rafmacalaba/datause-encoder-data huggingface dataset:
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# **I. Introduction**

Have countries reformed bank regulatory and supervisory policies over the last twelve years, and which reforms have enhanced banking system performance? The Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank (Bank) work on designing effective financial sector reform strategies, communicating those recommendations to countries, and helping countries implement changes to improve the efficiency and stability of their financial systems. Indeed, in 1999 the Bank and the IMF started the Financial Sector Assessment Program (FSAP) to assess systematically the status of banking systems in countries and to make recommendations for reform. But have these recommendations been adopted and have they been successful at boosting the efficiency and stability of financial systems?

Motivated by these very basic questions, the World Bank asked us more than twelve years ago to start assembling the first cross-country database on bank regulation and supervision. Based on guidance from bank supervisors, financial economists, and our own experiences, the Bank began putting together an extensive survey of bank regulation and supervision.<sup>1</sup> The original survey, Survey I, had 117 country respondents in 1999.<sup>2</sup> The first update in 2003, Survey II, characterized the regulatory situation at the end of 2002, and had 152 respondents. Survey III, the last one taken while one of us was still with the Bank, has responses from 142 countries characterizing regulation and supervision as of 2005-2006. The World Bank completed Survey IV in 2011 (with some corrections in 2012) with responses from over 125 countries.

In this paper, we reassess what works in banking regulation based on the new World Bank survey (Survey IV) of bank regulation and supervision around world. This paper briefly

> 1 As in Barth, Caprio, and Levine (2006), we sometimes use the term regulation generically to apply to banking-sector policies and compliance mechanisms, while at other times to discuss particular, specific regulations or special aspects of supervision.

> 2 Specifically, the survey received answers from 1998 through 2000, but since the bulk of the answers were in 1999 we use this year to date the survey.
data from the 42-sector “non-competitive” IO tables, for 2007 and 2010, respectively, along with firm census data for 2008. These data sets are obtained from China’s National Bureau of Statistics (NBS). Notice that all evolutions in value added by firm type reported below arise from the changes in the IO table coefficients, not from the census data as we only have access to one year of data. The economic census data cover over 5 million enterprises in China, including all state-owned and private enterprises spanning all manufacturing and non-manufacturing industries. Balance sheet information, such as registration ownership type, equity share by ownership, output, value added, four-digit industry code (about 900 categories), exports, employment, original value of fixed assets, and intermediate inputs. The ownership type of a firm in our analysis is defined based on the registration type and equity share by ownership. Specifically, a firm is considered state-owned (foreign-invested) if it is registered as a state (foreign) company _or_ has more than (and equal to) 50% equity owned by state (foreign) investors.

There are 42 domestic and 42 imported product groups in the original “non-competitive” IO table. Each product group is further split into six sub-groups by ownership type and size: large SOEs (LSOE), small and medium SOEs (SSOE), large FIEs (LFIE), small and medium FIEs (SFIE), large private enterprises (LP), and small and medium private enterprises (SME). Firm size category (large and small-and-medium) is determined by firm employment and sales, with thresholds specified by the NBS. The classification criteria vary across industries, and are listed in Table A1 in the appendix.

The decision of putting firms into 6 groups is supported by the underlying firm distribution of export intensity and value added to sales ratios reported in the NBS micro data. Fig. 2 illustrates that firm average export intensity differs significantly across ownership types, not so much along the firm size dimension. In particular, FIEs are a lot more export-oriented than non-FIE firms. Fig. 3 illustrates that FIEs also appear to have higher value added to output ratios (VAY) than non-FIE firms. Within non-FIE firms, large firms tend to have higher VAY. Within FIEs, there is little difference in these key variables between Hong Kong SAR, China, Macau, and Taiwan, China (HKMT) firms and non-Chinese FIEs. Based on these findings, we separate firms based on 3 ownership types and 2 sizes, and group HKMT firms with other FIEs.

After assigning firms from the census to different groups, total sales/receipts at the group level are used to allocate gross output of each sector to each ownership-size type, while groups’ annual payrolls are used to split labor and non-labor components of the value added within the group. We can also assign exports (but not imports) into firm types in almost all industries using the firm census data.<sup>13</sup> Detailed import data, obtained from the statistical department of China Customs Administration, are disaggregated

> 13 Export data are not available for most service sectors.

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welfare economic criterion). Absent such a literature, it would be best to formulate policies cautiously.

