rafmacalaba/datause-encoder-data huggingface dataset:
- 33 -

evident. Moral issues seldom raised are also relevant: what about the rights of people killed or dispossessed prior to 1946 or 1948 or the date that serves the interests of those now represented politically? These issues have been raised, but not resolved, in Hungary, and have not figured importantly in debate in the other countries.

It is not surprising, given the reorganization and turmoil that has characterized the agricultural sectors of the region, that the paper trail of prior property rights has in some areas been lost. The more unexpected fact is how well preserved it is in many places, testifying again to the political inevitability of restitution. The emergence of yellowed but carefully preserved land titles, tax documents, and registries of property brougnt into the collective farm suggests an enduring conviction that these documents woulc some day be important. An elderly Romanian peasant viewing, in early May, 1991, a draft copy of the new land title looked at it carefully, paused, and commented, "Yes, I have one of those from my father. You should give it a bit more color." The restitution approach has an economic advantage to complement its apparent political appeal, and counter some of the economic problems it raises. Had land been distributed without payment to the agricultural work force with no higher principle than "land to the tiller," it would have been easy to exclude rural people from further **dis'ib-UU,I** of s **.e1WC** o-w-ned assets, ile grounus LhaL they aiready received ln tneir fair share. Since landowners have instead received back property that was rightly theirs all along, there can be little justification for excluding rural people from a share of assets accumulated by the state. Thus, when privatization swings into full force through vouchers or distributed shares, rural people will be integrated into the new capital markets.
# **Table A2 – T-Tests, Leavers Vs. Match**

||**2001-**|**2002**|**2002-**|**2003**|**2003-**|**2004**|
|---|---|---|---|---|---|---|
||**t**|**p>t**|**t**|**p>t**|**t**|**p>t**|
||**20**|**01**|**20**|**02**|**20**|**03**|
|hh head sex|-0.245|0.807|0.191|0.849|-0.308|0.759|
|hh head age|0.239|0.812|0.223|0.824|0.100|0.920|
|hh head education|0.143|0.887|0.632|0.529|-0.051|0.959|
|hh n. of children|-1.843|0.069|-0.161|0.873|-0.174|0.862|
|hh share of earners|0.454|0.651|-0.017|0.986|0.828|0.409|
|hh properties|na|na|0.000|1.000|-1.000|0.319|
|hh rural|-0.467|0.642|0.208|0.836|0.619|0.537|
|dummy for Q2 and Q3|0.226|0.822|0.000|1.000|0.980|0.328|
||**20**|**02**|**20**|**03**|**20**|**04**|
|hh head sex|-0.475|0.636|0.768|0.444|-0.155|0.877|
|hh head age|0.224|0.823|-0.029|0.977|-0.031|0.975|
|hh head education|-0.389|0.698|0.618|0.538|0.622|0.535|
|hh n. of children|-0.371|0.712|0.551|0.583|0.476|0.635|
|hh share of earners|-0.130|0.897|-0.712|0.478|-0.152|0.879|
|hh properties|na|na|na|na|na|na|
|hh rural|-0.467|0.642|0.208|0.836|0.619|0.537|
|dummy for Q2 and Q3|0.226|0.822|0.000|1.000|0.980|0.328|

29
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<!-- Start of picture text --> GDP, percent deviation from baseline Consumption, percent deviation from baseline<br>5 —— Scenario 1: Carbon tax and debt repayment 6 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Ca tax and reduction in direct taxes —— Scenario 2: Carbon tax and reduction in direct taxes<br>—— Scenario 3: Carbor\fex and an increase in hh. transfers 5 —— Scenario 3: Caron tex and an increase in hh. transfers<br>4<br>4<br>33<br>22<br>ak<br>1<br>i)<br>0 -1<br>-1 -2<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br>Debt/GDP, difference to the baseline in percentage points Imports/GDP, difference to the baseline in percentage points<br>0 —— Scenario 1: Carbon tax and debt repayment 0.2 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes —— Scenario 2: Carbon tax and reduction in direct taxes<br>—— Scenario 3: Carbon tax and an increase in hh. transfers 0.0 —— Scenario 3: Carbon tex and-an increase in hh- transfers<br>2<br>0.2<br>4 0.4<br>-6 0.6<br>0.8<br>8<br>-1.0<br>ap -1.2<br>-12 -14<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br><!-- End of picture text -->
Benchmarking product market regulation has proved to be a useful tool for monitoring the performance of policies and institutions in OECD countries and for identifying specific policy gaps, thus offering the opportunity to benefit from the experience of other member states. Two surveys have been conducted so far collecting data for 1998 and 2003. Results from these surveys point to a convergence in product market policies across OECD countries, with substantial improvements achieved by countries that originally exhibited relatively restrictive product market regulations, such as Poland, Turkey, Czech Republic, Greece, Italy, France, Mexico, Korea, Hungary, and Spain ( Figure 2) Substantial improvements in easing product market policies have also been achieved among EU15 countries where the average PMR score fell from 2.1 in 1998 to 1.4 in 2003. While this reflects the increasing harmonization of EU common market rules, the PMR benchmarking may have been instrumental in fostering this improvement.

