rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> CHART M3 CHART M4<br>GROWTH RATE OF REAL LOANS BANKS' EXCESS RESERVES (in million RM)<br>AND THEIR RATIO TO DEPOSITS<br>_ 0.10,o,o 0 1 \ 9 000<br>|5 /-4.='s N 78 , ' # lvj;<XR''\~~~~~~~~~~/ 1000<br>075 en 9 )0 02 9 0 03 04 909)0 90370 7 9090 90)0 0 700 99 010D} 20 oJo O2 9 03 01 od 0 0 970 2000 gOdO 99)1 90)0 90 70 3 970 7 9 SOg09 711 goo 91 00<br><!-- End of picture text -->

CHART M5 EVOLUTION OF SOME KEY INTEREST RATES <!-- Start of picture text --> Lendeog rate by Comm, B0fnk0.- Govern. Secunties (0 year)<br>--- Lending rat by Merch Banks_ T- -bill (3-month)<br><!-- End of picture text --> Chart M6 and Chart M7 underscore that there has been a marked increase in both general risk premium and bank dependent borrowers' specific spread -- evidence supporting balance sheet and lending channels. The increase is more prominent in the case of customers of merchant banks, compared to those of commercial banks. Due to the lack of data on corporate bond yields (and commercial paper rates) it is impossible for us to decompose the wedge between lending rate and risk-free yield into GRP and BDBSS.

25
# **Table 5(5): Robustness checks, a falsification exercise**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Full|Diff|Non-diff|Full|Diff|Non-diff|
|(t-r)|-0.408|-0.650|-0.304|-0.507|-0.240|-1.166|
||(0.559)|(0.854)|(1.242)|(0.673)|(0.920)|(1.063)|
|collpriv_sh_prcs|-12.174***|-14.619**|-10.795***|-13.149***|-12.747*|-12.854***|
||(3.243)|(6.621)|(3.997)|(3.608)|(6.947)|(4.458)|
|for_sh_prcs|-54.529***|-48.573***|-56.795***|-55.151***|-49.360***|-58.362***|
||(1.827)|(4.312)|(2.121)|(2.106)|(4.613)|(2.451)|
|primary||||-8.265||-8.894|
|||||(8.994)||(23.015)|
|resource||||-8.458|-8.250|-9.084|
|||||(8.997)|(11.283)|(23.016)|
|advanced-tech||||-11.802|-35.417*|-10.619|
|||||(12.407)|(19.636)|(15.718)|
|Ad-R&D-Sales||||23.051***|31.169***|35.719***|
|||||(7.978)|(11.587)|(13.472)|
|sigma||||0.002|0.015|-0.011|
|||||(0.031)|(0.039)|(0.076)|
|HS4 fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3,590|861|2,548|2,853|691|2,052|
|R-squared|0.592|0.643|0.579|0.596|0.619|0.595|

Notes: The regressions are at HS8 level, covering only processing trade with supplied materials (prcs). The dependent variable is the share of indirect exports (conservative measure), ranging from 0 to 100. The export shares of prcs by trade regime and firm ownership type variables range from 0 to 1. The omitted trade regime is normal trade. The omitted firm ownership type is SOE. “Diff” refers to differentiated products according to Rauch’s classification. “Non-diff” products include both homogenous and referenced products. *** p<0.01, ** p<0.05, * p<0.1.

41
_-9-_ 22. The relktive magnitudes of NPCk and **'s"** justify the application of the CEPR formul& of para.14. Table 2 below presents both the resulting CEPRs and the actual Effective Protection Rates (EPR), as well as the NEPRs. These results indicate large inter-sectoral variations in EPRs and NEPRs. Subsectoral CEPRs (adequately weighted by VAw) range between 15% for electronics to 30-40% for most other subsectors and up to 45% in synthetic yarns. Furthermore, though the overall average NEPR Is null (within an error itgin of + 10%), the large inter-sectoral variations of NEPRs indicate unequal incentives from one subsector to the other. The only subsector where the actual EPR is close to CEPR is Heavy Chemicals. Positive NEPRs are significant (25%) in Basic Steel Products and Synthetic Yarns, and substantial (47%) in Heavy Miscellaneous industries (paper, tyras). Nezative NEPRs are tiodest in electronics (7%), significant in food industries (22%), substantial in light chemicals (34%) and large in engineering industries (52%).

