rafmacalaba/datause-encoder-data huggingface dataset:
transport and communications; financial intermediation; real estate and business activities; public administration and defense; education; health and social work; other service activities; and private households).

Information on socio-demographic characteristics, such as individuals’ age and education are included in the BLFS as well. This allows the construction of a variable for potential labor market experience. Information on an individual’s location ( _dzongkhag_ and urban or rural area) and his or her gender and nationality (Bhutanese or not) can also be used. Lastly, the questionnaire contains a series of questions on households’ wealth and exact demographic composition. For precise definitions of all variables used in the empirical analysis of Section 4, cf. Table 1.

# **3.2. Analytical Strategy and Econometric Model**

The microeconomic literature has used a wide range of methods to investigate wage differentials between public and private sector workers (cf. Lausev, 2014, for an overview). Among the most commonly used methods are “single equation” models and “double equation” models. Single equation models are basically wage regressions in the tradition of Mincer (1974) for the whole economy that include a public sector dummy variable. For double equation models separate wage regressions are run for the public and private sector samples. The main advantage of single equation models is that they make corrections for self-selection into labor force participation and specific sectors straightforward. In the context of analyzing wage differentials between public and private sector, controlling for such self-selection is of paramount importance. On the other hand, double equation models are more flexible and allow intercepts and returns to productive characteristics to differ across sectors. They can also be an intermediate step for counterfactual decompositions, i.e., for an investigation of the proportion of the differences in the average levels of hourly wages between public and private sector workers that is accounted for by differences in the level of individuals’ characteristics (the “characteristics effect”) and by differences in the impact on earnings of these characteristics (the “coefficients effect”) as originally suggested by Oaxaca (1973).<sup>3</sup>

> 3 The Oaxaca type decomposition was originally introduced to study racial or gender wage differentials. It has since been applied to many other areas.

- 9 -
- - <u>Table 5.5: Trade administrative costs in Japan: Paper based documentation versus Electronic Data Input based</u>

||Paper-<br>based|Cost (J Ye<br>EDI-based|n)<br>**Saving:**<br>**EDI-based**<br>**versus**<br>**Paper-based**<br>**(percentage)**|Paper-<br>based|Time (h<br>EDI-<br>based|ours)<br>**Saving:**<br>**EDI-based**<br>**versus**<br>**Paper-based**<br>**(percentage)**|
|---|---|---|---|---|---|---|
|Common|16,706|10,197|**39**|19.1|12.8|**33**|
|Special for agro-food products|9,864|7,884|**20**|4.2|3.7|**12**|
|Total|26,570|18,081|**32**|23.3|16.5|**29**|
|Source: OECD (2003)Quantitative<br>TD/TC/WP(2003)31/FINAL;<br>to Japan: Single Windows fo|Assessment of<br>based on JETR<br>r Trade and P|the Benefits o<br>O (Japan Exter<br>ort-related Proc|f Trade Facilitatio<br>nal Trade Organ<br>edures<br> (in Japan|n<br>, Paris, O<br>ization), 2<br>ese). Toky|ECD, docu<br>002.Repor<br>o.|ment<br>t on Market Access|

