rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 10.  The investment equation by firm size and CEO government connections: non-state domestic only**

||<br>**Bureau**<br>|<br>**cratic CEO**<br>|**By size**<br>**s**<br>|**Entrepre**<br>|**neural CE**<br>|**Os**<br>|
|---|---|---|---|---|---|---|
||**Small**|**Medium**|**Large**|**Small**|**Medium**|**Large**|
|ln(GDP per capita)|-0.067|0.076|-0.013|-0.105**|-0.007|0.017|
||(-0.468)|(0.813)|(-0.348)|(-1.996)|(-0.220)|(0.540)|
|ln(city pop)|-0.239|0.084|0.045|-0.001|0.015|0.037|
||(-1.429)|(1.099)|(1.152)|(-0.031)|(0.580)|(1.209)|
|ln(firm age)|-0.107|-0.001|-0.007|-0.096**|-0.037**|-0.019|
||(-1.060)|(-0.021)|(-0.188)|(-2.366)|(-2.249)|(-1.367)|
|Loan|0.304|0.186***|-0.041|0.306***|0.161***|0.059|
||(1.242)|(2.878)|(-0.395)|(4.979)|(6.653)|(1.534)|
|Trade credit|-0.327|-0.068|-0.032|0.265|0.026|0.044|
||(-1.586)|(-0.414)|(-0.356)|(1.606)|(0.340)|(0.688)|
|UCA|0.044|-0.005|-0.035|0.048***|0.050***|0.039***|
||(1.064)|(-0.139)|(-0.800)|(3.794)|(5.572)|(3.171)|
|sales/lagged K|0.002|0.031|0.050***|0.009***|0.010***|0.003|
||(0.261)|(1.420)|(2.757)|(4.225)|(2.635)|(0.721)|
|CF/lagged K|0.092|-0.085|0.076*|0.104***|0.164***|0.280***|
||(0.804)|(-0.732)|(1.896)|(3.269)|(3.953)|(5.643)|
|Lagged sale growth|0.000|-0.052|0.093|0.035*|0.003|0.031***|
||(0.012)|(-1.481)|(1.308)|(1.960)|(0.371)|(2.845)|
|Avg ind Q|0.156|0.206|-0.012|0.360*|0.239**|0.064|
||(0.394)|(0.751)|(-0.061)|(1.813)|(2.142)|(0.779)|
|area dummies|yes|yes|yes|yes|yes|yes|
|Number of observations|113|329|208|2,046|3,683|1,364|
|Adjusted R2|0.023|0.081|0.107|0.080|0.082|0.076|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.

51
# (1) _before tax profits/ta= net margin/ta+ non-interest income/ta_ - _overhead/ta_ - _loan loss provisioning/ta_

The _non-interest income/ta_ accounts for the fact th.at many banks also engage in non-lending activities,' such as investment" banking and brokerage services .. The _overhead/ta_ variable represents the bank<sup>1</sup> s entire overhead, while· _loan loss provisioning/ta_ simply measures actual provisioning for bad debts.

While the underlying data reflects international accounting standards as much as possible, some differences in accounting conventions regarding the valuation of assets, loan lpss provisioning, hidden reserves, etc., no doubt remain.<sup>4</sup> We focus on accounting meastires of income and profitability, as (risk-adjusted) financial returns on bank stocks are equalized by investors in the absence ofprohibitive international investment barriers.<sup>6</sup> Similarly, Gorton and Rosen (1995) and Schranz (1993) focus on accounting measures of profitability when examining managerial entrenchment and bank takeovers.

In section 3 below, we provide information on aggregate income statement items for domestic and foreign banks in 80 countries. Also, we present accounting averages for country groups, by income and geographical location: Such average statistics for different groupings illustrate the differences between domestic and ·foreign banks in developing and developed countries.

: Next, we analyze of the determinants of the interest, profitability, and overhead variables, or _net margin/ta, before tax profits/ta,_ and _overhead/ta._ The regression analysis addresses' what are the ultimate determinants of these three variables and serves to inform us who is affected by these underlying determinants, in an economic sense. The relationship between a bank's net interest margin and its overhead, for instance, tells us to what extent the bank can pass on its overhead cost to its depositors and lenders. The relationship between before tax profits and overhead, in turn, tells us how much of its overhead the bank can pass on to any of its customers (including, say; its brokerage customers).

