rafmacalaba/datause-encoder-data huggingface dataset:
# **I.  INTRODUCTION**

Trade liberalization and export promotion have been central to Vietnam’s continuing economic transition. To this end, Vietnam has pursued a multi-pronged approach to gradually reducing trade barriers and increasing the outward orientation of the economy. Vietnam’s economy has doubled in size during the last decade, while its poverty rate has halved; exports are growing by 20 percent per year (substantially faster than GDP), and foreign direct investment (FDI) inflows by 10 percent per year.

Vietnam’s commitment to trade liberalization has been fueled, to a large extent, by bilateral and multilateral trade agreements. Under the ASEAN Free Trade Agreement (AFTA, 1995), tariffs on imports from ASEAN countries were reduced to below 20 percent and are targeted to be below 5 percent by 2006. Under the United StatesVietnam Bilateral Trade Agreement (USBTA, 2001), Vietnam made substantial further commitments to liberalize its trade regime, including tariff reduction and removal of quota restrictions. Other trade reform measures that Vietnam has committed to under the USBTA include improving transparency in its trade laws, introducing dispute settlement procedures, protecting intellectual property, and facilitating investment. USBTA provided a sizable fillip to exports since it became effective, with exports to the U.S. increasing by 128 percent and accounting for 82 percent of total export growth in 2002, making U.S. the largest market for Vietnamese exports<sup>2</sup> . Currently, Vietnam is vying for membership in the World Trade Organization (WTO) which requires a commitment to simplify import controls and reduce the level of import protection.

The composition of Vietnam’s exports and imports has also altered significantly during this period. While the share of crude oil in total exports declined from a third in the early 1990s to a fifth by 2002, manufacturing exports rose from 6 percent to 32 percent (see Figure 1). There has been a significant diversification in Vietnam’s export markets as well. With Vietnam becoming less dependent on oil exports, Japan and Singapore have become less important as destinations for Vietnam’s exports. The EU and U.S. have

> 2 IMF (2003).

2
where _L_ is the total loss; _PV_ is the present value of lost earnings; _Y_ is the mean annual earnings; _α_ is an adjustment factor to account for the part of the school year when schools were closed; _r_ is the rate of return on one year of schooling; _S_ is the total number of students; and _β_ is an adjustment factor to allow for some distance learning during the period of school closure.

Previous studies have documented average rates of return to schooling by income level. The returns vary by country and country group, but not by very much (see Table 1).

<u>Table 1: Returns to Schooling (Percent)</u>

|Incomegroup|Returns|
|---|---|
|Low|9.3|
|Middle|9.2|
|High|8.2|
|World|8.8|

_Source_ : Psacharopoulos and Patrinos (2018) We use an even more conservative estimate and assume that every additional year of schooling equates to about 8 percent in additional future earnings.<sup>44</sup> Therefore, the returns to education estimate, _r_ , that we use here is 8 percent. We then use the number of months of education closures to estimate the loss in marginal future earnings. For example, if _Country X_ closes its schools and universities for four months, – that is, 1/3 of the school year – then the loss in marginal future earnings would be 2.64 percent per year over a student’s working life. Therefore, our earnings adjustment for _Country X_ , α, is 2.64 percent.

We use mean annual earnings, _Y_ , of employees in 2011 PPP dollars from ILO or the World Bank JoIn database. The data is country-specific where available; otherwise we calculate the average for a country's income class. The ILO (2020) data are from the ILOSTAT database <u>(https://ilostat.ilo.org/, retrieved May 2020) and from the World Bank (2020b) the data is from the</u> Global Jobs Indicators Database <u>(https://datacatalog.worldbank.org/dataset/global-jobsindicators-database, retrieved May 2020). We also adjust the aggregate loss by assuming that not</u> all future workers will always be employed by giving a range of estimates based on labor force participation (Azevedo et al. 2020).

To estimate the present value (PV) of lifetime earnings, we assume a working life of forty-five years and a three percent discount rate for all groups. We use data for the total number of students, _S_ , from UNESCO.

We assume that some of the learning loss will be mitigated by distance learning measures. Therefore, we include _β_ , an adjustment factor to allow for some distance learning during the period of school closure. We assume an optimistic level _β_ , at 0.9, indicating that only 10 percent of students suffer learning loss due to the distance learning opportunities, a conservative estimate that assumes that having distance learning opportunities equates with learning. The actual learning losses might be considerably higher.<sup>45</sup> Our estimate of distance learning coverage comes from <u>UNESCO. Finally, we use World Bank data on GDP by country. We estimate total losses within</u> a range by adjusting labor force participation between 0.7 and 1.0.