28
|Table 1. Residen|tial tariff sc<br>|hedules<br>|||<br>||||
|---|---|---|---|---|---|---|---|---|
||Tariff|Fixed chare|Fixed|Number|Size<br>of|Price first|Price|% increase|
||<br>type|g<br>per month?<br>Yes/No|<br>charge<br>($/month)|<br>of blocks|<br> <br>the<br>first<br>block,<br>kWh|,<br>block|,<br>highest<br>block|<br>from first<br>block to<br>highest<br>block|
|Benin|IBT|No|—|3|20|9.6|16.3|70|
|Botswana|Linear|Yes|1.63|1|—|5.9|5.9|—|
|Burkina Faso|IBT|Yes|1.1|3|50|18.4|20.8|13|
|Cameroon|IBT|No|—|3|50|8.6|12|40|
|Cape Verde|IBT|No|—|2|40|22.5|28|24|
|Chad|IBT|No|—|3|30|15.7|38.1|143|
|Congo,<br>Dem.<br>Rep. of|IBT|No||11|100|3.98|8.52|114|
|Congo,Rep. of|Linear|Yes|5.06|1|—|11|11|—|
|Côte d’Ivoire|IBT|Yes|0.64|2|40|6.9|14.2|106|
|Ethiopia|IBT|Yes|0.16–1.58|7|50|3.2|8|150|
|Ghana|IBT|Yes|0.54|3|300|7.6|15.3|101|
|Kenya|IBT|Yes|1.74|4|50|4.9|44|798|
|Lesotho|Linear|No|—|1|—|7.2|7.2|—|
|Madagascar|Linear|Yes|2.98|1|—|7.6|7.6|—|
|Malawi|IBT|Yes|0.92|3|30|2|4.1|105|
|MWI–prepaid|Linear|No|—|1|—|3.1|3.1|—|
|Mali|IBT|No|—|4|200|26.6|31|17|
|Mozambique|IBT|Yes|2.79|4|100|4|12.1|203|
|MOZ–prepaid|Linear|No|—|1|—|11|11|—|
|Namibia|Linear|No|—|1|—|11.7|11.7|—|
|Niger|Linear|Yes|0.43|1|—|13.6|13.6|—|
|Nigeria|IBT|Yes|0.15–2,38|5|20|0.9|6.5|622|
|Rwanda|Linear|No|—|1|—|14.6|14.6||
|Senegal|IBT|No|—|3|—|23.8|26.2|10|
|South Africa|IBT|No|—|2|50|0|7.2|—|
|Tanzania|Linear|Yes|1.93|1|—|10.8|10.8|—|
|TZN–low use|IBT|No|—|2|50|4.1|13|217|
|Uganda|IBT|Yes|1.1|2|15|3.4|23.3|585|
|Zambia|IBT|Yes|1.3|3|300|1.6|3.7|131|
|Zimbabwe|IBT|No|—|3|50|0.60|13.5|2,159|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 2. MWI = Malawi; MOZ = Mozambique; TZN = Tanzania. — Not available.

The structure of blocks—their number, size, and respective price levels—is what ultimately defines how tariffs reflect costs, affect demand, and address equity issues. The sample countries have adopted widely differing approaches to tariff design, with one third of countries practicing linear tariffs (as in Rwanda and Tanzania) and the remaining two thirds practicing IBTs.

Looking at the two thirds of sample countries that practice IBTs, most have adopted relatively simple structures (figure 2a) with two or three blocks (as in Benin, Burkina Faso, Cameroon, Cape Verde, Côte d’Ivoire, Ghana, Madagascar, Malawi, Senegal, Tanzania, Uganda, Zambia). A minority of countries have opted for more complicated structures that use four and more blocks (the Democratic Republic of 6
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or assuming P(y0 > V0) is positive,<sup>8</sup> an alternative expression rewritten based on Bayes’ theorem Equality (1b) lends itself to straightforward estimation using household panel survey data, where the denominator can be estimated from the cross section in time 0, and the numerator from the panel data spanning both time 0 and time 1. Given appropriate adjustments for inflation rates, the vulnerability line in time 0 can then be updated for later periods just as with poverty lines.

Some analogy can be highlighted between the familiar poverty line and this vulnerability line. First, just as a poverty line can be constructed anchored to a benchmark (e.g., level of energy or median household consumption), a vulnerability line can be constructed given a specific value for the insecurity index _P_<sup>_1_</sup> (say, 5 or 10 percent). Second, a lower value for the insecurity index is desirable and implies that a lower proportion of the population designated as “secure” is at risk of falling into poverty.