Finally, it is worth noting that most models of social protection institutions consider single policies. An interesting exception, though, can be found in the work of Blanchard and Tirole (2005), who model the joint design of unemployment insurance and employment protection. More generally, it is to be expected that the optimal design of one institution would be dependent on how other interacting institutions are designed.

# **<u>Empirical Evidence</u>**

There exists a huge statistical/econometric literature on labor markets in both developed and developing countries. Highlights are surveyed in various articles in the three-volume edition of the Handbook of Labor Economics (Ashenfelter and Card, 1999), among other places.

On the empirical side, six kinds of data analysis are potentially useful. They are aggregate cross-sectional quantitative data, micro data from comparable cross sections, panel data, cross country time series data, experimental data, and qualitative data.

This review of the empirical evidence begins by presenting examples of empirical research using each of these types of data that led to better understanding of labor market functioning and/or interesting policy conclusions in developing countries. It then continues to address what we have learned about finding the right labor market model to fit a particular country’s circumstances. As an example of good policy-relevant empirical work, the research that has been conducted on the effects of India’s employment protection laws is featured. The section concludes by considering some of the limitations of existing policy research in the area of the economics of education.

# **<u>Empirical Research on Labor Markets and Labor Market Policies: Six Kinds of Data</u>**

In this section, I present examples of studies that have used each kind of data. Before beginning, two lessons presented by Ravallion (2007) should be mentioned. The first is that no single evaluation tool can claim to be ideal in all circumstances. The second is that the standard tools of counterfactual analysis for mean impacts have some potentially severe limitations.

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revenue is TD 5,670,918 (US$2,998,365), the average wage is TD 4,225 per year (US$2,234) and average number of workers is 111. All values are in real terms, depreciated by the price index provided by the INS.

_Table 2. Summary Statistics of the Variables_

||Variable|Definition|Obs.|Mean|S.D.|Min.|Max.|
|---|---|---|---|---|---|---|---|
|||Firms invest only in internal||||||
||Int only|innovation|16,471|0.04|0.19|0|1|
|||Firms invest only in external||||||
|D|Ext only|technology acquisition|16,471|0.30|0.46|0|1|
|ummy<br>variables|Innover|Firms invest in internal innovation|16,471|0.35|0.48|0|1|
|<br>{Z}|Externer|Firms invest in external innovation|16,471|0.62|0.49|0|1|
|||Firms invest in both sources of||||||
||Both|innovation at the same time|16,471|0.32|0.47|0|1|
|||Firms invest in any sources of||||||
||All|innovation|16,471|0.65|0.48|0|1|
|||Log investment amount of any||||||
|Innovation|ln_n_innov|sources of innovation|16,471|6.00|4.65|0|16.37|
|||Log investment amount of internal||||||
|investmen<br>t {U}|ln_n_inter|innovation<br>Log investment amount of external|16,471|2.93|4.21|0|16.25|
||ln_n_exter|innovation|16,471|5.48|4.55|0|16.21|
|||Log amount of sales revenue,||||||
||ln_revenue_d|depreciated|16,457|13.91|1.65|6.17|21.50|
|Inputs for|ln_labor|Log number of employees|16,471|3.98|1.23|0|8.48|
|production||Log amount of capital assets||||||
|function|ln_capital_d|beginning of the year, depreciated<br>Log amount of intermediary inputs|16,015|13.42|1.83|4.61|20.57|
||ln_interm_d|(materials), depreciated|16,167|12.73|2.23|1.60|20.19|
||Tech<br>staff|Technical staff share among total||||||
||raito|employees|16,394|0.74|0.25|0|1|
|Factors<br>h|Skills|Log average wage|15,369|<br>8.39|0.68|2.28|12.47|
|tat<br>ifl|Firm age|Years since firm’s creation|14,279|27.06|13.45|6|200|
|nuences<br>innovation|Dominance|A firm’s market share|16,457|0.01|0.06|0|1|
||Export_stat|Export status dummy|16,471|0.51|0.50|0|1|
||FDI_stat|FDI status dummy|16,471|0.27|0.44|0|1|

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Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 14: Fraction of the Year in Formal Employment - Tradable Sector Worker Sample - 19902009 <!-- Start of picture text -->
1.1<br>0.9<br>0.7<br>0.5<br>0.3<br>0.1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.1<br>-0.3<br><!-- End of picture text -->

Each point reflects an individual regression coefficient where the dependent variable is the fraction of the year spent in formal employment for the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive estimates imply smaller fractions of the year in formal employment for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

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# Multi-product exporters:

