rafmacalaba/datause-encoder-data huggingface dataset:
||**US**|**PH**|**ZA**|**AU**|**KE**|
|---|---|---|---|---|---|
|(Intercept)|2.74<sup>***</sup>|2.50<sup>***</sup>|2.58<sup>***</sup>|2.65<sup>***</sup>|2.68<sup>***</sup>|
||(0.05)|(0.06)|(0.08)|(0.06)|(0.05)|
|_Treatment_||||||
|People pay|-0.09|0.19<sup>*</sup>|0.12|0.13|0.04|
||(0.07)|(0.08)|(0.11)|(0.08)|(0.08)|
|Rich people pay|<br>-0.03|<br>0.20<sup>*</sup>|<br>0.19|<br>0.26<sup>**</sup>|<br>0.17<sup>*</sup>|
||(0.07)|(0.08)|(0.11)|(0.08)|(0.08)|
|Companies pay|<br>-0.13|<br>0.14|<br>0.22|<br>0.10|<br>0.21<sup>**</sup>|
||(0.07)|(0.08)|(0.11)|(0.08)|(0.07)|
|R<sup>2</sup>|0.00|0.01|0.01|0.01|0.01|
|Adj. R<sup>2</sup>|0.00|0.00|0.00|0.01|0.01|
|<br>Num. obs.|1516|1516|715|972|1335|
|RMSE|0.99|1.06|1.05|0.92|0.98|

**_***_** _p < 0.001, ** p < 0.01, *_ p < 0.05

Table 3 Treatment Effects by Country for Tax Morale Dependent Variable (OLS). Positive coefficients indicate a stronger belief that cheating on taxes is wrong.

For the tax fine measure, the results are also reasonably consistent. Table 4 shows that four of the five countries show a positive and significant effect of the _rich people pay_ treatment on the tax fine outcome measure. Australia does not show a significant effect, but the coefficient is relatively large and in the expected direction. The _people pay_ treatment does not show a significant effect in any country.

||**US**|**PH**|**ZA**|**AU**|**KE**|
|---|---|---|---|---|---|
|(Intercept)|2.31<sup>***</sup>|2.30<sup>***</sup>|2.32<sup>***</sup>|2.52<sup>***</sup>|2.48<sup>***</sup>|
||(0.05)|(0.06)|(0.07)|(0.07)|(0.06)|
|_Treatment_||||||
|People pay|0.00|0.14|0.11|-0.06|-0.09|
||(0.07)|(0.08)|(0.10)|(0.09)|(0.08)|
|Rich people pay|<br>0.20<sup>**</sup>|<br>0.41<sup>***</sup>|<br>0.33<sup>**</sup>|<br>0.26<sup>**</sup>|<br>0.33<sup>***</sup>|
||(0.07)|(0.08)|(0.11)|(0.09)|(0.08)|
|Companies pay|<br>-0.00|<br>0.17<sup>*</sup>|<br>0.19|<br>0.07|<br>0.11|
||(0.07)|(0.08)|(0.11)|(0.09)|(0.08)|
|R<sup>2</sup>|0.01|0.02|0.01|0.01|0.02|
|Adj. R<sup>2</sup>|0.01|0.02|0.01|0.01|0.02|
|<br>Num. obs.|1516|1516|715|972|1335|
|RMSE|1.02|1.09|1.01|1.02|1.03|

**_***_** _p < 0.001, ** p < 0.01, *_ p < 0.05

Table 4 Treatment Effects by Country for Tax Fine Dependent Variable (OLS). Positive coefficients indicate more support for fining tax cheats.
delivery set of arguments comes from educational researchers seeking to explain what makes an effective school. In addition to the work on SBM done by economists, educational researchers have also studied SBM (Briggs and Wohlstetter, 2003). The authors arrive at a list of elements indicated in the literature to be characteristics of successful SBM schools: a vision focused on teaching and learning; use of decision making authority to bring about meaningful changes in teaching and learning; development of teachers’ knowledge and attitudes towards a learning community; the distribution of power across stakeholder and shared leadership, and mechanisms for collecting and communicating information about school performance.