However, a major difference between this vulnerability line and the poverty line is that the former is constructed using a dynamic poverty framework while the latter a static one; another is that this vulnerability line is defined to be used at the population level for population-averaged quantity rather than at the household level. Put differently, the construction of vulnerability lines is a two-step process. In the first step, (absolute) poverty lines are constructed (in practice, they are often linked to notions of minimum nutritional requirements). Then in the second step, these

> 8 This assumption is generally satisfied in practice as long as V0 is less than the maximum value of household consumptions in time 0. Despite its deceptively straightforward formula, this framework can provide a usefully wieldy tool for analysis. An example of its application (usually with an additional assumption of bivariate normality) is a topic known as the “screening problem” in the statistics literature on quality control. This problem exists in situations where the performance of an individual (or quality of a product) on an immediate test is correlated with a future performance and the former is easy to measure while the latter is not. See, for example, Owen (1988) or Tang and Tang (1994) for brief overviews.

8
# **Table A4: Panel A – Multinomial Regressions, 2006**

Log Odds ratio between Renters and Mortgage holders (Marginal Effects)

||||||||||**Hausman F-**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**parttime**|**unemp**|**retired**|**urban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|**Statistic**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-0.0270|0.0634|0.0116|0.1109 ***||0.0083||0.0966 ***|0.97|0.26|
|Estonia|-0.0244|-0.0280|0.0318|-0.0146|0.0630|-0.0159|0.0216|0.0323|1888.07 ***|0.18|
|Hungary|-0.0524 **|0.0146|0.0436 **|0.0659 ***|-0.0610|-0.0223|-0.0203|0.0010|1.86 **|0.13|
|Latvia|-0.0407|-0.0624 *|-0.0862 ***|-0.0448 ***|0.1418|0.1140|-0.0357|-0.0488|1.05|0.07|
|Lithuania||-0.0227||-0.0233 **||-0.0223||0.0047|1.03|0.24|
|Poland|-0.0253|0.0976 **|0.0156|0.2000 ***|0.0153|0.0737|0.0158|0.0445|2.99 ***|0.32|
|Slovak Republic|-0.0562 **|-0.0521 **|-0.0346|0.0172|0.4221 **|0.3552 *|-0.1178|-0.1358|1.25|0.29|
|Slovenia|0.1746 *|0.1794 **|0.1637 ***||-0.1311 ***|-0.1968 ***|-0.0524 ***|-0.0361|1.57 *|0.10|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.0906|0.0913|0.0584|0.1479 ***|1.1195|0.9986|1.7892 **|1.8079 **|3.86 ***|0.24|
|Belgium|-0.1624 ***|-0.0823 **|-0.0661|0.0572 ***|-0.2079|-0.3132 **|0.0167|-0.0010|1.73 **|0.35|
|Cyprus|0.0877|0.0876|0.1964 ***|0.0866 ***|-0.3356 ***|-0.2513 **|-0.0565|-0.0310|2.18 ***|0.16|
|Denmark|0.0992|0.0338|0.0927 *|-0.0702 ***||-0.0351||0.0210||0.37|
|Finland|-0.0834 **|0.0931 *|-0.0655 *|0.0919 ***|-0.3296 **|-0.4673 ***|0.0110|0.0062|2.93 ***|0.31|
|France|-0.1209 **|0.0393|-0.0779|0.0092|-0.2911 ***|-0.2854 **|-0.0628|-0.1220 **|1.62 **|0.32|
|Greece|0.0330|0.0591|-0.0559 *|0.0662 ***|-0.0267|0.0794|-0.0988 ***|-0.0872 ***|1.10|0.14|
|Ireland|-0.0464|-0.0403|-0.0406|0.0969 ***|-0.0922|-0.2177 **|-0.0174|-0.0025|2.59 ***|0.28|
|Italy|0.0570 *|0.1120 ***|-0.0464 ***|0.0732 ***|0.1182 ***|0.0594|-0.0133|-0.0325|3.32 ***|0.13|
|Luxembourg|-0.1749 ***|0.0173|0.0014|-0.0399|-0.0903|-0.0439|-0.0916|0.1169 *|0.96|0.35|
|Netherlands|0.0950 ***|-0.0003|0.0657 **||-0.1629|0.0180|0.2239 ***|0.2162 ***||0.31|
|Portugal|-0.1100 *|-0.0905|-0.0471|0.1604 ***|0.0456|0.0779|-0.0370|-0.1589 ***|1.66 **|0.19|
|Spain|0.1007 **|0.0515|0.0494 *|-0.0063|0.0184|-0.0522|0.0254|0.0041|2.31 ***|0.14|
|Sweden|-0.0747 **|0.0627|-0.1424 ***|-0.0663 ***|0.1991|0.1729|-0.0082|0.0365|1.44 *|0.33|
|United Kingdom|-0.2297 ***|-0.0308|-0.0458 *|-0.0272 *|0.3641 ***||-0.0963 ***|-0.1390 ***|13.4 ***|0.33|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2006 32
formalized, it is useful to introduce some notation for the classification of recipients/nonrecipients as poor/nonpoor, as in Table 2.