TABLE 2: CORRECTED AND ACTUAL EFFECTIVE PROTECTION BY SUBSECTOR/a

|Subsector|CEPR (X)|Actual<br>EPR (%)|<br>NEPR(%)|Distributn(%)<br>of Projects<br>with +/- NEPR|
|---|---|---|---|---|
|Heavy Chemicals|28|37|9|60/40|
|Light Chemicals|28|-6|-34|0/100|
|Synthetic Yarns|44|70|26|50/50|
|Basic Steel Products|37|60|23|57/43|
|Electronics|14|7|-7|60/40|
|Other Engineering|40|-12|-52|0/100|
|Food Industries|30|8|-22|50/50|
|Ileavy<br>Miscellaneous|30|77|47|80/20|
|Overall|30|30|0|42/58|

/a CEPRs, NEPRs and Actual EPRs in this table are averages weighted by VAw.

Source: Annex 3 23. Indian policy-makers have traditionally tried to adjust nominal protection levels to their perceived need of each industry, or product-group, through a multiplicity of ad-hoc tariffs and exemptions. The large intersectoral variations in NEPR reflect the ad-hocism and inadequacy of such protection policies. This is further illustrated by the intra-sectoral discrepancies within each subsector. Only in light chemicals and engineering are all the projects subject to the same incentive (negative NEPR), as indicated by the last column of Table 2. In practically every other subsector, the projects are distributed in approximately equal shares between those benefitting from a positive NEPR and those subject to a negative NEPR, which can be construed to be the result of a quasi-random prccess. The structure of Effective Protection in India provides thus very heterogeneous incentives.

24. The use of the Corrected Nominal Protection rates (CNPRs, cf. para.15) permits to estimate the respective impacts of capital costs and input costs on
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reflected in a decline in the stock of domestic assets with the RBI. The extent of sterilization increased so much in wake of large capital inflows that by end 2003 the RBI had completely exhausted its stock of domestic assets. The government had to create new “market stabilization bonds” in March, 2004 that could be sold by the RBI (Joshi and Sanyal, 2004). Despite being significant, the sterilization was not 100 percent. Increase in reserves was associated with an increase in reserve money supply, Figure 13. In order to look at the unsterilized part of the reserve intervention we regress log reserve money on nominal GDP—since the demand for money is closely related to nominal economic growth. The excessive money supply is then attributed to the unsterilized reserve intervention by the RBI, Figure 14.

**Figure 13: Increase in Reserves in early 2000s was partially sterilized as reflected in a decline in net domestic assets**

<!-- Start of picture text --> Change in Net Foreign Assets Change in Net Domestic Assets<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>-1000<br>bn Rs<br>1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14<br><!-- End of picture text --> 17
In terms of missing or incomplete data, the US data set is complete, with the exception of information on domestic and foreign firms involved in the investigations – that data is only collected through investigation USA-AD-1058 (cases initiated through October 2003). More recent data on firms can be updated from the _Federal Register._

# **3.14 Venezuela (VEN)**

Venezuela has a single track antidumping investigation procedure administered by the Comisión Antidumping y Sobre Subsidios (CASS, Anti-dumping and Subsidy Commission), which is assisted by the Secretaría Técnica (ST). CASS issues the rulings, while the ST helps with administration and investigation duties. The Venezuelan system was instituted in the early 1990s. The CASS website, <u>http://www.cass.com.ve/ contains useful information on the dumping legislation and on the cases</u> themselves. However, the Venezuelan internet infrastructure is relatively unreliable, so we found it useful to archive pages offline for extended use. This site provided the information for this data set.

In addition to the standard data described in section 2, the file for Venezuela contains additional data described in the table below.