7
-7-

# Soviet Esports

The USSR has contracts to export or exports natural gas to fourteen countries. Besides Eastern Europe, the USSR currently supplies Austria, West Germany, Italy, Finland, France, Belgium and Turkey and has a contract with Creece. It has held supply discussions with Spain and some Scandanavian countries. Table 3 summarizes the quantities of current and projected USSR natural gas exports.

Table 3

||.ovi|et Natu|ral Gas|Export|s 1970-|95 (BCM|)||
|---|---|---|---|---|---|---|---|---|
||1970|1975|1980|<br>1985|1986|1987|<br>Mid to|late 1990s|
||||||||ACQ*|Possible<br>Range|
|Austrla|1.0|<br>1.9|<br>2.9|<br>4.2|4.0|3.9|<br>3.9|3.4-4.4|
|Federal Republic of<br>Germany (Including|||||||||
|West Berlin)||3.1|10.7|12.4|15.3|17.3|19.3|16-25|
|Italy||2.3|7.0|6.0|8.0|8.6|12.3|10-15|
|France|||4.0|6.8|9.3|8.8|8.0|6-12|
|Fnlnand||0.7|0.9|1.0|1.2|1.6|1.2|1-2.5|
|Turkey||||||0.5|3.5|3-6|
|Switzerland|||||||0.4|0.36|
|Greece|||||||1.5|1-3|
|Sweden||||||||0.5-1.5|
|Total Western Europe|1.0|8.0|25.5|30.4|37.9|40.7|50.1|41-68|
|East European 6|2.4|11.3|26.6|34.7|37.2|39.3||55-65|
|Yugoslavia|||2.1|3.6|4.0|4.4||6-7|
|Total|3.4|19.3|54.2|68.7|79.2|84.4|50.1|102-140|

* Annual average contract quantity.

Source: International Gas Trade in Europe: The Role of the Soviet Union by JonAthan P. Stern, Royal Institute of International Affairs, London.

The pricing provisions of the Soviet natural gas export contracts to Western European nations remain confidential, but from press accounts and industry rumors, it can be surmised that the gas is priced competitively with other European gas supplies as well as with altetr.ative fuels. The base price is believed to originally have approximated $4.00/HMBtu with some fluctuations to individual countries. Taking into account what is believed to be the adjustment clauses, Soviet exports are now estimated to be priced in the $2.00-$2.50/MMBtu range for Western European importers at their borders.
27

**Table 2.1 Production function estimation for port sector**

|**Author**|**Activity**|**Functional**<br>**specification**|**Data**|**Variables (1)**|**Economies of Scale **|**Other Measurements**|
|---|---|---|---|---|---|---|
|Chang<br>(1978)|Infraestructure?|Cobb-Douglas|Time series<br>Annual observations (21)<br>(1953-1973)|Q1(L,K,e<sup>T/L</sup>)|Constants|Average productivities<br>Marginal Productivities|
|Reker el al.<br>(1990)|<br>Terminal-berth<br>of Containers|Cobb-Douglas|Panel data<br>Three terminal<br>Monthly observations (70)<br>(May 84-February-90)|Q2(C;B;L)|Diminishing|Nothing|
|Tongzon<br>(1993)|Terminal-berth<br>of Containers|Cobb-Douglas|Panel data<br>Three terminal<br>Monthly observations (70)<br>(May 84-February-90)|Q3(X1, X2,X3)|Increasing|Efficiency for wharf|
|(1):<br>Q1=<br>Q2=<br>Q3=<br>L =<br>K =<br>E<sup>T/L</sup>=<br>C =<br>B =<br>L =<br>X1=<br>X2=<br>X3=|Production = Annual gro<br>Production = Number of<br>Production = Number of<br>Men years (excluding po<br>Value of the clear assets<br>Proxy for the technolog<br>Net crane operation time<br>Berth hours<br>Labor<br>Number of cranes per be<br>Labour per berth hours<br>Number of TEUS carrie|ss earning to the por<br>TEUS<br>TEUS per berth hou<br>rk workers)<br>of the port (prices of<br>ical progress, (T/L =<br> <br>rth hour<br>d per berth hour|t in 1967 prices (wage payments to p<br>r<br>1967)<br>Tons for unit of labour)|ort workers not included)|||

27
## **Template 4  ODA in infrastructure service delivery**

|**US$ millions**|**t-4**|**t-3**|**t-2**|**t-1**|**t**|
|---|---|---|---|---|---|
|Total infrastructure||||||
|140: I.4. Water supply and sanitation||||||
|210: II.1. Transport and storage||||||
|220: II.2. Communications||||||
|230: II.3. Energy||||||

Table 1.4 provides another example of how to leverage the SPI exercise using secondary sources. In this case, the table shows data from the OECD Credit Reporting Database for Sierra Leone.