Each household who participates in the program receives a sum G. The targeting differential (T in equation 4) is the difference between the average amount going to the poor _(nfl IG/n )_ and that going to the nonpoor _(nl2Gn_ **_02)._** When expressed as a ratio to G, we will refer to _TIG_ as the "targeting coefficient": This is simply the difference between the fraction of the poor who receive the program and the fraction of the nonpoor who do so. The targeting coefficient lies between -1 and +1. If the program is perfectly targeted to the poor (nl2= n21=O) then _TIG=l;_ if the program is perfectly targeted to the nonpoor (nil= n22=O) then T/G=-1; a uniformn ("untargeted") allocation _(n1 1l/nj=nl2 1 n0 )_ implies that TIG=O.

The targeting coefficient is a measure of association for the 2x2 contingency table in Table 2. It is related to the "phi coefficient', a common statistic of association in a 2x2 contingency table, and here given by: (The necessary and sufficient condition for _TIG_ = 0 is that (nlu - n2 2)( nl2- n2l) = 0.) The phi. coefficient is related to the standard chi-square test statistic for the test of independence in a contingency table; in particular N02 X2. This provides a basis for statistical inference about targeting performance.

In a decentralized program, as in our case, the targeting performance can be analyzed beyond the overall national level. As out theoretical model suggests, the final allocation is the outcome of different layers of decision-making. The performance in reaching poor families _15_
- 16 -

_Proposition 9. When two public tasks exhibit weak, negative externalities, unbundling is optimal. Moreover, public ownership would be superior to fragmented privatization as the duration of contracts becomes longer_ .

In the real world, there are wide variations in PPP transactions, but there is a systematic trend reflecting the nature of individual infrastructure sectors (Table 4). The majority of PPP transactions take a form of greenfield projects where private firms or public-private joint ventures build and operate a new facility for the period specified in the project contract. This is a part of concessions in Table 3 and includes build, lease, and transfer (BLT),<sup>8</sup> build, operate, and transfer (BOT),<sup>9</sup> build, own, and operate (BOO),<sup>10</sup> merchant,<sup>11</sup> and rental.<sup>12</sup> Concessions are also a common mode of PPP transactions in particular for transport and electricity utilities. In the telecommunications sector, in which commercial investments are becoming increasingly feasible in recent years, divestiture is also an important tool of private sector involvement. In total, more than 90 percent of PPP transactions concluded between 1990 and 2007 aimed at integrating investment and operation under the greenfield, concession or divestiture framework.

<u>Table 4. Number of PPP transactions between 1990 and 2007</u>

||Energy|Telecom|Transport|Water &<br>sewerage|Total|
|---|---|---|---|---|---|
|Management &|35|5|62|104|206|
|lease contract||||||
|Greenfield project|971|589|376|226|2162|
|Concession|103|10|594|241|948|
|Divestiture|478|203|65|26|772|
|Total|1587|807|1097|597|4088|

Source: PPI database.