_Cross-national benchmarking of SBM policies:_ A recent benchmarking study provides a cross-country comparison of SBM implementation along a set of dimensions.<sup>2</sup> A simple version of the benchmarking framework is reproduced in Table 1 below – it is being applied in many educational policy areas as part of the SABER framework. Scoring for the study used a more complicated variant that included sub-dimensions under each of five dimensions, with numerical values of 1, 2, and 3 attached to the Low, Medium and High classifications. The numerical values are treated as cardinal numbers and totaled – a further classification of the totals leads to a four-fold classification of SBM in a country as being “Latent”, “Emerging”, “Established” and “Mature” along each policy dimension and also an overall dimension. The actual scoring is done by experts who presumably know the policy in each country though it is not clear how comparability is established across different experts rating different countries.

|**Tab**<br>|**le 1: Autonomy and Acco**|**untability at the School L**<br>|**evel**|
|---|---|---|---|
|**Managerial Factor**||**Strength**||
||**LOW**|**MEDIUM**|**HIGH**|
|(A) Teacher<br>and<br>Personnel<br>Management|Centralized Hiring and<br>Firing|Regional Hiring and<br>centralized firing|Local hiring and firing|
|(B) Budget Planning<br>and Approval|Centralized budget<br>based on payroll plus an<br>allotment for materials<br>and utilities|Decentralized budget<br>with regional variations.<br>Budget based on payroll<br>and equity<br>considerations|Decentralized at school<br>level. Budget approved<br>by the school council<br>and funds transferred<br>directlytothe school|
|(C) Teacher<br>Assessment|None|Routine evaluations, no<br>direct accountability|Schools conduct<br>routine evaluations that<br>provide teachers and<br>schools with incentives<br>toperform better|
|(D) Student<br>Assessment|None or based on local<br>tests|Periodic standardized<br>testing but results not<br>made public|Routine standardized<br>testing; results made<br>public|

Source: Arcia et al., 2011 2 The SABER framework – see http://www.worldbank.org/education/saber. The “School Autonomy and Accountability” policy domain describes the detailed indicators for each of the five dimensions.

6
claims<sup>10</sup> by _all_ banks that the BIS tracks (Exhibits II.6 and II.7). These data show that total international claims with a maturity of over 2 years have been falling and, since 2009, longerterm claims growth has been often lower than total claims growth. This suggests international banks are letting their longer-term loans run off. In contrast, although falling, longer-term claims growth to EMDEs has remained positive since 2011. Yet, is has been lower than total claims growth, suggesting international banks have shifted their lending expansion in EMDEs towards shorter maturities.

**European bank deleveraging in EMDEs varies from region to region and from country to country depending on the bank’s business model and nationality.**<sup>**11**</sup> The Eastern European region was particularly affected by the retrenchment of mainly Austrian and Italian banks<sup>12</sup> that provided cross-border funding and local credit. Latin America has a strong presence of Spanish bank subsidiaries although they tend to be more self-reliant and raise deposits in domestic markets. The Middle East and North Africa has a presence of French banks. British banks are active in Asia and South-Saharan Africa among others.

**_II.6_ For all banks combined, growth of international** **_II.7_ As a result, the fraction of longer maturities to** **<u>claims with longer maturities lagged behind... total outstanding international claims has declined</u>** Growth Intl Claims of All Banks Claims with maturity >2yr as a Fraction of Total Intl _%, YoY (2001Q1-2012Q3)_ Claims of All Banks _%,_ _<u>(2001Q1-2012Q3)</u>_

<!-- Start of picture text --> 60% 40%<br>40% 35%<br>20%<br>30%<br>0%<br>25%<br>-20%<br>20%<br>-40%<br>15%<br>Total to all<br>Total maturity >2yr to all<br>Total to EMDEs<br>Outside Europe EMDEs Europe<br>Total maturity >2yr to EMDEs<br>Source: BIS Source: BIS<br>2001Q1 2001Q4 2002Q3 2003Q2 2004Q1 2004Q4 2005Q3 2006Q2 2007Q1 2007Q4 2008Q3 2009Q2 2010Q1 2010Q4 2011Q3 2012Q2<br>2000Q1 2000Q4 2001Q3 2002Q2 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1<br><!-- End of picture text -->

**European banks continue to be part of as significant portion of deals in the syndicated loan market in EMDEs, but their volumes have been falling.** European banks are very frequent deal participants in the syndicated loan market to EMDEs which includes project finance and

> 10 International claims are similar to foreign claims but exclude claims of local affiliates in local currency.

