rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4: Correlations between core indicators-raw and residual**

## **Raw correlations**

||Log private<br>credit to GDP|Log deposits<br>to GDP|Log net-<br>interest<br>margin|Log ATMs<br>per 100,000<br>people|Log private<br>bonds to<br>GDP|Log public<br>bonds to<br>GDP|Log assets of<br>institutional<br>investors|<br>Log stock<br>market<br>capitalization<br>to GDP|<br>Log number<br>of listed firms<br>per mln.<br>inhabitants|
|---|---|---|---|---|---|---|---|---|---|
|**Log private credit to GDP**|1.00|||||||||
|**Log deposits to GDP**|0.85|1.00||||||||
|**Log net-interest margin**|-0.68|-0.71|1.00|||||||
|**Log ATMs per 100,000 people**|0.71|0.59|-0.47|1.00||||||
|**Log private bonds to GDP**|0.66|0.55|-0.49|0.69|1.00|||||
|**Log public bonds to GDP**|0.29|0.40|-0.33|0.43|0.41|1.00||||
|**Log assets of institutional investors**|0.78|0.76|-0.65|0.65|0.78|0.15|1.00|||
|**Log stock market capitalization to GDP**|0.61|0.63|-0.45|0.58|0.47|0.29|0.70|1.00||
|**Log number of listed firms per mln. inhabitants**|0.51|0.52|-0.44|0.54|0.53|0.27|0.46|0.50|1.00|
|**Log stock market turnover**|0.34|0.27|-0.30|0.40|0.25|0.05|0.27|0.30|0.16|

## **Correlations of residuals**

||Log private<br>credit to GDP|Log deposits<br>to GDP|Log net-<br>interest<br>margin|Log ATMs<br>per 100,000<br>people|Log private<br>bonds to<br>GDP|Log public<br>bonds to<br>GDP|Log assets of<br>institutional<br>investors|<br>Log stock<br>market<br>capitalization<br>to GDP|<br>Log number<br>of listed firms<br>per mln.<br>inhabitants|
|---|---|---|---|---|---|---|---|---|---|
|**Log private credit to GDP**|1.00|||||||||
|**Log deposits to GDP**|0.69|1.00||||||||
|**Log net-interest margin**|-0.40|-0.48|1.00|||||||
|**Log ATMs per 100,000 people**|0.21|0.19|0.21|1.00||||||
|**Log private bonds to GDP**|0.32|0.19|-0.19|0.11|1.00|||||
|**Log public bonds to GDP**|0.00|0.17|-0.14|0.20|0.10|1.00||||
|**Log assets of institutional investors**|0.45|0.50|-0.29|-0.04|0.30|-0.24|1.00|||
|**Log stock market capitalization to GDP**|0.36|0.41|-0.19|0.10|0.20|0.16|0.49|1.00||
|**Log number of listed firms per mln. inhabitants**|0.16|0.21|-0.12|0.10|0.14|0.08|0.02|0.34|1.00|
|**Log stock market turnover**|0.01|-0.05|-0.04|0.06|0.04|-0.15|-0.17|0.12|0.20|

18
countries. In a cross-sectional regression framework, Ram and Zhang (2002) find some evidence that FDI is boosting host economies’ income growth rates, based on data for the 1990s. Yet they note that the results are not robust to all their model specifications. UNCTAD (1999), on the other hand, fails to find a clear linkage between FDI and growth rates, as the sign of the coefficient for FDI is either positive or negative depending on the variables that enter the regressions. In a similar approach, Dutt (1997) also falls short of detecting any empirical linkage between foreign investment and per capita growth rates.

Nonetheless, using a panel of data for the 1970-1999 period for 84 countries, Li and Liu (2005) establish a clear linkage between FDI and growth rates. They confirm this outcome for different econometric techniques, including a simultaneous equation system. In contrast, Carkovic and Levine (2005) also use a panel setting and control for simultaneity bias, but do not find robust results for positive growth effects of FDI inflows in their sample of 72 countries for the 1960-1995 period. They note that this outcome (and the inconclusive evidence in the literature in general) might be due to the specific empirical approaches and the different time periods used.