**Table 1.4 Sierra Leone 2007, ODA in Infrastructure**

|**US$ millions**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|
|---|---|---|---|---|---|---|
|Total infrastructure|2.79190057|66.48603636|39.50414176|94.27007312|3.33610499|55.43493961|
|140: I.4. Water supply and sanitation|0.97405642|8.82893883|18.16274457|5.58331844|10.6412804|11.78243788|
|210: II.1. Transport and storage|0.16644811|66.45760378|0|56.27007312|2.94492878|27.37850787|
|220: II.2. Communications|0.67516879|0|0.56316173|0|0.03401264|0.02792133|
|230: II.3. Energy|1.95028367|0.02843258|38.94098003|38|0.35716357|28.02851041|

_Source: http://www.oecd.dac.org_ .

# Initial data collection: Practical concerns

SPI data need to be collected from central ministries (finance and economic planning) as well as from key line ministries, autonomous agencies, parastatals and, in a few occasions, nongovernmental organizations (NGOs). The involvement of key official and technical personnel is therefore critical in order to accurately identify sources and resources, and to validate inputs. As part of the data collection effort it is also essential to plan ahead and factor in a given country‘s budget and administrative cycles.

## **Budgetary cycle and budget classification**

Establishing the budgetary cycle of the given country is a prerequisite to identifying the time periods that may be particularly inconvenient for data collection activities, to tailor the fieldwork timetable accordingly, and to get a sense of when budget data will become available (even before being published). Likewise, knowing the budget coding system used by the government is essential. In particular, the country may or may not have adopted the IMF‘s Government Finance Statistics Manual 2001 (GFSM 2001)<sup>5</sup> for reporting budgetary accounts. If the country does not use the GFSM 2001 for its reporting, a code-mapping exercise will be necessary (to be discussed in more detail in the next section).

## **Strategy for fieldwork**

Institutional mapping is followed by establishing the scope of the data collection in terms of the institutions and sectors covered, as well as the level of detail and disaggregation. This is where the complexity of comprehensive spending data collection is matched to the desired accuracy. _Resource_

> 5 http://www.imf.org/external/pubs/ft/gfs/manual/.

22
paper. Section 6 describes the concept and measurement of mobile savings. Section 7 presents the core analysis. Section 8 discusses the future of mobile savings and concludes.

# **2 The Reach of Mobile Phones in Kenya**

This section reviews recent data on the extent to which mobile phone and mobile money usage has penetrated Kenyan society. The information presented here comes from the Kenya Afrobarometer survey, which was conducted in November and December 2011. The Afrobarometer sample is representative of the population of Kenyan adults (age 18 and older).

The Afrobarometer data show that mobile phone access has reached extremely high levels in Kenya (Table 1). The survey asked each respondent if he or she every uses a mobile phone and, if so, who owns the phone that he or she uses most often. Eighty percent of Kenyan adults own their own individual phones, an additional 10 percent use a phone owned by someone else in their households, and 3 percent use a phone owned by someone outside their households. Slightly lower numbers of women and rural residents own their own individual phones—78 percent of women and 77 percent of rural residents— and women and rural residents are more likely to use a phone owned by another member of their household. Overall phone usage rates differ very little between men and women and between urban and rural households. On average Kenyan households own 2.4 mobile phones, and 93 percent own at least one phone (Figure 1).

The survey separately asked how often respondents normally use a phone to 1) make or receive a call, 2) send or receive a text message, and 3) send or receive money or pay a bill. A detailed tabulation of the basic findings from these questions is shown in Figure 2. As with the mobile access figures, the usage rates for particular mobile functions are strikingly high. At least once a day, 81 percent of Kenyan adults make or receive a call, 61 percent send or receive a text message, and 22 percent send or receive money or pay a bill using a mobile phone.