> 11 Also see Section V for a discussion on transmission channels.

> 12 Branches and subsidiaries of foreign banks have a significant market share in some CEE countries: about 75 per cent in the total assets of the banking system in Hungary and Latvia and close to 90 per cent in Romania.

10
**18 |** P a g e

Over 2004‐2007, the largest chunk of FDI flows went to the financial sector (41 percent) followed by telecommunications (26 percent in 2007 alone due the privatization of Telekom Srpske). Manufacturing took 18 percent of the total FDI inflows during this period. In terms of FDI stock, however, the share of industries producing tradable goods in total FDI stock appears to be larger amounting to almost 35 percent, which appears to be close to the average for transition economies. Metal industries (6.3 percent) followed by foods (6.2 percent) have been the major recipient of FDI. Unfortunately, no breakdown for “other industries” is available in FDI data obtained from the BiH Central Bank (Table 11).

The origins of FDI are important for one reason: those inflows originating in highly developed countries and/or carried out by large MNCs are more likely to bring superior technology, organization and marketing skills. On this count, the picture is mixed as there is a conspicuous absence of large MNCs and a big chunk of FDI came from two neighboring countries—Serbia and Croatia. They accounted in 2008 for almost 30 percent of total FDI stock. Their large presence may point to the significance of direct informal links influencing investment decisions. On the other hand, however, the EU accounted for almost 50 percent of the FDI stock. Austria with 34 percent and Slovenia with 10 accounted for almost 90 percent of the EU total investments in BiH. Around 12 percent come from countries that have not been identified in statistics published by Central Bank of BiH (Figure 2).

**Table 11: FDI stock in 2007 by sectors of the national economy (in percent)**

|**Industries**|**34.5**|**Services**|**65.5**|
|---|---|---|---|
|Foods & beverages|6.2|Construction<br>Sale, maintenance and repair of motor vehicles and|0.7|
|Chemical products|1.6|motorcycles/retail sale of automotive fuel<br>Wholesale trade and commission trade, except of motor|1.4|
|Other non‐metallic mineral products|3.3|vehicles and motorcycles|6.7|
|Manufacture of basic metals|5.3|Telecommunications|16.4|
|Manufacture of fabricated metal products,<br>except machinery and equipment|1.0|Financial intermediation, except insurance and pension<br>funding<br>Insurance and pension funding, except compulsory social|38.1|
|Motor vehicles, trailers and semi‐trailers|1.6|security|1.0|
|Other industries|15.5|Real estate activities|1.3|

Source: Base on the data provided by the Central Bank of Bosnia and Herzegovina.
_Table 7: Heterogeneity in disaster exposure by plot characteristics_