8 A private sponsor builds a new facility largely at its own risk, transfers ownership to the government, leases the facility from the government and operates it at its own risk up to the expiry of the lease.

9 A private sponsor builds a new facility at its own risk, operates the facility at its own risk, and then transfers the facility to the government at the end of the contract period. The private sponsor may or may not have the ownership of the assets during the contract period.

10 A private sponsor builds a new facility at its own risk, then owns and operates the facility at its own risk.

11 A private sponsor builds a new facility in a liberalized market in which the government provides no revenue guarantees. The private developer assumes construction, operating, and market risk for the project.

12 Electricity utilities or governments rent mobile power plants from private sponsors for periods ranging from 1 year to 15 years. A private sponsor places a new facility at its own risk, owns and operates the facility at its own risk during the contract period.
**Table A1 Applied MFN tariffs in Russia in 2006-2020**

|**Year**|**Number of**||**Simple a**|**verage**|||**Weighted**|**average**||
|---|---|---|---|---|---|---|---|---|---|
||**product**|**Ad valo**|**rem rate**|**Applied **|**MFN rate**|**Ad valo**|**rem rate**|**Applied **|**MFN rate**|
||**lines**|**Mean**|**Standard**<br>**deviation**|**Mean**|**Standard**<br>**deviation**|**Mean**|**Standard**<br>**deviation**|**Mean**|**Standard**<br>**deviation**|
|**2006**|10,875|10.67|8.52|11.67|11.31|11.75|9.45|13.71|12.18|
|**2007**|11,001|10.29|8.57|11.36|11.28|10.10|8.92|11.66|11.19|
|**2008**|11,057|10.41|8.67|11.25|10.77|10.85|10.28|13.39|22.68|
|**2009**|11,067|10.87|10.00|11.85|13.18|9.88|9.40|11.62|16.64|
|**2010**|11,051|10.06|10.05|11.09|13.46|9.09|9.04|11.02|17.64|
|**2011**|11,125|10.11|10.20|11.55|14.80|9.66|9.96|12.01|20.77|
|**2012**|11,557|10.06|9.61|10.88|10.68|8.27|7.41|9.05|8.11|
|**2013**|11,557|9.24|9.30|10.01|10.30|7.29|7.11|8.02|7.83|
|**2014**|11,557|8.48|9.10|9.19|10.02|6.60|6.81|7.29|7.55|
|**2015**|11,557|7.67|9.05|8.41|9.87|5.86|6.67|6.56|7.36|
|**2016**|11,557|7.39|8.98|8.12|9.78|5.52|6.30|6.08|6.64|
|**2017**|11,557|7.30|8.94|8.01|9.73|5.30|5.89|5.82|6.18|
|**2018**|11,557|7.28|8.92|7.98|9.71|5.14|5.54|5.65|5.85|
|**2019**|11,557|7.27|8.91|7.98|9.70|5.05|5.36|5.56|5.68|
|**2020**|11,557|7.20|8.59|7.90|9.40|4.97|4.77|5.48|5.14|

Note: Table presents main summary statistics of the Russian tariffs computed for products at ten-digit level of the Russian tariff classification. An ad valorem rate ignores the specific component of the tariff. An applied MFN rate includes both ad valorem and specific components of the tariff. Specific component of the tariff is translated into the ad valorem equivalent according to the formula that is explained in the methodology section and using import statistics of Russia in 2006-2010. For 2011-2020, the ad valorem equivalent is computed using import data of 2009-2011. Weighted averages are computed using weights computed as the share of the product line _h_ in the total import at time _t_ .