Differences by gender in mobile phone usage for particular functions are small. Nearly the same number of women as men make or receive a call on a daily basis (80 percent versus 82 percent). The gender gap is slightly higher for daily use of text messages (58 percent versus 65 percent) and for daily use of mobile money (21 percent versus 24 percent). A comparison of urban and 3
**Table C1. Results of Policy Overlaps under DEPS in Scenarios of Various Overlaps**

||**Electricity**<br>**Price**<br>**(yuan/kWh)**|**Marginal**<br>**Revenue of**<br>**Renewables**<br>**(yuan/kWh)**|**Marginal**<br>**Revenue of**<br>**Fossil-**<br>**based**<br>**Electricity**<br>**(yuan/kWh)**|**Unit Cost of**<br>**Electricity**<br>**Used by**<br>**Covered**<br>**Sectors**<br>**(yuan/kWh)**|**Allowance**<br>**Price**<br>**(yuan/t)**|**Cost Per**<br>**Ton of**<br>**Abatement**<br>**(yuan/t)**|
|---|---|---|---|---|---|---|
|**Phase 1**|||||||
|**R2**|0.692|0.769|0.746|0.711|65|15.3|
|**RN**|0.692|0.769|0.746|0.701|65|15.3|
|**SN**|0.690|0.767|0.747|0.690|68|19.4|
|**NO**|0.695|0.695|0.695|0.695|152|19.6|
|**Phase 2**|||||||
|**R2**|0.686|0.798|0.743|0.747|71|17.3|
|**RN**|0.687|0.799|0.744|0.706|72|17.5|
|**SN**|0.685|0.798|0.685|0.685|101|22.9|
|**NO**|0.695|0.695|0.695|0.695|152|19.6|
|**Phase 3**|||||||
|**R2**|0.675|0.745|0.821|0.790|207|22.5|
|**RN**|0.673|0.754|0.838|0.694|227|24.2|
|**SN**|0.673|0.760|0.673|0.673|263|29.0|
|**NO**|0.681|0.681|0.681|0.681|313|32.0|

# **2. DEPS with Optimized Renewable Subsidy and Electricity Taxes**

Additional cost savings are possible with exogenous renewable subsidies and electricity taxes by optimizing tax and subsidy rates together. Figure C1 presents the relationship between cost per ton, a broad-based electricity consumption tax rate, and renewable subsidy rates with DEPS. As the existence of an optimal level of renewable subsidy, there also exists an optimal level of electricity tax that corrects the incomplete cost passthrough just correctly. The optimal rate of the tax also depends on the rate of overlapping renewable subsidies. The higher the renewable subsidy rates, the lower the electricity price, the higher the needed overlapping electricity tax. Therefore, the two overlapping policies (electricity tax and renewable subsidies) are interdependent.

59
<!-- Start of picture text --> Deal breaker Most important factor,<br>Risk (0-100%) %<br>B @ A. Policy and regulatory risk 49%<br>3 ) B. Power sector context risk 24% 32%<br>é % C. Country context risk 31% 40%<br>+> 5ot 1. Ease of market entry | 30% | 935%<br>= 3 2. Clarity of investment priorities 17% 16%<br>a 2 3. Certainty of cash flow 51%<br>Fs5 3is} § x° 5 4.6 . SectoralSectoralFirm’s pers grtrack o nalwth recordtrack record ‘17%an[19% | 25%37%<br>7. Governance and political risk re, 70%<br>£3 8. Business environment 35% 16%<br>8 8 9. Macro-economic framework [18% 6%<br>10. Banking and capital markets 7% 4%<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br><!-- End of picture text -->
right hand side, CMF is China’s global market share for finished machinery of product j at time t-1, RCAC is the revealed comparative advantage index in components for country j (scale to 0 to 100) at time t-1. Finally, matrix W contains covariates such as dummy variables for industry, location, time, and China’s import tariff.

For equation (3) we reclassify our product into 3-digit SITC, broader than the classification used in equation (2). This relatively more aggregate category reduces the extreme variation in market share that one would obtain with a more detailed product category, which allows us to better examine the effect of spillover. In our sample, we also keep products that have components in their classification. Finally, because some countries do not engage in export of particular products, SCC is censored from below. Thus we estimate equation (3) using Tobit with zero as the lower censoring point.

We estimate equation (1) to (3) using pooled time series and cross product data. The time series spans 1992 to 2004, while the cross product varies depending on the estimated equation. Most of our trade data are from UN-Comtrade and the rest are obtained from the World Bank WDI database.

We summarize our results from estimating equation (1) to (3) in Table 9 and present the detailed results in Appendix A. Numbers in the 1st subcolumn of the 1st column in Table 9 represent estimates of coefficient α 2 of equation (1), which is components, while numbers in the 2nd subcolumn represent estimates of α 2 + α 3, i.e., the net effect of quantity on unit value of the finished product. Other columns present results from estimating equation (2) and (3). Rows in Table 9 represent the scope of sample in which equations (1) through (3) are estimated. Results that are statistically significant are given “(+)” or “(-)” sign, while “n.s” indicates those that are not.

