rafmacalaba/datause-encoder-data huggingface dataset:
19

**Table 1: Community-based Woodland and Forest Management in Tanzania, 1999**

|**Regions**|**Management of public lands or**|**Management of reserved forest areas**|**Total**|
|---|---|---|---|
|_Districts_|**village areas**|_Government Forest reserves_||
||_Village Forest Reserves (VFRs),_<br>_Conmnunity Forest Reserves (CFRs)_<br>_Individual 'Reserves' (ngitiri)_|_National Forest Reserves (NFRs)_<br>_Local Government Forest Reserves_||
|ARUSHA|||61|
|_Babati_<br>_Kiteto_<br>_Simanjiro_|53 villages directly manage 176,500 ha<br>ha all as VFR|8 villages manage 5,635 ha Ufiome<br>NFR|communitiesl82,<br>135 ha|
|_Ngorogoro_||||
|SINGIDA||||
|_Singida Rural_|5 villages directly manage five VFR,<br>totaling 43,000 ha;<br>15 villages in process of establishing 15<br>VFR, total area estimated at 50,000 ha||20 communities<br>est. 93,000 ha|
|MWANZA||||
|_Sengerema_|381 villages manage 381 VFR;||661 communities|
|_Magu_<br>_Mwanza Rural_<br>_Geita_<br>_Kwimba_|280 sub-villages, clans and other groups<br>manage 280 CFR;<br>647 individuals manage private<br>'reserves';||647 individuals<br>35,000+ ha|
|_Ukerewe_<br>_Missungwi_|Total: 35,000+ ha|||
|TABORA||||
|_Urambo_<br>**_Nzega_**<br>**_Igunga_**<br>**_Tabora_**|76 villages manage 7,660 ha VFR;<br>31 individuals manage 394 ha private<br>'reserves'|7 local groups co-manage 13,700 ha of<br>Urumwa NFR|83 communities<br>31 individuals<br>21,754 ha|
|TANGA||||
|_Lushoto_<br>_Tanga_|7 villages directly manage 605 ha;<br>Ivillage manages first gazetted VFR<br>(Mpanga) 60 ha|I village co-manages 5,000 ha of<br>Shume-Magamba NFR<br>I village manages part of Baga NFR,<br>500 ha<br>I village manages part of Kabosa NFR,<br>500 ha|11 communities<br>6,665 ha|
|**MOROGORO**<br>_Kilosa_|**7 villages manage 10,000 ha in VFR**||7**communities**<br>**10,000 ha**|
|**Total**|**544 Villages**<br>**280 Groups**<br>**678 Individuals**|**11 Villages**<br>**7 Groups**|**555 Villages**<br>**287 Groups**<br>**678 Individuals**|
|**Total**<br>**'Reserves'**|**1,502 village, community or**<br>**individual 'reserves'**|**5 reserved forest**areas|**1,507 reserves**|
|**Estimated**<br>area (ha)|**323,219**|**25,335**|**348,554**|
Table 7: O.L.S. and I.V. Regression Results without cohorts between 1968 and 1971