Apart from data and methodological issues, a few studies have tried to find further reasons for the inconclusive evidence. Based on their results, Balasubramanyam et al. (1996) note that FDI might promote growth only in export-promoting rather than in import-substituting countries and that, thus, openness to trade is essential for the growth effects of foreign investment. Borensztein et al. (1998) find that certain characteristics in the host countries may play an important role. More specifically, they discover that countries need a particular educational attainment level to benefit from FDI. Borensztein et al. argue that to be able to benefit from positive (technological) spillover effects, host economies have to have the educational capacity to incorporate these effects.

More recently, two papers have been published that examined the linkages between the effectiveness and regulations of financial markets, FDI and growth. In essence, Hermes and Lensink (2003) and Alfaro et al. (2004) find that countries with better financial systems and financial market regulations can exploit FDI more efficiently and achieve a higher growth rate. Both studies argue that countries need not only a sound banking system, but also a functioning financial market to allow entrepreneurs to obtain credit to start a new business or 4
# 5.4. Instrumental Variables

Our three international economic activities - imported inputs shares, export shares and FDI shares - can arguably be considered endogenous in Eq. (5), due to the presence of reverse causality. Thus, for example, foreign companies may choose to invest in plants that already exhibit relatively higher shares of skilled workers, and both exports and imports of inputs may be more likely to be pursued by plants whose workers possess higher educational levels, that can be necessary to engage in contacts and negotiations with foreign customers or suppliers. To address this problem, Tables 19-21 present results from estimating Eq. (5) using instrumental variables (IV).<sup>51</sup> In the case of imported inputs, the positive and negative effects that were, respectively, reported for Brazil and China in Section 4.1 are preserved, and the magnitude of the coefficients increases considerably (in absolute value). The speci fi cation tests support the endogeneity assumption for the share of imported inputs and the validity of the corresponding instruments.

In the case of exports, the IV estimates validate the negative effect previously found for China in Section 4.2, but the IV estimates are not signi fi cant for Brazil. In both countries, the instruments for export shares seem to be appropriately correlated with that variable in the fi rst stage regressions and tests for overidentifying restrictions also support their validity. However, the fi rst speci fi cation test reported in Table 20 suggests that in Brazil exports are not endogenous in Eq. (5), so that our previous OLS estimates are preferred on efficiency grounds.

The effects of FDI from IV estimation are still positive in Brazil, but they become negative and signi fi cant in four out of six cases in China. In those four cases, the speci fi cation tests support both the need for instrumental variables and the validity of the instruments adopted,

> 51Detailed lists of the instruments used are shown in the notes to Tables 19-21. They are based on industryregion averages of some of the endogenous variables and, in China, on the fraction of plants that have been restructured into shareholding companies and the average share of government-owned capital. In Brazil, we also use industry-level tariffs, import penetration rates and export shares taken from Muendler (2003a) and Muendler (2003b).

25
absent, and property rights are often disputed, badly registered, and poorly protected. We also keep in mind that a house is not only a roof but should be considered more broadly together with a number of complementarity services and utilities such as water, sewers, and power as well as access to jobs, goods, and schools.

# **i)  Quantity effects: Fostering supply and demand vs. enabling markets**

A key complication with housing policy is the vast array of instruments available to policy makers. More “fundamental” institutional policies seek to enable the housing market to work. More direct policies either provide housing on the supply side or allow residents to consume more housing through subsidies on the demand side. In between, and at an intermediate level, the provision of key infrastructure and utilities or appropriate urban planning will hopefully raise the quality of the housing being supplied.

While a huge housing shortfall cannot disappear overnight, housing stocks should be able to adjust reasonably fast to demographics and income changes, provided property markets are allowed to function. In principle, the residential construction industry should face minimal barriers to entry and be competitive while employment in the sector could potentially grow fast. Given this, the provision of a working institutional framework that allows the expression of supply and demand mechanisms to play their role seems like an attractive proposition.