**Table A2.  Calculation of the Russian Simple Average Tariff on Matched Tariff Lines only in 2011-2020.**

||**Number of matched tariff lines**|**un-weighted average tariff**|**standard deviation**|
|---|---|---|---|
|2011|10,493|10.5|12.8|
|2012|11,210|10.6|10.3|
|2013|11,210|9.7|9.9|
|2014|11,210|8.9|9.6|
|2015|11,210|8.1|9.5|
|2016|11,210|7.9|9.4|
|2017|11,210|7.8|9.3|
|2018|11,210|7.7|9.3|
|2019|11,210|7.7|9.3|
|2020|11,210|7.6|9.0|

# Source: Authors' estimates

Note: Comparison with table 1 shows that for 2012-2020, the simple average calculated on matched tariff lines only is about 0.3 percentage points lower than the simple average calculated on the full set of tariff lines.

32
about half of the countries in the world (Table 4). In many countries there are also non-bank regulatory authorities that may supervise or regulate (to varying degrees) cooperatives and credit unions, finance companies, MFIs, etc.

The patterns of supervision documented in Table 4 have consequences for the response rates for different data elements in the survey. Financial regulators tend to collect and publish data on the institutions they supervise and the data tend to reflect their policy priorities. This pattern shows clearly in Table 5, where volumes of deposits and loans; and data on branches and other forms of physical outreach are relatively available, the data on numbers of deposits and loans and numbers of individuals are relatively less populated. Volume data comes from balance sheets and is reported regularly for monitoring purposes and many countries require registering bank branches with the authorities. The collection of data relevant to financial inclusion is rarer, however, and is often lower priority and done on an ad hoc basis.<sup>12</sup>

# **_3.1 Data Available on Mutually Owned Financial Institutions (MOFIs)_**

Even when financial cooperatives are regulated, few countries are able to provide financial access relevant data on them. In countries where cooperatives are supervised by the main financial regulator, less than 60 percent have data on the values of deposits and loans, and just a third on the number of accounts and loans. There is a significant difference in data availability by region. Latin America has the best data coverage with 80 percent of countries collecting data on values and 60 percent on numbers of loans and deposits. Sub Saharan Africa faces the greatest challenge in collecting data on cooperatives. In the region, only 20% of countries where cooperatives were regulated by the financial authority, had data on the number of deposits and loans. Many cooperatives are small and many of them do not have an MIS or even a computer, which likely accounts for some of this trend.

> 12 Many respondents reported collection the data on numbers of loans and deposit accounts in response to our request. In contrast, in many developed countries information on financial inclusion is collected through household surveys.

12
Similarly, in Kasargod (KL) a discussant points out a fundamental flaw in the housing allotment policy. This policy, which allocates house-building grants to those who do not possess a shelter, ironically overlooks landless people.

_Ward Member (male):_ There are many defects in selecting beneficiaries. Usually, we give houses and toilets to persons who have land. If we select a person who doesn't have land, then we can't give them a house. Even if people say that they’ll own land after partition [of ancestral land], it’s still not possible. Only if you own land now, you can be granted a house.

These examples show how GP presidents and ward members struggle over the definition of who is poor. This struggle arises not only from differences in opinion between Government and the public but also from a lack of consensus regarding the meaning of poverty within different arms of Government.

Discussions of this nature prepare the poor to question governmental techniques and tabulations and exercise vigilance over who gets included in the list of beneficiaries. For instance, the example below from Dharmapuri (TN) shows villagers critiquing the census data produced by the government.

_Villager (BC):_ Our calculation is correct. The village people took that [census]. But the census taken by the government is not proper. It differs. So the ward members should look into it and add the beneficiaries...

_Villager (SC):_ This Palani [name of a villager] is rich. But he’s been added in the BPL list. How’s this possible? This BPL list is wrong.

These discursive exchanges around the BPL category thus play a vital role in creating a mutual recognition between the government and the public about what is required to be classified as a deserving beneficiary. These exchanges complicate the State’s attempt at defining poverty as a Cartesian category, precisely estimated and classified via commensuration, by debating the process and exposing its loopholes. This exercise of debating definitions acts as a training ground for the poor and disadvantaged, who have long been excluded from such fundamentally important debates and decisions.

32