· The regression analysis starts from the following basic estimating equation: (2) · _lijt_ = _ii0_ + _G:/'FSjt_ + _G:/'iBtt+ &!/}Xjt_ + _Bijt_ I where _Iijt_ = is the independent variable (either _net_ margin/ta~ _before tax profits/ta_ or _overhead/ta)_ for domestic bank _i_ in country _j_ at time _t; FSjt_ is the share of foreign banks in co~try _j_ at time _t; Btt_ are bank variables for bank _i_ at time _t; Xjt_ are country variables for C<,mntry _j_ at .time _t._ Further, _ao_ is a constant, and ~ _G:/'i,_ and ~~ are coefficients, while _f.ijt_ is an error term. All regressions inelude cotlntry and time-specific fixed effects. The set of regressors varies with the dependent variable under consideration.

> 4 See Vittas (1991) for an account of the pitfalls in interpreting international bank operating ratios.

> 5 Also, financial returns data are not available for a similarly large set of banks and countries.

4
# **V.  TRENDS**

# **Is Formalization of Vietnam’s Private Sector on the Rise?**

40. The jump in private sector registrations since implementation of the 1999 Enterprise Law has led to a common assumption that Vietnam’s formal sector has turned a corner and is now on the rise. A closer look at the data on existing Vietnamese private companies reveals a more ambiguous picture.

- The sector’s share of GDP has remained constant

- The number of firms has grown, though more modestly than indicated by registrations

- Employment has grown dramatically, particularly, it seems, in the past year

41. The domestic formal private sector’s 7.6 percent share of Vietnam’s GDP at the end of 2000 was the same as five years earlier at the end of 1995. The contribution of Vietnam’s state enterprises has also remained unchanged, with the only significant development being a fall in the _in_ formal private sector’s share in favor of growth among foreign-invested companies. As such, there has indeed been an increase in the GDP share of the total formal private sector, i.e. the combination of the foreign and domestic formal private sectors. It is, however, probably too early to see the GDP effects of private sector growth under the Enterprise Law as these firms were mostly new firms registering throughout the year, many of which probably began with investments but not immediate outputs. Registered capital, one of the few available indicators of private sector investment, has indeed been on the rise since 1997.<sup>27</sup>

**Table 5.1 GDP by Enterprise Form, 1996 and 2000 (%)**

||**Shares**||**Growth**|
|---|---|---|---|
||**1996**|**2000**|**Average YoY**|
|State Sector|40.8|40.6|7.3|
|Non-state Sector|59.2|59.4|6.7|
|- Domestic non-state|42.7|40.2|5.1|
|- Households & Farmers|35.0|32.6|4.9|
|**_- Private Companies_**|**_7.6_**|**_7.6_**|**_7.1_**|
|- Sole Proprietorship|3.2|3.3|8.5|
|- Ltds. and|4.4|4.3|6.1|
|Shareholding<br>- Collectives|9.2|8.5|4.1|
|- Foreign Invested|7.3|10.7|17.4|
|**Total**|**100.0**|**100.0**|**7.0**|

Source: GSO, 2001 42. Developments in manufacturing have been similar, with even greater growth among foreign firms and, consequently, even greater growth in the collective formal private sector. The slight fall in the domestic formal private sector’s share is somewhat deceptive, as it varies depending on the way in which GDP is measured.<sup>28</sup>

> 27 Steer, L. (2001). _The Private Sector in Vietnam._ p. 9.

> 28 The domestic formal private sector’s share of GDP measured in nominal terms actually rose over the given period. A possible explanation for this is the private sector’s significantly higher export-orientation (as implied by the 1999

21
# **Figure 2. Evolution of Education Premiums for Completed Primary, Secondary and University Education in Latin America (Source: Aedo and Walker 2011)**

Basic OLS estimates of Mincer-style regressions <!-- Start of picture text -->
1.40 1.40<br>Brazil Chile<br>1.20 1.20<br>1.00 1.00<br>0.80<br>0.80<br>0.60<br>0.60<br>0.40<br>0.40<br>0.20<br>0.20<br>0.00<br>0.00<br>1990 1992 1994 1996 1998 2000 2003 2006<br>University Secondary Primary University Secondary Primary<br>1.40 1.40<br>Colombia Costa Rica<br>1.20 1.20<br>1.00 1.00<br>0.80 0.80<br>0.60 0.60<br>0.40 0.40<br>0.20 0.20<br>0.00 0.00<br>1997 2003 2008 1998 2000 2002 2004 2006 2008<br>University Secondary Primary University Secondary Primary<br>1.40 1.40<br>El Salvador Mexico<br>1.20 1.20<br>1.00 1.00<br>0.80 0.80<br>0.60 0.60<br>0.40 0.40<br>0.20 0.20<br>0.00 0.00<br>1998 2001 2003 2006 2008 1992 1994 1996 1998 2000 2002 2004 2005 2006 2008<br>University Secondary Primary University Secondary Primary<br><!-- End of picture text -->