<!-- Start of picture text --> Poland<br>Turkey<br>Mexico<br>Hungary<br>Italy<br>Greece<br>Switzerland<br>Czech Rep.<br>France<br>Spain<br>Portugal<br>Korea<br>Norway<br>EU15<br>Germany<br>Slovak Rep.<br>Netherlands<br>Belgium<br>Austria<br>Finland<br>Japan<br>Luxembourg<br>Sweden<br>2003 1998<br>Canada<br>New Zealand<br>Ireland<br>Denmark<br>U.S.<br>Iceland<br>United Kingdom<br>Australia<br>0 0.5 1 1.5 2 2.5 3 3.5 4<br><!-- End of picture text --> _Source_ : Conway, Janod, Nicoletti 2005. _Note_ : A lower figure indicates a better performance.

**Figure 2. Product Market Regulation among OECD countries, a comparison between 1998 and 2003**

10
**Table 5.9 Manufacturing Output Growth**

||**1995**<br>**Output**<br>**(Bil VND)**|**1999**<br>**Output**<br>**(Bil VND)**|**1999**<br>**Output/Worker**<br>**(Mil VND)**|**5 year**<br>**growth**|**Contribution**<br>**of the formal**<br>**private sector**<br>**togrowth**|
|---|---|---|---|---|---|
|Manufacturing|83,260.5|133,702.4|44|60.6%|14.3%|
|Of which||||||
|_Food &_|27,008.2|40269.8|68|49.1%|16.0%|
|_Beverage_||||||
|_Textiles_|6176.2|9254.9|43|49.8%|14.8%|
|_Garments_|2949.8|5210.7|17|76.6%|24.6%|
|_Leather_|3569.9|8179.7|20|129.1%|20.1%|
|_Paper & Paper_|1946.8|3520.3|59|80.8%|28.6%|
|_Products_||||||
|_Rubber &_|2272|4960.7|129|118.3%|36.7%|
|_Plastics_<br>_Metal Products_|2331.6|4715.5|441|102.2%|19.1%|

Source: GSO, 2001 60. Overall, from 1998 to 2000, the share of private companies in manufacturing fell slightly, but the percentage of private sector workers in manufacturing rose from 53 percent to 57 percent. Employment in the crucial food and beverage processing sector increased over the same period by 54 percent, despite a fall in the total number of companies. The result was an increase in per firm employment from 20 workers to 32. Other benchmark sectors for judging Vietnam’s reliance on labor-intensive, exportoriented activities are textiles, garments, and leather goods and all three showed impressive growth over the two year period. GSO output statistics now show the private sector to be ahead of the state sector in garments and in leather shoe production.

**Table 5.10  1998-2000 Growth in Labor Intensive Manufacturing** **<u>(%)</u>**

||Increase in Firms|Increase in Employment|
|---|---|---|
|Food and Beverage Processing|-5|54|
|<br>Textiles|65|92|
|Garments|44|40|
|Leather Products|68|24|

Source: GSO, 2001 61. But Vietnam still has a relatively large untapped potential to expand its activities in labor intensive and export-oriented manufacturing industries. A study of Vietnam’s export oriented industries showed that—compared with other Asian nations— manufacturers accounted for only a relatively small share (36.4%) of exports in 1997. Given its high human capital and its large supply of labor relative to land Vietnam could significantly raise its labor-intensive manufacturing exports. The study predicts that, with more efficient use of available resources, Vietnam could be expected to increase its share of labor-intensive manufacturers in total exports by 25 percent.<sup>36</sup> Even though exports surged in recent years, the share of manufacturing in total exports in 2000 still stands roughly at the same level (36.4%).