**Table 3.14.1 : Additional Venezuelan Data Included in AD-VEN-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-V**|**EN-Master :**||
|**U.**|**TERM_DATE**|Date of the termination action|
|**V.**|**P_AD_DUTY**|Preliminary antidumping duty imposed|
|**W.**|**F_AD_DUTY**|Final antidumping duty imposed|
|**X.**|**INIT_GP**|Decision number of the initiation action|
|**Y.**|**P_GP**|Decision number of the preliminary dumping/injury determination|
|**Z.**|**F_GP**|Decision number of the final dumping/injury determination|
|**AA.**|**TERM_GP**|Decision number of the termination action|
|**AB.**|**REVOKE_GP**|Decision number of the revocation action|
|**AC.**|**CASE_WORKING_ID**|Number on the folders used to organize the case files|
|**AD.**|**NOTES**|Comments|
|**In AD-V**|**EN-Products :**||
|**D.**|**NOTES**|Comments|
|**In AD-V**|**EN-Foreign-Firms :**||
|**D.**|**F_AD_MARGIN_FIRM**|Final dumping margin for a specific firm|
|**E.**|**NOTES**|Comments|

34
- 10 -

The transport cost to market variable ( _TCMK_ ) is considered to be a more-accurate measurement than the above, representing connectivity with the road condition taken into account. Given the georeferenced road condition data, transport costs to bring one unit of a good to a major market are calculated using spatial software. In principle, if road conditions are bad, road user costs tend to be high and travel speed low, which increases time costs. From each location, the optimal route is selected to minimize total transport cost to reach a city with a population of more than 15,000. Similarly, transport costs to Freeport of Monrovia are calculated.

A more-general market accessibility measurement can be calculated. The above transport cost variables are still less general, because they reflect connectivity to a particular destination. In reality, people and firms need to be connected to different locations or potentially all cities and markets. The level of the importance of each market may depend on proximity to it. Thus, with the size of market capacity and distance to the market taken into account, the following simple market access index is considered, based on a conventional gravity framework:<sup>3</sup> This is the sum of purchasing power or market capacity, _Y_ , inversely weighted by the degree of impediment between two locations (e.g., distance, _d_ ). For _Y_ , city population is used as a proxy for market capacity; _d_ is measured according to transport costs from district _j_ and city _k_ .

To estimate Equation (2) in the logit framework, a critical empirical concern is that it assumes the independence of irrelevant alternatives, which requires that preferences between any pair of two choices be independent of the third option (e.g., Bhat, Paleti, and Singh 2014). In our context, this means that the relative attractiveness of one location ( _A_ ) over

> 3 The literature also uses negative exponential weights. See, e.g., Elbadawi, Mengistae, and Zeufack 2006; Lall and Mengistae 2005.
§ Figure **1.** _Adjussm't_<sup>_(ndwors for CamernOA_</sup> Temaso4zratlek1973<sup>_JIOU)_</sup> <!-- Start of picture text --> 130 .<br>1320IS<br>Ito~~~~~~~~~~~<br>I. 0<br>190<br>s o<br>2* <<br>60<br>1P 1'.771 197h 19979 952<br>Noe: WoleJ mnJx of pr.s, ul esponred uommuIt c to ImpujeteJ Ctttmanoie.<br><!-- End of picture text --> <!-- Start of picture text --> ulto ol proJucer to wotid pnce lpercent)<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>Ss<br>so<br>Jo _6<br>40<br>20<br>I2012<br>t970 197L 1974 1970 1975 39 1961 3<br>dnggt For coibe ad Coca Pt = dOfftJ4CC pfoi*e at. P, -wofi peace. ER UCDJJ<br>eahap<br><!-- End of picture text --> ~~~~~~~~~~~~~~~~~~Figure I _(co0..4xwd)_ R eaIxd .rame (1973<sup>-300)</sup> <!-- Start of picture text --> 170£<br>130<br>310<br>*X 'S.<br>l<br>1970 1972 1974 1976 1978 1930 1912 1964<br>P-In.ct AdAt I pce of muttrAzdables ot<br>Pnrace of craaUbes etzmudmn auniu) I prce ot now,t bks<br>- ManulaitugA coiuipeuuvs Um ullDOft-*@gjMed : a_u wt wlkAl pice iVs a<br>tradtag paruu I doeac iasufacnTww wna is icmic a hs Vi cmuwfuvcaul<br>200m dmn,pwm 193 10<br>-<br>1954<br>atl|*&,*<br>397 197 397If4 *97* I3975 $ 198 312 3954<br>Note: CaV uqG at om 1973 bawe al<br><!-- End of picture text -->