|||||Dependent v|ariable: Pr(bi|rth teen=1)||||
|---|---|---|---|---|---|---|---|---|---|
|||Whole sampl|e||At least KCP|E|At leas|t secondaryc|omplete|
||O.L.S.|I.V.|I.V.|O.L.S.|I.V.|I.V.|O.L.S.|I.V.|I.V.|
|First-stage(all|Second-stage|RHS variabl|es included,b|ut not reporte|d)|||||
|Reform||-0.042|-0.015||0.772***|0.670***||0.990***|0.803***|
|||(0.14)|(0.13)||(0.14)|(0.13)||(0.23)|(0.22)|
|Second-stage||||||||||
|Education|-0.041***|0.641|0.014|-0.055***|-0.119***|-0.129***|-0.038***|-0.153***|-0.172***|
||(0.00)|(3.49)|(0.01)|(0.00)|(0.03)|(0.04)|(0.00)|(0.04)|(0.05)|
|Year|1.645***|-19.607|(dropped)|1.707***|4.189***|4.472***|0.864|7.051***|7.849***|
||(0.34)|(108.62)||(0.43)|(1.44)|(1.59)|(0.59)|(2.44)|(2.99)|
|Year<sup>2</sup>|-0.000***|0.005|-0.000***|-0.000***|-0.001***|-0.001***|-0.000|-0.002***|-0.002***|
||(0.00)|(0.03)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Muslim|0.009||0.095***|0.029||-0.020|-0.002||-0.075|
||(0.02)||(0.03)|(0.03)||(0.05)|(0.03)||(0.07)|
|City|-0.007||0.010|-0.009||0.027|-0.083*||0.063|
||(0.03)||(0.04)|(0.04)||(0.04)|(0.05)||(0.09)|
|Town|0.013||0.056|0.008||0.012|-0.056||0.002|
||(0.03)||(0.04)|(0.03)||(0.03)|(0.04)||(0.06)|
|Countryside|0.018||0.133***|-0.008||-0.087*|-0.059||-0.120**|
||(0.03)||(0.04)|(0.03)||(0.05)|(0.04)||(0.05)|
|Abroad|-0.092||-0.067|-0.037||-0.024|-0.078||-0.077|
||(0.06)||(0.07)|(0.07)||(0.08)|(0.07)||(0.13)|
|Embu|0.176***||0.112***|0.125***||0.037|0.071||-0.135|
||(0.02)||(0.04)|(0.03)||(0.06)|(0.04)||(0.10)|
|Kalenjin|0.173***||0.055|0.134***||0.074|0.057||-0.061|
|<br>|(0.02)||(0.04)|(0.03)||(0.05)|(0.04)||(0.08)|
|Kamba|0.160***||0.002|0.125***||0.085**|0.106**||0.068|
||(0.02)||(0.05)|(0.03)||(0.04)|(0.04)||(0.06)|
|Kikuyu|0.156***||0.002|0.090***||0.014|0.090**||-0.095|
||(0.02)||(0.05)|(0.03)||(0.05)|(0.04)||(0.09)|
|Kisii|0.192***||0.074*|0.166***||0.101**|0.133***||0.002|
||(0.02)||(0.04)|(0.03)||(0.05)|(0.04)||(0.08)|
|Luhya|0.243***||0.168***|0.208***||0.102|0.161***||-0.031|
||(0.02)||(0.04)|(0.03)||(0.07)|(0.04)||(0.10)|
|Luo|0.212***||0.137***|0.153***||0.047|0.082*||-0.104|
||(0.03)||(0.04)|(0.04)||(0.07)|(0.05)||(0.10)|
|Masai|0.021||0.023|-0.014||-0.157*|-0.055||-0.421***|
||(0.03)||(0.03)|(0.04)||(0.09)|(0.04)||(0.16)|
|Meru|0.038||-0.034|0.014||-0.069|0.045||-0.124|
||(0.03)||(0.04)|(0.04)||(0.06)|(0.05)||(0.10)|
|Mijikenda|0.062*||0.040|0.029||-0.129|0.040||-0.248|
|/ Swahili|(0.03)||(0.04)|(0.04)||(0.10)|(0.06)||(0.16)|
|Recall|-0.002**|-0.012|-0.003**|0.003**|0.016**|0.020**|-0.016***|0.039**|0.048*|
||(0.00)|(0.05)|(0.00)|(0.00)|(0.01)|(0.01)|(0.00)|(0.02)|(0.02)|
|Survey FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