**_Figure 2 Notes_** _:_ The education earnings premium for primary is the difference in the logarithm of monthly earnings between complete primary and incomplete primary education, the education earnings premium for secondary is the difference in the logarithm of monthly earnings between complete secondary academic and complete primary education, and the education earnings premium for university is the difference in the logarithm of monthly earnings between complete university and

28
follow-up. Second, it shows (B) the change in the rich-poor gap between baseline and follow-up for children in comparison villages. Here we observe that with one exception (card-sorting) on all dimensions the rich-poor gap increased between baseline and follow-up. Third, it shows (A) – (B) the difference between the change in the rich-poor gap for children in batch 3 villages and the change in the rich-poor gap for children in comparison villages. (A) is represented by the estimated δ 5<sup>+δ</sup> 7<sup>, (B) is represented by the estimatedδ</sup> 5<sup>, and (A) – (B) is represented by the</sup> estimated δ 7<sup>. Thus, assuming that the change in the rich-poor gap between baseline and follow-</sup> up in comparison villages is a valid counterfactual for the change that could have happened in batch 3 if there had been no ECED project, the reduction in the gap between rich and poor children as a result of the ECED project is (A) – (B).

In the absence of the ECED project, the rich-poor gap in child outcomes widens substantially in comparison villages as shown in (B) of Figure 11. For example, the rich-poor gap in the language and cognitive development domain of the EDI and in a child’s ability to draw a house increases by about 0.24 and 0.30 standard deviations, respectively. On the other hand, most of reported rich-poor gaps narrow in batch 3 as shown in (A). Specifically, we find decreases in the rich-poor gap in batch 3 in social competence by 0.246 standard deviations, in the communication and general knowledge domain of the EDI by 0.228 standard deviations, in pro-social behavior by 0.216 standard deviations, and in the test of executive function (i.e. the Dimensional Change Card Sort game) by 8 percentage points.

The causal reduction in the gap between rich and poor children as a result of the ECED project is presented in (A) – (B) of Figure 11. In the case of social competence, the gap closes by 0.3 standard deviations, in the language and cognitive development domain of the EDI the gap closes by 0.185 standard deviations, in the communication and general knowledge domain of the EDI, the gaps closes by 0.298 standard deviations, and in the case of pro-social behavior it closes by 0.377 standard deviations. Thus, our findings imply that the greater progress for poor children as a result of the ECED project in batch 3 leads to a reduction in the gap between rich and poor children.

19
areas simultaneously, to<sup>the extent of 50% damage, the loss works</sup> out to be Rs. 89<sup>crores ($53 m). This gives an exRected</sup> catastrophic crop loss of<sup>almost Rs. 13 crores ($7.8 m). Under the</sup> same assumptions as made<sup>for the continuing crop losses, this would</sup> raise the economic cost<sup>of cultivation in Guntur above its private</sup> cost by almost 10%.

# VIII. PESTICIDE OVERUSE AND<sup>YIELD LOSS WITHIN COTTON</sup>

The preceding section has<sup>indicated that the external costs</sup> associated with pesticide<sup>overuse can be very widespread and</sup> extremely large. Within<sup>the framework of the theoretical model</sup> sketched out in Section III,<sup>we had emphasised the adoption of the</sup> IPM approach to address<sup>the problem. In addition to limiting the</sup> externality problems, adoption<sup>of IPM technicues can lead to</sup> efficiency aains within cotton<sup>cultivation itself. Thus, in this</sup> section we will attempt to<sup>quantify the inefficiencies existing in</sup> the present cotton cultivation<sup>practices and the advantages to be</sup> reaped by adopting a package<sup>of IPM practices as well as the</sup> possible savings in externality<sup>costs.</sup> The analysis is based on<sup>data supplied by the Pyrethroids</sup> Efficacy Group (PEG) India, which<sup>has been managing trial plots</sup> since 1989/907 and collecting<sup>statistics on costs of cultivation,</sup> 7 Indian scientists have<sup>been cognizant of the value of IPM</sup> techniques in increasing<sup>private profitability and in reducing</sup> environmental damages since<sup>at least the early 70s, when a UN</sup> supported IPM program was launched<sup>in cotton and rice. However, it</sup> appears that the idea of IPM did<sup>not gain popularity and the Indian</sup> 64
# 4.3 The 1994 Aluminum Memorandum of Understanding

The aluminum industry has never been the subject of international control, and indeed it was not on UNCTAD's list of core commodities. This reflects the fact that it is predominantly a developed country commodity, because although bauxite is mined in developing countries, the value added in the industry is largely at the smelting stage and this tends to be located principally where relatively inexpensive electricity (generally hydroelectricity) is available. Until the end of the nineteen seventies, the aluminum industry was effectively under the control of the major producing companies who set relatively stable producer prices, so that there is a history of something approaching regulation.