Institutional reform for the land and housing market is unfortunately harder than it may seem at first sight. Many complementary factors need to be in place at the same time. First, property rights need to be well defined. They are sometimes absent.<sup>50</sup> In some parts of the world, property rights often collide with other rights, such as those given to tenants. For land and housing markets to work, property rights also need to be tradable. This arguably requires a reasonably uniform system of rights. This condition is clearly not met in many countries where land rights are administered by customary chiefs, sometimes in a whimsical manner; many systems of right coexist uneasily; and the trade of property assets is heavily restricted within a group (Durand-Lasserve et al., 2015). Then, land parcels need to be clearly delineated and registered through a system of cadaster and land registry. Such a system can only operate if titles are not contested and the registry is kept up to date. Although this latter condition sounds trivial, keeping land registries up to date is a struggle in most developing countries, including some of the more advanced ones. Finally, even when all those conditions are met, the land market will not work unless titles can be enforced and secured by a fair and timely judiciary system (Glaeser et al., 2016). With only a little exaggeration, one may conclude that well-functioning land and housing markets essentially require a high level of development.

> 50 Among many examples, Kibera, the largest slum of Nairobi (see Henderson et al., 2017).

60
**Table 7: Opportunity Share of Inequality in Educational Achievement**

||**2003**||**2007**||
|---|---|---|---|---|
||**urban/rural**|**urban/rural**|**six regions**|**governorate**|
|Overall inequality|8603.9|10050.1|10050.1|10050.1|
|sd|153.9|180.9|180.9|180.9|
|Inequality of opportunity: level|1858.8|1832.2|2169.5|2431.7|
|sd|67.11|88.23|94.08|89.58|
|Inequality of opportunity: share|0.216|0.182|0.216|0.242|
|sd|0.00529|0.00667|0.00625|0.00571|
|_Higher alternative correction_|||||
|Overall inequality|10958.8|13646.5|13335.6||
|sd|230.3|354.6|423.1||
|Opportunity share|0.289|0.230|.266||
|sd|.006|0.005|.006||
|_Lower alternative correction_|||||
|Overall inequality|8057.6|9956.2|10130.81||
|sd|135.3|187.3|192.9554||
|Opportunity share|0.187|0.163|.213||
|sd|.00685|0.00642|.00717||
|Gender|0.0000446|0.00301|0.00177|0.000207|
|sd|0.000144|0.00114|0.000915|0.000688|
|Father’s education|0.0819|0.0385|0.0373|0.0367|
|sd|0.00484|0.00207|0.00200|0.00197|
|Mother’s education|0.0633|0.0204|0.0190|0.0165|
|sd|0.00318|0.00178|0.00173|0.00174|
|Immigration status|0.0144|0.0329|0.0329|0.0361|
|<br>sd|0.000922|0.00264|0.00246|0.00231|
|Language at home|0.00742|0.0148|0.0149|0.0136|
|sd|0.00110|0.00125|0.00129|0.00138|
|Books at home|0.00633|0.00436|0.00571|0.00554|
|sd|0.00137|0.000944|0.00101|0.000997|
|TV-satelite-phone|0.0312|0.0231|0.0214|0.0214|
|<br>sd|0.00205|0.00345|0.00342|0.00325|
|Urban-rural|0.0114|0.0452||0.0483|
|sd|0.00186|0.00334||0.00310|
|Six regions|||0.0828||
|sd|||0.00594||
|Governorates|0|0|0|0.0636|
|sd|0|0|0|0.00646|

_Source: TIMSS 2003 and 2007 surveys. Sample: TIMSS 2003 and 2007 samples of eight-graders._

# _Significant achievement inequality in national exams_

To investigate how learning opportunities evolve across schooling levels, here we expand the analysis to achievements at national exams at primary, preparatory, and secondary levels. For this use, we use retrospective data from the Survey of Young People of Egypt in 2009 (Asia and Office, 2011). The survey collected detailed information from a sample of 15,029 individuals aged 10 to 21. Notably, it asked information on whether individuals completed different schooling levels, and their scores in the national examinations at completion of the primary, preparatory, and 23
# 3. Sample Construction and Descriptive Statistics

To study the relationship between cross-listing, corporate governance and ownership structure, I create a sample of foreign firms that listed on a major US stock exchange since the year 1990. The complete list of cross-listing firms is obtained from the Bank of New York’s ADR Index. The cross-listing year and type of issue details are also verified using Citibank’s ADR Universe. In the event of a discrepancy between the Bank of New York’s ADR Index and Citibank’s ADR Universe, I use the information that corresponds closest to the list of cross-listing firms obtained from the NYSE and the NASDAQ.