. w..- -4wz*"NO4MIt NIVlt*,<sup>VOZ[.</sup> 1, %0-
- 61 -

# 84 Sources of Prjiect Fi[ancl 1

14. Project funds came largely from IBRD/IDA loans and government contributions. Very little reliance **was** placed on fees from users as shown below:

|SOURCES|AVERAGE<br>RANGE<br>(% of Project Cost)|COHzES|
|---|---|---|
|IBRD/IDA|56.9<br>26-95|95% in Burkina|
|Other External Loans|7.3<br>0.36|0 in 8 cases|
|Government Budgetary|||
|Contributions|34.6<br>5-74|74% in China|
|User Fees &Other|1.2<br>0-13|0 in 10 cases|

# C. THE MAJOR GAPS

15. Considerable research has been done in the Bank with regard to pricing of health services. Of foremost **inportange are** the studies by de Ferranti; Jimenez; and Birdsall which **emphasized a** methodology for selective pricing of public sector health services and identified certain complementary measures **(e.g.,** risk-sharing arrangements) that **can** assist in institutionalizing recoimended pricing reforms.

16. While efficiency pricing sr&._is generally infeasible in health because of the problems of measuring externalities; as in **the** education sector, there are important X-efficiency, fiscal, and even distributional benefits to be obtained through a systematic analysis of marginal costs and selective application of user **fees.** Examples exist in the private health sector in some developing countries and in the public sector in China. A number of the projects reviewed contain funds for costing or pricing studies, through which a good empirical basis should be gained. Recent sector work in countries such as Zambia, Uganda and **Zimbabwe** has focused on cost recovery issues. However, our SAR review shows that disaggregated cost analysis is still the exception rather than the rule in Bank projects. 17. A useful next step would be to conbine the results of recent research with **the** country analyses to produce a practical guideline on how to categorize marginal costs and how such costs compare across countries. The X-efficiency, fiscal and distributional consequences of various levels and structures of' uer charges should be explored at an empirical level. The role and cost of the private sector in providing certain kinds of services should be compared across countries. Finally, ongoing health care costing studies should be closely monitored and used to produce practical recomendations for user charges that can be incorporated into future Bank operations in those countries.
**<u>Table 1: Descriptive evidence on credit access</u>**