# Diversification and micro-level dynamics<sup>∗</sup>

Leonardo Iacovone<sup>†</sup> and Beata S. Javorcik<sup>‡</sup>

> ∗The authors are grateful to Gerardo Leyva and Abigail Dur ́an for granting us access to INEGI data at the offices of INEGI in Aguascalientes under the commitment of complying with the confidentiality requirements set by the Mexican Laws. Special thanks go to all INEGI employees who provided assistance and answered our questions, in particular to Gabriel Romero, Alejandro Cano, Araceli Martinez, Armando Arellanes, Ramon Sanchez, Otoniel Soto, Candido Aguilar, Adriana Ramirez. The author are also thankful to Alan Winters, Bernard Hoekman and participants of the World Bank trade seminar for their useful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors or the countries they represent.

> †University of Sussex and World Bank, 1818 H Street, NW; Washington, DC, 20433, USA. Email: Liacovone@worldbank.org

> ‡University of Oxford and CEPR, Department of Economics, Manor Road Building, Manor Road, Oxford OX1 3UQ, UK. Email: beata.javorcik@economics.ox.ac.uk

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not the predominant means of adaptation in this setting. It is possible that the construction of schools differs across settings, such that materials or ventilation approaches result in a greater ability to keep classrooms cool in hotter areas. We leave further examination of these important questions around adaptation to future work.

# **7 Robustness Checks**

## **7.1 Does temperature affect stream choice?**

One potential source of endogeneity in our model could arise from stream choice being a function of temperature. Since the stream of study is chosen at the start of 11<sup>_th_</sup> grade, it could be that temperature during the 10<sup>_th_</sup> grade affected performance on end-of-year exams, which, in turn, affected the choice of stream.

We know that for a given location, temperature across years is correlated.

If there is selection into streams based on temperature exposure during 10th grade, 2. then our results might be biased. To test this, we run equation where the outcome variable _PS_ is the proportion of students in the science stream in school _j_ in year _t_ . _Lag_ 2 _T_ is the second-year lag of temperature bin _M_ . _Lag_ 2 _Prec_ is the second-year lag of precipitation bin _N_ . _Female Share_ is the share of women in school _j_ and year _t_ . We include the share of female students to control for gender specific preferences on which stream to enroll in. _γr_ ( _j_ ) and _αt_ are the region (which the school is mapped to), and year fixed effects, respectively.

The results presented in Table 10 are all economically insignificant, suggesting temperature is not playing a large roll in stream selection. While the point estimates of 10<sup>_th_</sup> grade exposure to heat are negative, the effect size is infinitesimal.

## **7.2 Does temperature affect grade progression?**

It could be that our population of students is a selected sample of students who are resilient to or more adapted to higher temperatures. This would occur if only the students who can adapt remain in school long enough to appear in our sample. If this was true, we would be underestimating the effects of high temperature exposure on test performance.

To address this concern, we consider data on grade progression within the schools in our sample and check grade progression of each school from 11<sup>_th_</sup> grade to 12<sup>_th_</sup> grade. (We do not have cohort information prior to the 11<sup>_th_</sup> grade.) We have this information for approximately 83% of school x exam year combinations between 2017-2019.

We do not find any effects of high temperature exposure in the 11<sup>_th_</sup> grade on drop out rates and grade progression rates in our sample (Table 11). This is true for both male and female students, suggesting that our estimates are valid.

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# **Figures and Tables**

## **Figures**

Figure 1: International migration from Nepal <!-- Start of picture text --> All 7.43<br>India<br>Non-India<br>4.63<br>3 .49 3.56 3.41<br>2.68<br>3.17<br>2.8<br>2.48 2.61<br>.78<br>.37<br>.19<br>1971 1981 1991 2001 2011<br>Year<br>8<br>6<br>4<br>2<br>Migrant to population share, (percent)<br>0<br><!-- End of picture text --> Source: Census data for various years.

Note: This figure shows the migrant to population ratio for each of the census years. Destination breakdown was not available for 1971.

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# **VI. Conclusions**

In this paper we have developed an innovative small open economy computable general equilibrium model of the Kenyan economy that is capable of assessing the impact of the liberalization of regulatory barriers against both domestic and multinational service providers. We find that the reform package we consider in this paper (in many cases reforms under consideration by the Kenyan government) could provide very substantial gains to the Kenyan economy. Reduction of the barriers against potential service providers, both foreign and domestic, is the largest source of the gains. Moving to tariff uniformity, could provide additional significant gains and provide an improved environment for the political economy of protection.