To be included in the final sample, the following criteria are imposed: (1) Since I am interested in testing the effect of US legal rules, I include only Level 2 ADRs and Level 3 ADRs in the sample. Firms that list via Level 1 ADRs (OTC stocks) or Rule 144a are subject to little or no SEC disclosure requirements and do not have to comply with US GAAP and so are excluded from the sample. (2) I use only those cross-listing firms that are covered by Worldscope database. Since most of the firms that cross-list are big companies with high market capitalization, there is little danger of losing out a large sample of the cross-listing firms by using Worldscope. Further, I have access to firm-level data in Worldscope only from the year 1990. So the year 1990 is the initial cut-off date for the sample. (3) Finally, I include only firms for which I can get ownership information from one year prior to cross-listing to one-year after cross-listing. So there is at least three years of ownership information for each cross-listing firm. The final sample extends from 1990 to 2002 and contains 425 firms that cross-listed on a major US stock exchange.

## _3.1. Cross-listing by Year, Exchange and Country_

Panel A of Table I shows the distribution of the sample by year of listing and exchange. More number of firms list on the NYSE, which has the strictest disclosure requirements than on the NASDAQ. Very few firms list on AMEX stock exchange.

12
Table 10: Impact of adding cash to therapy on human capital accumulation at 12 and 24 months.

||(1)|(2)|(3)<br>**Primary **|(4)<br> **outcomes**|(5)|(6)|(7)<br>**Sec**|(8)<br>**ondary outco**|(9)<br>**mes**|
|---|---|---|---|---|---|---|---|---|---|
||Enrolled<br>in school|Compet-<br>encies|Self-<br>efcacy|Pregnant|Married|Risky sex|Time<br>prefs.|Paid work|Desired<br>fert.|
|_Panel A: Midline (12 month_|_follow-up)_|||||||||
|IPT-G+|-0.058|-|-0.306|0.003|0.008|-0.025|0.013|-0.006|0.332|
||(0.028)||(0.249)|(0.015)|(0.013)|(0.023)|(0.034)|(0.017)|(0.061)|
|IPT-G mean|0.404|-|32.125|0.077|0.062|0.269|0.477|0.744|3.512|
|IPT-G SD|0.491||4.974|0.268|0.241|0.444|0.500|0.255|1.223|
|Observations|1043||1042|1024|1037|1045|1052|1045|1041|
|H0: IPT-G+=0 p-values|0.040|-|0.222|0.841|0.541|0.281|0.715|0.713|0.000|
|FDR adj. q-values|0.200||0.468|0.841|0.676|0.468|0.715|0.715|0.000|
|_Panel B: Endline (24 month_|_follow-up)_|||||||||
|IPT-G+|-0.032|0.020|0.660|-0.026|-0.008|0.004|-0.024|-0.035|0.079|
||(0.027)|(0.081)|(0.702)|(0.027)|(0.023)|(0.037)|(0.031)|(0.015)|(0.081)|
|IPT-G mean|0.299|1.414|29.505|0.217|0.181|0.339|0.423|0.813|3.872|
|IPT-G SD|0.458|1.217|8.731|0.413|0.385|0.474|0.495|0.230|1.513|
|Observations|952|838|948|934|947|936|950|949|933|
|H0: IPT-G+=0 p-values|0.238|0.807|0.350|0.346|0.719|0.911|0.440|0.021|0.332|
|FDR adj. q-values|0.701|0.911|0.701|0.701|0.911|0.911|0.440|0.064|0.440|

Notes: This table shows ITT estimates of the impact of adding Cash to IPT-G on the primary and secondary human capital accumulation outcomes, comparing the IPT-G only group to IPT-G+ group; All regression models include randomization block fixed effects centered and fully interacted with treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Columns 1-6 show results for the primary outcomes: whether they are enrolled in school if school is in session or were enrolled in school when school was last in session (or if they have completed secondary schooling), the score on the Competencies test, the score on the Schwarzer and Jerusalem (1995) self-efficacy scale, whether they have been pregnant since baseline, whether they have married since baseline, and risky sex (whether they used a condom at their last intercourse); Columns 7-9 show results for the secondary outcomes: Time preferences (prefers 110,000 Uganda shillings in one month instead of 90,000 today), the self-reported probability of engaging in paid work at age 25, and desired fertility (desired number of children at time of survey); The rows marked as _H0:..._ show the p-value for a t-test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes and round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of CRs.
# **Figure 6-1 Almost all fiscal instruments in the West Bank and Gaza are either inequality-reducing or have no effect**