34
Table 8 – Balancing results of the sample _after_ matching - mean values

|||TEC- geral|TEC|- after g<br>educatio|eneral<br>n|TEC - c<br>gen|oncurrent<br>eral educa|ly with<br>tion|Techn<br>the|ical educ<br>tertiary|ation at<br>level||FIC||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||(1)||(2)|||(3)|||(4)|||(5)||
||treat.|contr.<br>Mean<br>dif.|treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|
|Experience|19.9|19.9|19.0|18.6||20.7|20.7||21.6|20.6||20.0|20.1||
|Potential experience|16.9|17.0|16.5|16.2||17.3|17.3||16.2|15.2||18.3|18.4||
|Self-employed / employers|0.20|0.19|0.17|0.16|**|0.22|0.21||0.22|0.23||0.26|0.25|***|
|Agriculture job|0.02|0.01|0.01|0.01||0.02|0.02||0.02|0.02||0.03|0.03|*|
|Activity - extractive industries|0.01|0.01|0.01|0.01||0.01|0.01||0.01|0.01||0.00|0.00||
|Activity - manufacturing|0.15|0.14|0.16|0.15||0.15|0.15||0.17|0.17||0.17|0.17||
|Activity - construction|0.03|0.03|0.02|0.02||0.03|0.03||0.04|0.07||0.05|0.05||
|Activity - commerce and repair|0.16|0.17|0.16|0.16||0.17|0.16||0.13|0.12||0.21|0.22|*|
|Activity - lodging and food|0.02|0.02|0.02|0.02||0.02|0.03||0.02|0.01||0.04|0.04||
|Activity - transportation, warehousing<br>and communications|0.04|0.05|0.05|0.04|**|0.04|0.04||0.03|0.04||0.06|0.06||
|Activity - financial services|0.03|0.03|0.02|0.02||0.03|0.04||0.05|0.02|*|0.02|0.02||
|Activity - real estate activities and<br>service activities|0.12|0.12|0.12|0.11||0.11|0.10||0.15|0.12||0.10|0.10||
|Activity - public administration|0.10|0.11|0.09|0.09||0.12|0.11||0.09|0.09||0.07|0.07||
|Activity - education|0.13|0.12|0.07|0.08||0.17|0.18|***|0.18|0.19||0.07|0.08|*|
|Activity - health and social services|0.14|0.15|0.22|0.23||0.08|0.08||0.06|0.06||0.05|0.05||
|Activity - others collective, social and<br>personal services|0.04|0.03|0.03|0.03||0.04|0.04||0.05|0.09||0.08|0.07|***|
|Activity - household activities|0.01|0.02|0.01|0.01||0.02|0.01||0.00|0.00||0.05|0.05||

20
Table 14 Poverty rates estimated using different models to impute household consumption in 2010/11

|_Excluded vars_|(1)<br>Original<br>Model<br>_None_|(2)<br>Model 1<br>_Wage-_<br>_employed_|(3)<br>Model 2<br>_Wage-_<br>_employed,_<br>_no-toilet_|(4)<br>Model 3<br>_Wage-_<br>_employed, no_<br>_toilet,_<br>_imported rice_|(5)<br>Model 4<br>_Non-farm_<br>_employment_|(6)<br>Model 5<br>_All food_<br>_consumption, all_<br>_employment_|
|---|---|---|---|---|---|---|
|2010/11 GHS|0.4354|0.4367|0.4409|0.4495|0.4377|0.4541|
||(0.016)|(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|
|2012/13 GHS|0.4153|0.4166|0.4166|0.4285|0.4184|0.4281|
||(0.016)|(0.016)|(0.016)|(0.016)|(0.017)|(0.016)|
|2015/16 GHS|0.4049|0.4045|0.4049|0.4130|0.4060|0.4111|
||(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|
|2018/19 GHS|0.4188|0.4195|0.4175|0.4182|0.4195|0.4142|
||(0.018)|(0.018)|(0.018)|(0.019)|(0.018)|(0.017)|
|2018/19 NLSS|0.3909|0.3909|0.3909|0.3909|0.3909|0.3909|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|

Note: SE in parentheses. The table shows different imputed poverty estimates from survey-to-survey imputation exercise. Each coefficient is from a separate imputation. Different models are used to test the robustness of the estimates to different specifications of the consumption model, which exclude different sets of covariates as reported in the table. Data are from the 2018/19 NLSS (baseline survey) and 2010/11, 2012/13, 2015/16, 2018/19 GHS.