|**Dependent variable:**Any disaster loss on plot|(1)<br>Pooled|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Mali|(5)<br>Niger|(6)<br>Nigeria|(7)<br>Tanzania|
|---|---|---|---|---|---|---|---|
|Any hired labor on plot|-0.0151|-0.00717|-0.0513***|-0.000535|-0.0291|-0.00315|-0.0130|
||(0.0111)|(0.0242)|(0.0167)|(0.0142)|(0.0205)|(0.0217)|(0.0151)|
|Any inorganic fertilizer used|-0.0445***|-0.0482**|0.0236|-0.0621***|-0.0527*|-0.0357**|-0.0885***|
||(0.0109)|(0.0187)|(0.0161)|(0.0171)|(0.0289)|(0.0154)|(0.0290)|
|Any organic fertilizer used|0.0285***|0.0666***|0.0382**|-0.00299|-0.00833|-0.0106|-0.0126|
||(0.0105)|(0.0195)|(0.0158)|(0.0160)|(0.0255)|(0.0188)|(0.0266)|
|Plot is irrigated|-0.00339|0.000150|-0.0647|-0.201***|-0.169***|0.00297|0.0370|
||(0.0211)|(0.0334)|(0.0592)|(0.0461)|(0.0535)|(0.0305)|(0.0502)|
|Plot is intercropped|0.0491***|0.0353|0.146***|-0.0335|-0.0271|-0.0221|0.0419**|
||(0.0117)|(0.0242)|(0.0181)|(0.0292)|(0.0364)|(0.0164)|(0.0207)|
|Plot is owned|-0.000302|0.0200|-0.00111|-0.0542**|-0.0529|-0.0296|-0.00966|
||(0.0121)|(0.0199)|(0.0215)|(0.0246)|(0.0329)|(0.0184)|(0.0234)|
|Log plot area (ha)|0.00875***|0.00983*|0.0219***|0.000514|-0.0272***|-0.0122*|0.0339***|
||(0.00333)|(0.00504)|(0.00568)|(0.00562)|(0.00835)|(0.00677)|(0.00803)|
|Plot topographic wetness index|0.00731***|0.00315|0.00503|0.00210|0.00349|0.00134|0.0121***|
||(0.00165)|(0.00407)|(0.00345)|(0.00244)|(0.00267)|(0.00161)|(0.00225)|
|Plot elevation (m)|-9.16e-05***|-8.41e-05***|-0.000371***|-0.000299**|-0.000172|-2.12e-05|-6.58e-05***|
||(1.60e-05)|(2.64e-05)|(3.41e-05)|(0.000131)|(0.000186)|(4.61e-05)|(2.37e-05)|
|Urban household|-0.0211|0.00330|-0.115***|-0.0356|0.0446|-0.0212|0.0273|
||(0.0206)|(0.0529)|(0.0410)|(0.0484)|(0.0472)|(0.0249)|(0.0291)|
|Household engaged in livestock farming|0.00545|0.0210|-0.00556|0.0271|-0.145***|-0.0153|0.0398**|
||(0.0101)|(0.0211)|(0.0133)|(0.0203)|(0.0466)|(0.0182)|(0.0173)|
|Household has access to electricity|0.0711***|0.120***|0.0173|-0.0240|-0.0302|0.00143|0.0195|
||(0.0161)|(0.0241)|(0.0283)|(0.0167)|(0.0493)|(0.0197)|(0.0358)|
|Observations|116,773|39,801|17,900|31,257|9,004|6,623|12,187|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Average marginal effects from multivariate log<br>parentheses. *** p<0.01, ** p<0.05, * p<0.1|YES<br>it regressions. Base c|NO<br>ategory for crop fixed|NO<br>effects is 'other cro|NO<br>p'. The estimates are|NO<br>weighted to be natio|NO<br>nally representative.|NO<br>Standard errors in|

28
**Hausman, R., L. Pritchett, and D. Rodrik** (2005), “Growth Accelerations,” _Kennedy School of Government_ , Mimeo.

**Hausmann, R. and Rodrik, D** . (2005), “It is not how much you export but what you export”. John F. Kennedy School of Government, Universidad de Harvard, august, Cambridge, MA

**Hausmann, R., Hwang, J. and  Rodrik, D.** (2006). "What You Export Matters," _CEPR Discussion Papers_ No.5444

**Johnson, Simon, Jonathan Ostry and Arvind Subramanian** (2006), “Leves for Growth, Finance and Development”, March Vol. 43, No. 1.

**Krugman, P.R** ., (1987), “The Narrow Moving Band, the Dutch Disease, and the Economic Consequences of Mrs. Thatcher,” _The Journal of Development Economics_ , Vol. 27, pp. 41-55.

**Krugman, P** . (1999), “Balance Sheets, the Transfer Problem, and Financial Crises.” International Tax and Public Finance, Vol.6(4)(November), pp. 459-72.