||**Total**|1|2|**By w**|**ealth qu**<br>3|**artile**|4||
|---|---|---|---|---|---|---|---|---|
|**Formal sector**|||||||||
|Applied for a loan|0.03|0.01|0.01||0.02||0.06||
|Amount applied for, in USD|606|87|96||192|*|851||
|Loan fully approved|0.93|0.86|1.00||1.00||0.90||
|Loan partially approved|0.02|0.14|0.00||0.00||0.02||
|Amount received, in USD|422|83|96||200|*|501||
|Monthly interest rate|0.02|0.01|0.03|*|0.02||0.02||
|Loan length, months|12|9|7||9||14|*|
|No guarantee|0.12|0.29|0.11||0.16||0.07||
|Land as a collateral|0.50|0.71|0.78||0.58||0.42||
|_Loan purpose_|||||||||
|Investment|0.47|0.50|0.78||0.47||0.42||
|Consumption|0.27|0.00|0.22||0.26||0.29||
|Heath|0.09|0.29|0.00|*|0.11||0.06||
|Education|0.10|0.14|0.00||0.16||0.11||
|**Semi-formal sector**|||||||||
|Applied for a loan|0.20|0.17|0.22|***|0.23||0.21||
|Amount applied for, in USD|55|28|28||57|***|102|***|
|Loan fully approved|0.98|1.00|0.98||0.98||0.96||
|Loan partially approved|0.01|0.00|0.01||0.02||0.01||
|Amount received, in USD|51|27|26||54|***|93|***|
|Monthly interest rate|0.06|0.06|0.07||0.05|*|0.05||
|Loan length, months|5|5|5||5||5||
|No guarantee|0.62|0.67|0.66||0.62||0.51|**|
|Land as a collateral|0.31|0.31|0.29||0.29||0.37|*|
|_Loan purpose_|||||||||
|Investment|0.37|0.29|0.36||0.44||0.41||
|Consumption|0.33|0.38|0.37||0.31||0.27||
|Heath|0.14|0.18|0.16||0.14||0.09||
|Education|0.12|0.10|0.09||0.08||0.20|***|
|**Informal sector**|||||||||
|Applied for a loan|0.21|0.20|0.20||0.23|*|0.19|*|
|Amount applied for, in USD|34|13|24|***|33|**|68|***|
|Loan fully approved|0.99|0.98|0.99||1.00||1.00||
|Loan partially approved|0.00|0.00|0.00||0.01||0.00||
|Amount received, in USD|34|14|24|***|33|**|69|***|
|Monthly interest rate|0.01|0.01|0.01||0.01||0.02||
|Loan length, months|4|4|4||4||4||
|No guarantee|0.97|0.98|0.95||0.96||0.96||
|Land as a collateral|0.03|0.02|0.03||0.04||0.02||
|_Loan purpose_|||||||||
|Investment|0.25|0.14|0.23|**|0.30||0.31||
|Consumption|0.35|0.40|0.37||0.30||0.30||
|Heath|0.25|0.32|0.27||0.24||0.20||
|Education|0.12|0.12|0.10||0.11||0.16||
|**Total Observations**|3294|823|824||823||824||

**Source:** Own computation from WB survey 2011. **Note:** The wealth quartiles are calculated using a principal component analysis based on housing conditions, assets, livestock and land ownership. T-test is performed using the immediate lower quartile as a reference: * significant at 10%; ** significant at 5%; *** significant at 1%

18
**TABLE 2: Summary Statistics of the regulatory Governance Database**

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Formal Autonomy|40|0.780|0.120|0.386|0.939|
|Informal Autonomy|40|0.800|0.115|0.400|0.950|
|Formal Transparency|40|0.755|0.142|0.500|1.000|
|Informal Transparency|40|0.652|0.158|0.200|0.909|
|Informal accountability|40|0.817|0.153|0.500|1.000|
|Formal accountability|40|0.522|0.197|0.100|0.950|
|Tools/Capacities|40|0.623|0.171|0.315|0.936|
|ERGI|40|0.707|0.105|0.474|0.889|

Source: Authors calculations

# **6. RESULTS**

This section describes the results with different specifications. As stated above, all the specifications were run using a semi-logarithmic functional form of these models for each of the indicators. It should be remembered that the percentage impact in each indicator is given by _e_ δ − 1 when interpreting the coefficient estimates of the dummy.

First, we describe the results when only the change-in-ownership variables are included. Then we included a dummy for the existence of a regulatory agency as well as it interactions with the ownership dummies. After this, we included a quadratic form of experience of the regulatory agency. Following this, we introduced the ERGI in the specifications as well as its interactions with the ownership. Finally, we decomposed the regulatory index through a Principal Component approach and obtained three principal components that are introduced in the models.

# **6.1. Change in Ownership**

We first explored the impact on performance of private sector participation. We do this because, together with the introduction of regulatory agencies, private participation was the main change in the sector. As described in Andres _et al._ (2007a) and World Bank (2007), by 1993 only 3 percent of the total connections were in private hands. In contrast, by 2005, 61 percent of them were under private management.