39
Table 4a: Decision Making Power over Large Purchases - Correlation with Gender Equality Norm: Attitudes and Norms

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Female in<br>HH has all<br>the power<br>(Yes=1/No<br>=0)|Male in<br>HH has all<br>the power<br>(Yes=1/No<br>=0)|Joint<br>decision<br>making<br>with<br>spouse<br>(Yes=1/No|No power<br>at all<br>(Yes=1/No<br>=0)|Female in<br>HH has all<br>the power<br>(Yes=1/No<br>=0)|Male in<br>HH has all<br>the power<br>(Yes=1/No<br>=0)|Joint<br>decision<br>making<br>with<br>spouse<br>(Yes=1/No|No power<br>at all<br>(Yes=1/No<br>=0)|
||**_G_**|**_ender Equa_**|=0)<br><br>**_lity Norm:_**|**_Men and wo_**|**_men should_**|**_have equal_**|=0)<br><br>**_opportuniti_**|**_es_**|
|Female|0.17***|-0.18***|0.03**|0.02|0.13***|-0.12***|0.00|0.03**|
||[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|
|Personal Belief:Agree(No=0/Yes=1)|-0.00|-0.09***|0.10***|-0.00|||||
||[0.01]|[0.01]|[0.01]|[0.01]|||||
|Personal Belief:Agree*Female|-0.04***|0.09***|-0.06***|-0.02|||||
||[0.01]|[0.01]|[0.01]|[0.01]|||||
|Community Norms (0/1)|||||0.01|-0.07***|0.08***|-0.01|
||||||[0.01]|[0.01]|[0.01]|[0.01]|
|Community Norms (0/1)*Female|||||-0.01|0.05***|-0.05***|-0.05***|
||||||[0.01]|[0.02]|[0.02]|[0.02]|
|Constant|0.24***|0.59***|0.12***|0.02|0.26***|0.48***|0.17***|0.00|
||[0.02]|[0.03]|[0.03]|[0.03]|[0.03]|[0.04]|[0.05]|[0.04]|
|Observations|42669|42669|42669|42669|25183|25183|25183|25183|
|Adjusted R-squared|0.06|0.11|0.20|0.20|0.06|0.09|0.22|0.25|
|Mean Female|0.17|0.09|0.56|0.19|0.15|0.07|0.57|0.19|
|Mean Male|0.07|0.22|0.51|0.18|0.06|0.18|0.54|0.18|
|Norms+Fem(Coeff)|-0.04|-0.01|0.03|-0.02|0.00|-0.02|0.03|-0.05|
|Norms+Fem(SE)|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|Norms+Fem(P-value)|0.00|0.34|0.00|0.03|0.91|0.04|0.05|0.00|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|YES|YES|
**Table 6: Baseline Gravity Model Estimation with Aggregate Labor Clause Indicator**

|VARIABLES|(1)<br>OLS<br>RE|(2)<br>OLS<br>FE|(3)<br>PPML<br>Full FE|(4)<br>PPML Any<br>Labor Clause|
|---|---|---|---|---|
|Regional Trade Agreement Indicator|0.356***|0.165***|0.0965**|0.164***|
||(0.0222)|(0.0246)|(0.0397)|(0.0493)|
|Any Labor Clause Indicator|-0.209***|-0.206***||-0.193***|
||(0.0301)|(0.0325)||(0.0524)|
|GDP Importer|0.882***|0.767***|||
||(0.00600)|(0.0165)|||
|GDP Exporter|1.043***|0.525***|||
||(0.00642)|(0.0191)|||
|1 = Contiguity|1.348***<br>(0.0927)||||
|Origin GATT membership|0.563***<br>(0.0299)||||
|Destination GATT membership|0.192***<br>(0.0283)||||
|1 = Common official or primary language|0.779***<br>(0.0376)||||
|1 = Common colonizer post 1945|0.368***<br>(0.0479)||||
|Distance (Pop weighted)|-1.269***<br>(0.0169)||||
|Constant|-16.81***|-15.13***|16.22***|16.21***|
||(0.226)|(0.454)|(0.0205)|(0.0215)|
|Observations|501,256|501,256|1,088,662|<br>1,088,662|
|Number of CountryPairs|32,554|32,554|32,554|32,554|

