3|7|
|Tour/Correspon-|P||1|||1|
|<br>denceOutside|NP|||1|2|6|
|Family or Friends|T||1|2|3|7|
||P||1|||1|
||NP|1||2|3|6|
|Business Contract/|T||3|||3|
|Partner|P||2|||2|
||NP||1|||1|
|Unspecified|T<br>P<br>NP||1<br>1-|||1<br>1|
|All Private|T|1|9|6|6|22|
||P||6|1||7|
||NP|2|2|4|5|13|
|Total|T|7|11|8|6|32|
||P|4|7|3|0|14|
||<br>NP|4|3|4|5|is|

Total **b** Pribumi **c** Non-pribumi
share of investment financed through retained earnings, commercial banks, government subsidies and loans, and informal sources. Retained earnings were the most important source of financing for all types and sizes of enterprises. Commercial banks were the second most important source of financing for large and medium-sized enterprises (28.8 and 17.8 percent of investment respectively). In contrast, small enterprises financed considerably less investment through commercial banks (12.7 percent of investment) and, in fact, on average financed a greater proportion of investment through informal sources such as family and friends, ;md moneylenders. State-owned enterprises generally financed smaller proportions of investment through commercial banks (9.4 percent) than other enterprises, relying more on funds from the government (21.9 percent). This might explain why state-owned enterprises generally saw financial sector constraints as less problematic than privately owned enterprises (see Table III).

The second major source of data is from a survey of central banks conducted b) the World Bank.1<sup>5</sup> Barth _et al._ (2001b) produced a survey consisting of 175 questions covering various aspects of banking sector development, which they sent to national regulatory and supervisory agencies in both developed and developing countries. They obtained at least partial responses from 107 countries, mostly between late 1998 and early 2000 (see footnote 7 in Barth _et al.,_ 2001b). In addition to questions about bank supervision and regulation, they also asked about the structure of bank ownership, including the share of assets held by banks that are over 50 percent foreign owned, the share of assets in state-owned banks, and the percent of deposits held by the 5 largest banks. These are the main banking sector variables from this source used in the empirical analysis. In addition to these variables, several variables related to the regulatory and supervisory environments are included in some model specifications to control for the quality of bank supervision and restrictions on entry. It is plausible that restrictions on entry 9