Table 3 presents the results. Consistently with Andres _et al._ (2006), private sector participation exhibits significant effects in labor productivity, reduction of distributional losses, improvement in the quality of the service, reduction of the operational expenditures, increment in tariffs, and improvement in the cost recovery ratio. However, most of these changes occurred during the transition period. Despite this, some additional improvements happened after this period. Not surprisingly, coverage of the service, when the estimation accounts for utility specific time trends, presents no significant change.

- 14 -
Table 6: Uncertainty statistics across simulations

|||Covera|ge rate|||Width|of CI||
|---|---|---|---|---|---|---|---|---|
||EBP|Cubist|XGB|BRF|EBP|Cubist|XGB|BRF|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.928|0.730|0.917|0.899|1.331|0.811|0.951|1.552|
|Madagascar|0.691|0.953|0.947|0.974|0.799|1.453|1.301|1.596|
|Mozambique|0.863|0.711|0.730|0.756|1.778|0.858|0.828|1.414|
|Sri Lanka|0.684|0.948|0.923|0.954|0.731|1.017|0.923|1.109|
|Vietnam|0.913|0.904|0.873|0.915|0.667|0.566|0.521|0.834|
|Malawi|0.391|0.699|0.735|0.710|0.701|0.912|0.941|0.985|
|Tanzania|0.973|0.982|0.976|0.983|1.444|1.645|1.340|1.809|
|Average|0.777|0.847|0.872|0.884|1.064|1.037|0.972|1.329|
|**Panel B: Poverty**|||||||||
|Malawi|0.743|0.778|0.862|0.855|0.477|0.395|0.492|0.614|
|Tanzania|0.874|0.868|0.837|0.920|0.311|0.283|0.273|0.426|
|Average|0.809|0.823|0.850|0.888|0.394|0.339|0.382|0.520|

Note: The first four columns present the coverage rate across simulations for each country. The coverage rate is defined as the proportion of confidence intervals that contain the true value, derived from the census. The last four columns present the average width of the confidence interval across simulations for each country. The results are based on 100 simulations for each country and method.
4 developing countries tha the do imports from oilier industrial countries. Approximately 18 percent of developing countries' non-oil exports encounter NTMs, while the corresponding share for OECD intratrade is about 10 percent. The statistics for several product groups reveal an even greater difference in developing-industrial country coverage ratios. Between 52 and 64 percent of developing countries' textiles and clothing exports face restrictions as compared to under 10 percent of OECD exports of these goodIS.4 Nontariff measure coverage ratios for developing countries' exports of leather and footwear are 17 to 27 points higher tha shipments of these products from industrial countries. Voluntary export restraints and special imprt authorization procedures largely account for these coverage differences. Table 1 shows that coverage ratios are not always higher for developing countries. In food items, for example, the coverage ratio for industria countries is 28 percent, compared to 1 8 percent for developing countries. This is largely explained by the fact that tropical products, like coffee, tea and cocoa, accounting for approximately 15 percent of developing countries' food exports, face relatively fe-w OECD nontariff measures. Most industrial country NTMs are applied to temperate zone food products (particularly grains and dairy products), which are mainly exported by other OECD countries.

Table 2 provides another perspective on nontariff protecton facing developing countries by showing trade coverage ratios for different types of restrictions and by the product gro-ups to which they are applied. Within foods and feeds, variable import levies are the most commonly applied restrictions, accounting for 52 percent of all NTM-covered trade in the group, followed by quantitative restrictions _'Estimates_ of the restrictiv **e** ffect of NTMs in these sectors show the barrers are indeed formidable. The USlTC (1989) estimaed that the ad valorem equivalents ofexisting US NTMs on 54 broad categories of textile and clothing products ranged between15 to over 100percent. Laird and Yeats (1990) found that estimates for nominal equivalents of NTMs on grains, sugar, dairy, vegetable oils, poultry, pork oilseeds and nuts imported into fth EU and Japan ranged from 50 to 300 percent and more. See also Saxon and Anderson (1982) and OECD (1987).