The structure of the industrv has changed dramatically over the past decade, in part because of a decrease in concentradion andi in part because of the successful introduction of futures trading (in 1978) on the LME - see Haskel and Powell (1994). The refined aluminum price is now as variable as the prices of other traded commodities (Slade, 1991). Of even greater concern to the industry, however, was the fact that prices fell to very low levels over the five year period 1989-93. Initially, this was the effect of industrial recession, and the fall in price was of the sam.e ordier as that in other non-ferrous metals. However, from 1991 these already low prices were exacerbated by a massive increase in exports of refined aluminum from the ex-Soviet Union, largely the Russian Federation. Reported figures indicate a jump in exports from the former Soviet Union from 274,000 tons in 1990 to 1,521,000 tons in 1993.22 The 1993 is equivalent to 10% of "western world"<sup>23</sup> consumption of aluminum. The rise in Russian exports arose from the coincidence of the collapse of demand from traditional (largely defence oriented) users of aluminum in the former Soviet Union in conjunction with relatively low 22 World Bureau of Metal Statistics: _World Metal Statistics Yearbook 1994._ Actual exports in 1993 may have been significantly in excess of this reported figure. Tons are metric tons. Nickel also experienced a large rise in exports from the ex-Soviet Union.

**23** i.e. excluding ex-COMECON countries and China, for which historical data are unreliable.

33
was observed in Bangladesh for the Helen Keller sample, which include both urban and rural children, where the disparity was 25 percentage points. In the IFPRI sample which only includes rural children, there was no difference between the two groups. In Bolivia, Cambodia, Nepal and Zimbabwe, the difference between the two groups was between seven and four percentage points. These findings are consistent with the fundamental role of nutrition‐related behaviors and caring practices and the critical need for nutrition knowledge across all income groups. In Ethiopia, Indonesia and Peru both resource‐rich and resource‐poor children had similar access to adequate food as the differences were only one percentage point.

<!-- Start of picture text --> Figure 5c: Adequate Food Security by Household Wealth<br>Prevalence of Adequacy of Food<br>By wealth<br>67 68<br>53<br>47 46 47<br>38 37<br>34 34<br>32<br>29<br>25 25<br>18<br>13 12 13<br>BGD (HK, '10) BOL ('08) IDN ('10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, 11) ETH ('11) KHM ('10) PER ('12)<br>Resource Poor Resource Rich<br>Source: Author estimates.<br>Note: (1) Bangladesh (HK) include food security but not information on minimum acceptable diet.<br>(2) Indonesia (RKD) only has information on household level diatery diversity, nothing else.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

# **3.2.2 Adequate Care Practices**

More than 80% of the children under age of 24 months were breastfed in the countries studied, but access to the other three components of adequate care was mixed.<sup>**14**</sup> In Zimbabwe only 30% of the children under the age of six months were exclusively breast‐fed and only in Cambodia and Peru were more than three‐fourths of the children under six months exclusively breast‐fed (Figure 6a). Early initiation of breastfeeding ranged from 45% of children in Nepal to 78% of children in Bolivia.

> 14 Since we used children under 24 or 36 months of age, this does not imply that more than four‐fifths of the children received milk until they were 24 months old.

25
# **7. Conclusions**

Using different types of bilateral data that reflect international financial transactions (cross-border portfolio investments, syndicated bank loans, M&As, and greenfield investments), this paper offers a comprehensive and systematic account of how economies in the EAP region have integrated with the rest of the world since the 1990s. The paper shows, first, that EAP is increasingly more connected internationally, both with itself and with the rest of the world. Second, despite being relatively less important, EAP’s integration with the South has grown faster than that with the North. Third, EAP’s connections with the North involve more arm’s length financing than those with itself and the South, where FDI matters more. Fourth, there is significant heterogeneity in the financial connections of EAP. The more developed EAP economies integrate in a way that is similar to that of the North (with a larger role in EAP’s arm’s length financing), whereas less developed EAP economies integrate more similarly to South economies (with a larger participation in EAP’s FDI financing).

The broad set of stylized facts presented in this paper leads to some conclusions and conjectures that could be of relevance for researchers and policy makers interested in the EAP region and in cross-border financial integration patterns more broadly. Here we highlight a few.

The findings we uncover, related to the different patterns of integration with the North and the South and across different economies in EAP, are consistent with arguments in the literature that state that financial development has an important role in international financial transactions and in how EAP has engaged financially across borders. That is, whereas other factors are surely at play, the sophistication of financial markets as economies become richer might well explain the patterns we find. Two aspects of financial development can matter. One is the availability of financial instruments that allow savers to invest and lowers the costs and risks of those investments. This might prompt EAP economies (in particular the less developed ones) to invest in securities or conduct transactions with banks in the North. The other one is the availability of more sophisticated financial institutions 22
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