**Levy-Yeyati, E. and Sturzenegger, F.** (2005), “Classifying Exchange Rate Regimes: Deeds vs. Words.”, _European Economic Review_ , Vol. 49 (August), pp. 1603-1635.

**Levy Yeyati, E. and F. Sturzenegger** (2001), “Exchange Rate Regimes and Economic Performance”, _IMF Staff Papers_ , Vol. 47, pp. 62-98.

**Levy Yeyati, E. and F. Sturzenegger** (2003a), “To Float or to Trail: Evidence on the Impact of Exchange Rate Regimes”, _American Economic Review_ ,Vol. 93, No. 4, September 2003.

**Levy Yeyati, E. and F. Sturzenegger** (2003b), “A de facto Classification of Exchange Rate Regimes: A Methodological Note”, posted at http://www.aeaweb.org/aer/contents/ of _American Economic Review_ , Vol. 93, No. 4, September.

**Levy-Yeyati, E.** (2005), “Exchange Rate Regimes in the 2000s: A Latin American Perspective”, prepared for the _Conference on A New Economic Development Agenda for Latin America, Salamanca_ , October 8-9.

**Levy-Yeyati, E.** (2006), “Financial Dollarization: Evaluating its Consequences”, _Economic Policy, Vol. 21, No. 45, pp. 61-118, January._

**Lewis, WA** (1958) “Unlimited Supplies of Labour: Further Notes”, _The Manchester School_ , Vol. 26, 1:132.

**Prasad, Eswar, Raghuram Rajan, and Arvind Subramanian** (2006) Foreign Capital and Economic Growth, Research Department IMF, August 30.

**Rajan, Raghuram and Arvind Subramanian** (2005) What Undermines Aid’s Impact on Growth?, IMF Working Paper, June.

36
base D-D model on an outcome variable that is equal to one if the child works in paid or nonpaid activities or is looking for a job, and also if the child is not attending school. The econometric results confirm that the increase in child labor force participation attributed to Agatha is mostly seen among children –predominantly boys– who also stopped attending school (Table 7). This set of results holds for the whole sample and for rural areas. In contrast, the proportion of children simultaneously working –or looking for a job– and attending school did not change over time in shock areas with respect to the comparison group, providing an indication that at the margin the added labor force of children reduced their school participation.

# **5.2 Robustness analysis**

Additional empirical exercises confirm the robustness of the negative effects of the shock on the main variables of interest. We first test the central assumption underlying the internal validity of the identification strategy, namely whether or not the outcomes for affected and lessor non-affected households were systematically set on different pathways irrespective of Agatha. To do so, we pool two rounds of the _Encovi_ data collected before the shock (2000 and 2006) to estimate placebo treatment effects of the shock on consumption and poverty. Overall, results of this “fake” binary treatment do not provide evidence of diverging trajectories preceding the shock between the treatment and control groups (Table 8- Panel A). The doubledifference estimators for consumption per capita, poverty headcount and poverty gap are all statistically insignificant.<sup>14</sup> In a second exercise we assess whether other forms of sample selection are likely to confound the results. Using pre- and post-shock data from 2006 and 2011, we run models to estimate placebo treatment effects on independent variables that capture household head characteristics –most of which are supposed to not be directly affected by the shock– such as age, education, gender, marital status and area of residence. Systematic changes in these predetermined variables between the treatment and comparison groups could signal compositional changes such as endogenous migration and mortality. Nonetheless, results of this placebo test show that Agatha has no statistically significant relationship with any of the variables analyzed (Table 8- Panel B).

> 14 Results for most of the other outcomes analyzed in the paper (not shown but available upon request) follow a similar pattern.