**Notes:** Robust standard errors in parentheses (clustered on country pairs). *** p<0.01, ** p<0.05, * p<0.1.  In columns (1) and (2), OLS RE (FE) stands for Ordinary Least Squares Random (Fixed) Effects. The dependent variable in columns (1) and (2) is the natural log of the zero-corrected nominal imports from the CEPII-BACI dataset.  The data cover 1995-2018. In columns (3) and (4), PPML stands for Pseudo Poisson Maximum Likelihood and both (3) and (4) include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).  The gravity variables in columns (1) and (2) are from the CEPII Gravity dataset and the regional trade agreement indicator and labor clause indicator are from the Handbook of Deep Trade Agreements.

pg. 29
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- 2 -

# **EXECUTIVE SUMMARY**

Transport infrastructure is among the most important driving forces for economic growth. In Nepal, rural road accessibility remains a crucial challenge. The vast majority of rural residents had to spend more than 30 minutes to access to paved roads. Public procurement is an important policy instrument to use resources wisely and efficiently.

This paper addresses a series of policy challenges in public road procurement, from procurement planning to contract management and project quality assurance. Important, these issues are interrelated to each other. Therefore, the issues need to be addressed in concert. Using data collected from 155 rural road upgrading contracts in Nepal, five questions are addressed: How do bidders determine their bidding strategy, i.e., bid prices, in competitive bidding for rural road contracts? How do they decide whether to participate in the procurement market? What are the main determinants of efficiency in contracting road works? Why ex post contract amendments, such as cost overruns and delays, happen? Which factor is important to ensure the quality of roads delivered by the project?

Our main findings and policy implications are as follows:

# **_1. Competition needs to be enhanced particularly in government owned projects._**

Competition is the most important factor to bring down road procurement costs. The level of competition is clearly insufficient in some public tenders in Nepal. In particular in government owned projects, the average competition is about 4.5, unfavorably compared to an average of 7.3 in World Bank financed projects. Significant losses―possibly, $3 million―are estimated to be incurred by the lack of competition.

# **_2. Wider distribution of tender information at cheaper costs increases competition as well as transparency and accountability._**

To attract more interest and bidders, tender notices should be distributed widely and at low costs. This will bring down the cost of entry for firms. Public tenders can be advertized on national newspapers, rather than local ones. E-bidding or e-procurement can distribute tender information even more widely and in a timely manner. The findings are consistent with the recent initiative that the Nepal government has taken in its e-procurement strategy.

# **_3. Longer bid preparation periods encourage bidder participation in public tenders._**

As stipulated in the procurement guidelines, it is important to grant sufficient time for firms to prepare bids. By doing so, even inexperienced contractors are allowed to consider to participate in auctions, thereby intensifying competition in the market. Of particular note, the extension of the bid preparation period is a no-cost measure for procurers. In Nepal, some of the government owned projects had extremely short bid preparation periods. As an indicative
and product, exports plus domestic consumption must equal imports plus domestic production. The GTAP data set satisfies all accounting identities.

# **4.8 Share of Market Captured by Foreign Direct Investors in Services and by Cross-Border Sales of Services**

It was necessary to calculate the market share of foreign direct investors by source region in the services sectors of our model by host region. That is, take the banking sector in Tanzania as an example. We need to know the share of the market captured by banks from all the regions of our model, i.e., Tanzania, Kenya, Uganda, Rwanda, COMESA, SADC, the EU, US, China and the Rest of the World. The results of our calculations for our African regions are available in tables 6a to 6f. It is also necessary to calculate the share of cross border services in each of our regions for these seven sectors. For cross-border sales of services, we use the data from the GTAP 8.1 data set.

**4.8.1 Kenya and Tanzania.** In the cases of Kenya and Tanzania, we built the data set for this project. Detailed commercial databases were employed, where available. This included Bankscope for banking and Axco for insurance, which list all companies active in Kenya and Tanzania in these sectors. For ownership shares of the companies by region of our model, the websites of the companies often had to be consulted. In telecommunications, the

telecommunication regulatory authorities in Kenya and Tanzania list all active companies and their market shares in mobile and fixed line services. Details and documentation of the calculations are available in Jafari (2014e), who built on and updated earlier work by Worley (2009). The results for Kenya and Tanzania are presented in tables 6a and 6b.