> 36 Belser (1999). _Vietnam. On the Road to Labor-Intensive Growth._

29
**Table 6: Employment by broad sectors, education and gender in Ghana**

, **Ages 7-64.**

# **(figures in percent).**

||||**Ed**|**ucation**||**G**|**ender**|
|---|---|---|---|---|---|---|---|
||**Sectors**|**Primary**|**Middle**|**Secondary**|**Post-sec**|**Males**|**Females**|
|**198**<br>|Government|4.8|63.54|17.90|13.76|75.22|24.22|
|**7**||(2.58)|(10.28)|(16.77)|(52.27)|(11.58)|(3.38)|
||private-formal|12.24|71.43|15.51|0.82|82.37|17.63|
|||(3.52)|(6.18)|(7.77)|(1.82)|(7.00)|(1.33)|
||Private-informal|24.42|70.35|4.65|0.58|64.90|35.10|
|||(4.93)|(4.28)|(1.64)|(0.91)|(4.33)|(2.07)|
||Self employed agriculture|25.7|66.21|6.77|1.32|47.45|52.55|
|||(54.81)|(42.51)|(25.15)|(21.82)|(51.54)|(50.54)|
||Self-employed business|22.25|68.54|8.01|1.20|24.83|75.17|
|||(21.83)|(20.25)|(13.70)|(9.09)|(9.75)|(26.15)|
||Non-working|15.41|60.38|22.97|1.26|45.24|54.76|
|||(11.50)|(13.57)|(29.86)|(7.27)|(13.84)|(14.84)|
|**198**|Government|6.42|59.34|18.29|15.95|76.95|23.05|
|||(3.07)|(10.46)|(17.67)|(73.87)|(13.03)|(3.32)|
||private-formal|12.94|69.26|16.83|0.97|82.18|17.82|
|||(3.72)|(7.34)|(9.77)|(2.70)|(8.50)|(1.57)|
||Private-informal|21.72|72.40|5.43|0.45|63.96|36.04|
|||(4.46)|(5.49)|(2.26)|(0.90)|(4.98)|(2.39)|
||Self employed agriculture|30.94|61.05|7.46|0.55|46.63|53.37|
|||52.04|37.89|25.38|9.01|47.62|(46.35)|
||Self-employed business|25.79|66.26|7.20|0.75|24.90|75.10|
|||(25.65)|(24.31)|(14.47)|(7.21)|(11.93)|(30.61)|
||Non-working|16.73|59.49|22.78|1.11|42.93|57.07|
|||(11.05)|(14.50)|(30.45)|(6.31)|(13.94)|(15.76)|
|**199**|Government|5.07|59.96|32.07|2.90|71.27|28.73|
|||(2.90)|(8.29)|(22.96)|(51.61)|(10.87)|(3.75)|
||private-formal|5.67|70.85|23.08|0.40|80.34|19.66|
|||(1.45)|(4.38)|(7.39)|(3.23)|(4.96)|(1.04)|
||Private-informal|14.80|70.41|14.80|0.00|63.12|36.88|
|||(3.01)|(3.46)|(3.76)|(0.00)|(4.04)|(2.02)|
||Self employed agriculture|23.63|71.31|4.83|0.24|47.45|52.55|
|||(51.71)|(37.73)|(13.23)|(16.13)|(49.68)|(47.09)|
||Self-employed business|17.23|75.35|7.33|0.09|24.26|75.74|
|||(20.21)|(21.37)|(10.77)|(3.23)|(9.98)|(26.66)|
||Non-working|13.16|65.07|21.25|0.53|47.39|52.61|
|||(20.73)|(24.77)|(41.89)|(25.81)|(20.47)|(19.45)|

**Note:** The first row in each sector gives the row percentage, while figures in parenthesis gives the column percentage.

16
consumption, inventory holding, capacity formation, trade, etc. Models often cannot duplicate this complexity and detail. Given the enormity of the task, good modeling requires simplification and abstraction to represent the essential elements of the system under examination. Of importance not only is the detailing of individual relationships concerning say consumption or prices but also the competitive nature of the overall market structure. The approach that is taken to construct commodity models resembles that of other economic models: (1) Determination of modeling purpose(s), (2) Selection of model structure, (3) Specification of relationships, (4) Estimation of parameters, (5) Validation, and (6) Model solution or simulation. TLle purposes of constructing World Bank models center around short and 'ong term forecasting and policy simulation analyses. Short-term forecasting has been concerned with commodity prices and export earnings. Uncertainty surrounds the fluctuations in these variables, because of sudden changes in industrial activity relative to commodity inventory levels. Long-term forecasting has been performed to gain knowledge about supply response and future supply availability as well as prices. Policy simulation analysis helps to answer "what if" questions about past or future commodity market responses to particular policies such as buffer stock stabilisation, or to institutional changes.