17
productivity differences across industries which are fixed over time) and with and without year fixed effects (which allow for unobserved time-varying determinants of TFP which are common across industries). Intangible capital is statistically significantly correlated with both measures of TFP, for all models except for the industry fixed effects model without controls for time (for TFP1).

|**Table 2**<br>|**: Intangibl**<br>|**e investmen**<br>|**t and TFP**<br>|**across man**<br>|**ufacturing**<br>|**industries**<br>|**in Brazil**<br>||
|---|---|---|---|---|---|---|---|---|
|Dependent variable|TFP1|TFP1|TFP1|TFP1|TFP2|TFP2|TFP2|TFP2|
|Industry FEs|No|No|Yes|Yes|No|No|Yes|Yes|
|Year FEs|No|Yes|No|Yes|No|Yes|No|Yes|
|**Intangible capital**|**0.190***|**0.416****|**0.175**|**0.585****|**0.206****|**0.420****|**0.191***|**0.574****|
||**(1.810)**|**(2.390)**|**(1.640)**|**(2.130)**|**(1.960)**|**(2.390)**|**(1.800)**|**(2.090)**|
|Constant|-0.077|-0.095|-0.070<br>|-0.162|-0.083|-0.100|-0.077<br>|-0.161|
||(-1.570)|(-1.070)|(-1.530)|(-1.360)|(-1.710)|(-1.110)|(-1.670)|(-1.340)|
|R-squared – within|||0.012|0.218||||0.208|
|- between|||0.091|0.091||||0.101|
|- overall|0.016|0.206|0.016|0.202|0.019|0.198||0.195|

Note: ***, ** and * denote statistical significance at the 1%, 5% and 10% levels; t-statistics are in parentheses.

We stress that these correlations with exports and traditional TFP are only suggestive evidence. Intangible investment could be correlated with other unobserved sources of revenue or productivity growth. Similarly, it is also possible that the correlation observed in Figure 14 is driven by unobserved shocks that affected both export growth and intangible expenditures. Moreover, other determinants of comparative advantage, such as fixed capital accumulation, have not been controlled for in Figure 14. But at the very least, we hope that this serves to motivate more rigorous future work.

# **5. CONCLUSION**

The key message of our study is that there is significant intangible investment in the Brazilian business sector, comparable in levels (as a share of GDP) to that estimated for EU countries like Italy and Spain using the same methodology. As a share of GDP, it is about one-third of the investment level in the US, Japan and the UK, the countries which are leading in this area. This finding leads us to two broad conclusions about future work in this area.

33
changing the fragmented structure of regulatory agencies that give rise to regulatory arbitrage and weak enforcement; rules governing decision-making processes in corporate boards; and improving the role of institutional investors acting in a fiduciary capacity in forming and enforcing corporate governance policy, including voting and board representation.

Finally, the market for corporate control in India is at an emerging stage. Promoters typically maintain control of listed firms with relatively small shareholding given the support of lending institutions. Hostile takeovers were not allowed by the Securities and Exchanges Board of India (SEBI) as recently as 1997. There have been close to 600 public offers for corporate takeovers since then. Nevertheless, regulations and the ownership structure of listed firms make it difficult for attempts at hostile acquisitions to succeed (e.g., Kripalani 2000; Vaidyanathan 2002).

To summarize, despite extensive and strong investor protection on paper, actual protection of investors and standard corporate governance mechanisms are weak in the listed sector. We provide a discussion of the role of alternative and informal governance mechanisms in Section V.6 below.

# **IV.2 Empirical evidence on ownership, financing, dividend, and valuations of listed firms**

In this subsection, we examine and compare various characteristics of listed firms in India with those of other countries. Our initial sample of 1,395 listed firms (panel data set for the period 1995 to 2004) is collected and compiled from the CMIE _Prowess_ database (same database for Table 5 above). There are four groups of listed firms in our sample (there are no PSUs in the sample).

1. Small manufacturing firms: SSI firms (definition in Section III); 2. Large manufacturing firms: Non-SSI or NSSI firms; 3. Small non-manufacturing firms: SSSBE firms (definition in Section III); 4. Large non-manufacturing firms: Non-SSSBE or NSSSBE firms.

For each group of firms, data on financials, market variables, and ownership patterns were collected. Due to missing data items, our final sample for most of our empirical tests consists of 854 24