**4.8.2 Regions other than Kenya and Tanzania.** Other than for our seven business services sectors in Kenya and Tanzania as discussed above, and insurance and communications services in our Africa regions, the basic data source for foreign affiliate sales is the database developed by Fukui and Lakatos (2012). This database has some advantages over earlier efforts to develop databases of foreign affiliate sales. First, it is based on sales data, rather than using investment data as a proxy for sales data. Second, Fukui and Lakatos use the Eurostat database of data on foreign affiliate sales in 41 countries (including the United States), rather than relying on data from the United States alone. Despite having data for 41 countries, there are a large number

17
where φ(<sup>b</sup> i) =<sup>∂F2(</sup> ∂t<sup>h(i),1)</sup> . Again, using the same steps has in the previous section, it can be shown that the weighted growth rates of wages within brackets in equation (13a) is equal the growth in average wages minus the growth in different types of labor weighted by their wages shares. In this case, equation (13a) can be rewritten as in equation (10):

# 3 Data Set and Sample Characteristics

The data set used, “Quadros do Pessoal”, is based on an annual compulsory survey conducted by the Portuguese Ministry of Labor. It is a longitudinal data file covering all the firms and plants operating in Portugal<sup>18</sup> . Records are available at worker, plant and firm level, and provide a detailed description of the labor force characteristics. At the firm and plant level, there is information for the sector of activity, regional location and the number of employees. The analysis covers the period 1985-1994<sup>19</sup> . The original data has 123, 294 and 214, 047 plants and 1,8 million and 2,2 million workers in 1985 and 1994, respectively.

An important issue is the choice of the geographical unit of analysis. The location specific knowledge of a firm can be specific to the city, to the district or to another geographical unit, depending on the type of knowledge considered. The components of knowledge range from very location specific to more general ones, that cross international borders. There is evidence that knowledge spillovers are geographically localized (Jaffe et al., 1993) and most of the empirical literature has focused on metropolitan areas, based on the fact that communication is more intensive there (see Lucas, 1988). To widen the scope of the study to non-urban areas I use “concelhos” as the geographic unit<sup>20</sup> . The Portuguese Administrative Law establishes that the division of Portugal’s mainland is done into 275 “concelhos”, grouped together

> 18 Except those firms without wage-earnings employees and Public Administration.

> 19 In 1994 the classification of economic activities changed to match the European Community criteria and the correspondence between the two classifications is not unique.

> 20 Other papers have also used different units of observations: Peri (1992) uses a data set with information at the city and province level which cover all Italian territory and Combes (2000) uses “zones d’emploi” for France, which is the most similar unit to the one used in this study.

12
tailored FSAL didn't have its merits, but rather that any positive effects were likely to be felt later on, and were probably unlikely to be reflected in dramatic jumps in M2.

# (B). Venezuela

The case of Venezuela further underscores the primacy of reforms that focus on trade liberalization and macro-stability. As noted above, at the time of its intervention, Venezuela's financial sector was at a stage of development similar to that of the Philippines. Venezuela, however, had not undergone reform sufficien **t** o instill macro stability nor openness to trade -- in the two years prior to intervention inflation averaged 62.7%; in the intervention year, imports were only 15.3% of GDP. Largely due to these two factors, the model predicted that Venezuela's M2/ GDP would undergo a 3.84 point _contraction_ in the wake of the intervention. In fact, a 4.14 point contraction occurred.

These were not, however, the only two respects in which the two countries differed, as the Philippines was also somewhat further along the road to sustainable financial sector reform in some key areas. Although the Venezuelan FSAL was a part of a comprehensive reform program, and although progress had been made on a number of issues including exchange rate unification, deficit reduction, reduced protectionism, interest rate liberalization, and the transformation of indirect subsidies into focused social programs, a bit more had probably been accomplished in the Philippine case.<sup>63</sup> In the President's Report (PR) that accompanied the Venezuelan FSAL, the Bank identified fundamental financial sector reforms yet to be achieved: fiurther interest rate liberalization,

**63** Venezuela PCR, March 14, 1995, p. 3.

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