The selection of the structure of a commodity model reflects not only the formal methodology employed including model specification, estimation and simulation but also the attributes of the commodity market or a particular commodity problem to be analyzed. Examples of such attributes include the noncompetitive or cartel nature of a market, the presence of international stockpiles, or a range of tariff or nontariff trade barriers. Also relevant in this context is the empirical scope of a modei. While almost all econometric 6
# **<u>Annex: Rethinking Power Sector Reform Indices</u>**

The ‘Rethinking Power Sector Reform’ study employs various indices to measures reforms. As the main global index, a simple Power Sector Reform Index aggregates four dimensions of power sector reform consistent with the standard package of reforms prescribed by international donors in the 1990s. These dimensions are: restructuring (vertical and horizontal unbundling of power utilities); private sector participation; creation of an independent regulator; and competition in power generation. The index gives each country a score from 0 to 100 on each of the above four dimensions of power sector reform. The scores are based on giving equal weight to each step on each dimension of the reform continuum (see tabulation below). The average of the four 0-100 scores is used to provide an overall summary of the extent of reform. _The score of 100 for the reform index is not intended to signify ‘good practice’ but merely alignment with the 1990s model._

## **A. Global reform index**

<!-- Start of picture text --> Regulation No regulator Regulator<br>= 0 = 100<br>Restructuring Vertically integrated Partial vertical Full vertical Vertical & horizontal<br>unbundling unbundling unbundling = 100<br>= 0<br>= 33 = 67<br>Competition Monopoly IPPs Single Buyer Bilateral Competitive<br>Model Contracts market<br>= 0 = 25<br>= 50 = 75 = 100<br>Private Sector 0.5*(Percentage of generation capacity with private sector participation)<br>Participation<br>+<br>0.5*(Percentage of distribution utilities with private sector participation)<br><!-- End of picture text --> Separately, a set of distinct indices are used to measure regulatory and governance frameworks in the power sector. The scores for Vietnam and a selection of other case study countries, are presented below. _For these scores, 100% corresponds to what can be considered ‘good practice’._ 43
<!-- Start of picture text --> _a] 2<br>& 15<br>fe<br>Q<br>% 1<br>£<br>5 08 VAT rate of 32%<br>a oO@ 0 4<br>i) Q<br>oT Wwa -0.5<br>2%<br>Se 1<br>2s<br>dic -1.5<br>3a -2<br>cD)<br>SG<br>2 25<br>Cc<br>o -3<br>~~5S} 35<br>0 5 10 15 20 25 30 35 40 45 50<br>InitialVAT rate<br><!-- End of picture text -->
_SECURITIES CLEARANCE AND SETTLEMENTSYSTEMS_

## TRADE CLEARANCE,

- Trade CaptureM:atchimg/ConfirmatbofComparisou and Affirmation _4. Brkers,_ both buying and _selling. semd to_ the Clearing _Ae-t_ a details Broke deliver a _c5 The_ to their custers co ghe detail O custome?s ex cited orders.

- S. The Clring Agent com each side of the trade and _trep vaq_ brq., 6. For lare itutd iesrthe confirnation is nrall direted tD an it ry such as a custodian acing as agent "for he investor in the cleaing proces _During tht phae the inftmadonflow continues untii there_ **_are nw_** _msae_ **_Jn_** _trade deta. In some cases those procses m y_ **_occur_** _outsid of the lewing- ee Irad executonprocess When the trd are *4.nsmited as 'oked-lW itwactliw y$e r sstems;qf the echnge or r market, the d f e es hw t*ed Id&,-all cemp"in of trades_ **_bedween matii key_** _tw. *mW_ **_br_**

- Calculatilo of **settlemet** oblgatio 7. The Clearing Aget sends to the brokes, custo4ianad a sede FiJd s (gross, bilatal net or multiea net) and the fnd b :i l1 lm t at(go **r** oss, net). In _case_ ofmiakes, t inormation _fO_ continues nil the besare **coe** The selling broker must provide aiaility of securities befoe the ttime,t ad he buying broke must provide availabiliy of funds befor the -im ---. _Avaiaility_ of securities _*_ &ecurities _in book-entfM: .mmobilzed and demaeriaized serities are availe throuh the broker's acconts in the 4iotory Normally, Ie :w - se*it -trf trade are bloced unil the rade is setd4e and cann be used for other trads hse days. 2§e T2WJhm:Ybrokers_ **_h_** _e oldmakethemavaiable_ . : _the ett..:e_ . _da._

- Availabiy of _fUnds *_ Pamen _hgh_ asetm agent _In this care brokr or it -yn_ **_genaha._** _an account at the settlement gnt *ualy a b aand t learing balances to the settlement agewnt fore setalme day. &Oker%, pyta t b.azks or custodian_ **_s_** _endthefinus bore eement t- - ._

- * Diec PaMe by t &k. In this case buying brker 4irecty povide thends at settlemen **t** ime by a n&ns of payment (e.g., acheck, ceitifled a direc paymen through its paying agent to the selling broker.