Reforms that lead to greater access to business services will improve the productivity of labor and capital in all the sectors of the economy. We find that in the long run, the increased productivity of capital will induce an accumulation of capital and an increase in the capital stock, which will result in a general expansion of Kenya manufacturing.

# **References**

Broda, Christian and David Weinstein (2004), “Variety, Growth and World Welfare,” _American Economic Review_ , 94 (2), May, 139-144.

.

- Brown, Drusilla, Alan Deardorf, Alan Fox and Robert Stern (1996), “Liberalization of Services Trade,” in W. Martin and L. A. Winters, eds., _The Uruguay Round and the Developing Countries_ , Cambridge: Cambridge University Press.

Brown, Drusilla and Robert Stern (2001), “Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services,” _Review of International Economics_ , 9(2): 262-286 _._ Central Bureau of Statistics (2007a), Economic Survey, 2006, Nairobi: Ministry of Planning and National Development, Republic of Kenya.

Central Bureau of Statistics (2007b), Statistical Abstract, 2006, Nairobi: Ministry of Planning and National Development, Republic of Kenya.

Chinitz, B. (1961), “Contrast in agglomeration: New York and Pittsburgh,” _American Economic Review, Papers and Proceedings_ , 51:279-89.

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# **1. Introduction**

The impact of schooling on health care use has been examined in a wide range of settings and found to be important. Controversy exists, however, over whether, and to what extent, the estimated returns to schooling reflect the influence of unobserved preferences, knowledge, and motivation of individuals. To the extent that schooling outcomes reflect such attributes, existing estimates may overstate the schooling effect because of the failure to control for background variables. An alternative argument is that estimates of the schooling effect may be downwardly biased due to measurement error in the schooling variable. This paper contributes to the resolution of this debate by adopting an instrumental variable (IV) approach to estimate the impact of female schooling on maternal health care use in Indonesia during the 1990s. A large-scale school construction program that took place in Indonesia in the 1970s, the Sekolah Dasar INPRES program (SDIP) is used to construct an instrumental variable for education. Standard regression estimates presented here indicate that each additional year of schooling does indeed have a significant, positive effect on the timing of ante-natal care visits, use of hospital care and skilled assistance at delivery. IV estimates of the schooling effect are larger. The results suggest that schooling has a positive impact on maternal health care use even after eliminating the effect of unobserved variables and measurement error. The analysis also highlights several other correlates of poor maternal health use such as low levels of access to services and household socioeconomic status, which could potentially offset the impact of schooling.

2
- so -

equivalent to allowing a deduction for the cost of risk-taking. To the extent that loss offsetting of risks does occur, the risk premium itself ought to be reduce%l by the tax, h(1-u). Since our methodology does not reduce the risk premium by the tax, it will yield an overestimate of _rg_ to the extent that loss offsetting of risk occurs.

Whether or not loss offsetting occurs depends on the source of the risk. If the risk takes the form of capital risk as discussed in Bulow and Summers (1984), loss offsetting does not occur. On the oth3r hand, risks reflected in varying revenues will almost certainly be partly offset.

Risk can also take the form of uncertainty about future government policy, i.e.,policy risk. Auerbach (1990) underlines the importance of the credibility of tax regime for the effectiveness of tax policies. If an announced tax policy change is seen as a temporary change only and likely to be reversed, it will not have the same effect as a possibly less stimulative alternative but one which is seen as permanent. Also with an uncertain tax climate, investors are likely to demand higher expected return from investments.

_d. lhe data_ We briefly outline here the manner in which numbers can be attached to the variables in _rg_ and _rn._ The exact manner in which data are obtained depends upon the level for aggregation at which effective tax rates are being computed. Nonetheless the same general approach can be followed in all cases. The following summarizes the principle followed in constructing the various types of data.

_(1) Flnancing Ratios_ (a,a). King and Fullerton calculate effective tax rates separately for different sources of finance so they do not really need to use financing ratios. However, some studies have incorporated financial ratios for different types of firms as an element of their calculations thus picking up the way in which the interest deductibility provision benefits some types of firms more than others. These can be constructed using the structure of liabilities from published balance sheets. Depending on the application, either differences between end-of-year values of liabilities of debt, retained earnings and new issues, or the stock values themselves can be used to estimate 1 and a.

_ui) Rates of Return (i,p.o)._ As mentioned, King and Fullerton simply present their effective tax rates for arbitrary financial rates of return. Alternatively, one can try and measure the actual effective tax rates for a given year by using observed financial data. The bond rate _I_ can be obtained using longtern nominal corporate bond yields. The required return on equity before personal tax can be calculated