Marginal contribution to inequality reduction of each fiscal instrument, in Gini points, measured at Consumable Income <!-- Start of picture text --> Inequality reduction<br>VAT (indirect effect)-0.1<br>Fuel excise (indirect effect) -0.0<br>Customs (direct effect) -0.0<br>Fuel subs. (direct effect) -0.0<br>Fuel subs. (total) -0.0<br>Property -0.0<br>Fuel subs. (indirect effect) 0.0<br>PIT 0.0<br>Pension contributions 0.1<br>Pension income 0.1<br>Elec. subs. (direct effect) 0.1<br>Near-cash transfers 0.2<br>Fuel excise (total) 0.4<br>Fuel excise (direct effect) 0.4<br>Primary healthc. 0.4<br>Secondary educ. 0.6<br>VAT (direct + indirect effect) 0.8<br>VAT (direct effect) 0.9<br>Cash Transfer Program (CTP) 1.2<br>Hospital healthc. 1.3<br>Basic educ. 1.4<br>0 .5 1 1.5<br>Marginal contribution at Consumable income (Gini points)<br>Source: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>Notes:<br>[1] Marginal contributions are calculated as the change in a poverty or inequality indicator that results with the inclusion of a tax or transfer of interest.<br>[2] A positive bar represents a reduction in poverty or inequality, while a negative bar represents an increase.<br><!-- End of picture text -->

# **6.1.2 Poverty**

**All transfers are either poverty-reducing or neutral.** The CTP (largest targeted social protection program) and pension income have the largest poverty reduction effects in the West Bank and Gaza, reducing the national poverty headcount by 2.2 and 2.0 percentage points, respectively. While wealthier households capture a larger share of pension income than their share of Market income, a portion of the transfer still goes to poorer households, and the transfer increases income in Decile 1 by 1.6 percent, and by 3.9 and 0.7 percent in deciles 2 and 3 respectively. Electricity subsidies (direct effects), fuel subsidies (direct and indirect effects), and near-cash transfers have a much smaller marginal contribution to poverty reduction at only 0.6 and 0.3 percentage points, respectively.

**All taxes either increase poverty or are neutral. Customs duties and VAT have the largest (negative) contribution to poverty reduction as they increase the national poverty headcount by 5.3 and 4.8 percentage points, respectively.** Although nationally the VAT places a larger tax burden on households relative to customs duties (at 7.3 and 6.3 percent of Market Income, respectively), the VAT increases

43
The only approach we discovered to fairly accurately predict Brazil’s low level of openness is by also controlling for whether or not a country is located in Latin America and the Caribbean by adding a LAC dummy variable in the regression. The LAC factor is a highly significant, large and negative factor: LAC countries are expected to have a 36 percentage points lower trade-to GDP ratio then non-LAC countries, all else equal. This factor reduces Brazil’s predicted openness to 31 percent, fairly close to the observed 28 percent. However, all this tells us is that Brazil is not alone – other Latin American countries also have a low trade penetration relative to the rest of the world (controlling for size and other characteristics). It also hints at other factors common among Latin American countries which reduce trade openness.

**<u>Table 1: Summary of Regression Results</u>**

|expimp|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|lgdp|-2.595|-|9.291***|3.123|2.677|
|larea|-|-10.200***|-10.325***|-11.127***|-11.069***|
|lpop|-|-|-7.999**|-3.273|-4.711|
|urban|-|-|-|0.458*|0.544**|
|manu|-|-|-|0.842|0.941|
|lac|-|-|-|-|-36.324***|
|cons|105.98|214.15|197.03|185.17|189.62|
|R<sup>2</sup>|0.01|0.21|0.26|0.28|0.36|
|Brazil’s|85.9|51.5|61.7|64.5|31.1|
|hypothetical<br>openeness<sup>1</sup>||||||

* significant at 10 percent level

** significant at 5 percent level

*** significant at 1 percent level

1 Trade to GDP ratio estimated for Brazil by applying the coefficients of the regression models 6