- _In some occions the dearing agn guarantees_ **_the_** _trade netng he d wiy ad recet of_ **_settleet_** _obligations. This is referred to as "novadon"w or t oe parfor ano_ **_(the clearng_** _agenbecomethe rto ev sreler nd_ **_- theselerto_** _eer b-w)y4_ **_hT most markets trade clkaae &pksHy rangesfrm T+l_** _to_ **_T+S._**

# TRADE **SETTLEMENr**

8. Securities are delivered in exchange of finds.

## Delivery of securities

- Through a Depository (securities ar previously immobZized in the depository or issu in a dematerialized form)

- _Diectly_ **between** the brokens (physicai form of securities)

7
# **Figure 8: Disclosure of information**

**a. Laws or regulations require the ultimate (beneficial) owner and controller of a bank to be publicly disclosed (% of respondents answering Yes)**

**b. Banks disclose to the public governance and risk management framework (% of Yes)**

Source: Waves 4 and 5, BRSS.

Note: Off-shore financial centers are excluded from the calculations. DEV are developing countries. The number of countries for which we have a response for the specific item/question for both rounds appears within parentheses. * next to the number of countries represents a statistically significant difference in the mean values of a regional group in the two reference years at the 10% level using a two-tailed test. In both panels, higher values indicate that better quality information is available to market participants.

Ratings are an essential source of information to evaluate the creditworthiness of counterparts though they should be not the sole source of information to evaluate a bank’s risk exposure. The BRSS data show a modest increase in the share of countries that require banks to have external credit ratings, though in EAP and ECA this was still not a requirement (Figure 9, Panel A). Although economies of scale facilitate the provision of these information services, on average less than half the top-ten banks in developing countries overall and, specifically, in the ECA, SA and SSA regions had credit ratings provided by an international credit rating agency (Figure 9, Panel B). It follows that market participants in these regions often lacked key information to monitor and influence banks to limit excessive risk-taking.

17
**<u>Table 6. Predicted Poverty Rates Based on Imputation, from TA 1 to TA 3 using 2020/21 Poverty Line, Tanzania (percentage)</u>**

|**Mthd**||||**TA**<br>|**3**||||
|---|---|---|---|---|---|---|---|---|
|**eo**|**Model 1**|**Model 2**|**Model 3.1**|**Model**<br>**3.2**|**Model 4.1**|**Model**<br>**4.2**|**Model 8**|**Model 9**|
|1 Nl li i dl|**20.6**<sup>**a**</sup>|**20.9**<sup>**a**</sup>|40.9|**19.5**<sup>**a**</sup>|45.7|**20.8**<sup>**a**</sup>|**20.8**<sup>**a**</sup>|**20.6**<sup>**a**</sup>|
|) orma near regresson moe|(2.4)|(2.4)|(2.5)|(2.1)|(2.9)|(2.4)|(2.4)|(2.5)|
|2) Empirical distribution of the error|**20.6**<sup>**a**</sup>|**20.7**<sup>**a**</sup>|41.6|**19.0**<sup>**a**</sup>|45.8|**20.5**<sup>**a**</sup>|**20.4**<sup>**a**</sup>|**20.7**<sup>**a**</sup>|
|terms|(2.4)|(2.4)|(2.5)|(2.1)|(2.9)|(2.4)|(2.4)|(2.4)|
|_Control variables_|||||||||
|Food expenditures|||Y|Y|||||
|Non-food expenditures|||||Y|Y|||
|Utilities: water, kerosene, lighting|||||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.34|0.51|0.89|0.89|0.76|0.73|0.55|0.41|
|N1(base survey)|711|711|711|711|711|711|711|711|
|N2(target survey)|698|698|698|698|698|695|698|698|

**True poverty rate in TA 1**

21.0 <u>(2.2)</u>

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in TA 1. Food and non-food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 3 use the estimated parameters based on the TA 1. 1000 simulations are implemented. True poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.7.

37