The results of estimating equation (1) suggest two important findings. First, we find α 2 to be positive and statistically significant. This implies that China’s increase in the volume of exports in components is associated with increasing unit value of export of components from Southeast Asian countries. We find net coefficient for α 2 + α 3 is negative and statistically

> <sup>13</sup>
specifically, we test for the significance of adding the industry variable to the 1-digit occupation variable in a two-way multivariate analysis of variance (MANOVA) of averaged importance scores. The joint occupation-industry variable is highly significant indicating that it is informative to account for the industry of work beside a worker’s occupational group in characterizing the work activities carried out.<sup>5</sup> _Data set on exports._ In order to determine what activities in an economy are exported, information on the value added of a countries’ exports is needed. The industry composition of value added in exports is determined following the method outlined in Hummels et al. (2001) and Koopman et al. (2012). This is based not only on the value added in the exporting industry, but also on value added further up the production chain in other domestic industries delivering intermediates. Computation requires the use of national input-output tables that contain information on inter-industry deliveries, as outlined in Appendix A1. National input-output tables are taken from the Multiregional Input–Output table (MRIO) Database of the Asian Development Bank available for the year 2000 and annually for the period from 2007 to 2018. The industry classifications used in these tables were harmonized and mapped to a common set of 35 industries akin to the set of industries defined in the activities data.<sup>6</sup> Value added consists of compensation for workers (labor income) and a gross operating surplus (capital income) that accrues as income to the owners of capital assets. Capital assets cannot be straightforwardly allocated to activities, in contrast to workers. For example, a computer can be utilized in many activities and there is no information on its particular use. Data used for the baseline regressions only includes labor incomes. In in the task content of a particular occupation, suggesting that the task content of occupations is not necessarily constant across countries.

5 O*Net provides a list of 41 work activities with importance indicator scores on a scale of 0-100 for each 6-digit SOC code. We weight up the indicators to our activities using data from the US BEA Industry and occupation matrix for 2020. We establish the composition of O*NET 6-digit SOC codes in each of our activities, mapping from 6-digit SOC to our 13 occupational groups and from BEA 2-digit SIC to our industry groups. Next, we test for significant differences in the averaged importance scores across different activity pairs, weighting with the number of workers in each detailed SOC occupation. For ease of computation the 41 indicators are reduced to 10 principal components first, capturing 83% of the variation. Wilks’ test result for the joint occupation-industry variable is (F-value = 4.92 and p-statistic = 0.00), for the occupation dimension (F= 46.98, p=0.00) and for industry dimension (F=0.54 and p=1.00).

6 ADB MRIOTs are not available for the years 2001-2006. The MRIO database vintage used in this study was accessed in October 2021.

8
enough observations (many countries have only a few banks). Therefore, to estimate the degree of competition, we use the available data from Bankscope for the period 1987—1996, exploring the variation across banks and over time. We do investigate, however, the stability of the competition measure over this period.

When estimating _H_ , we include several control variables at the individual bank level— specifically, the ratio of the bank’s equity to total assets, the ratio of net loans to total assets, and the logarithm of total assets (in order to control for potential size effects). We estimate the model for each banking system using OLS with fixed bank-specific effects. We also include time dummies for each banking market and exclude countries with fewer than 20 bank-year observations. As a consequence, our sample reduces to 20 countries. We estimate _H_ , and the standard deviation of _H_ , and we check whether _H_ is significantly different from 0 or 1. In all cases, statistically _H_ is significantly different from 0 (at the 1% level). Only the banking system in Netherlands has an _H_ not statistically significantly different from 1 (perfect competition) at the 1% level (Norway as well, but at the 5% level). We also perform equilibrium tests and find most countries to be in equilibrium.<sup>8</sup> Alternatively, instead of the competition measure, we use data on the banking system concentration ratio (top three banks) of CG, in which case the specification is the same as theirs.

> 8 In this model, by replacing _P_ (the total revenues to total asset ratio) by the pretax return on assets ratio and then running the same regressions, we can conduct equilibrium tests.  We found most countries to be in equilibrium, i.e., the competition measures _H_ when using the pretax return on assets ratio as dependent variable is not significantly different from 0.  Appendix Table II provides standard deviations and significance tests of the individual _H_ - measures.

18