rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Datt-Ravallion Decompositions, 2002-2012/13 (percent of poverty change) <!-- Start of picture text --> 200%<br>150%<br>100%<br>163%<br>119% 125%<br>50%<br>0%<br>-19% -25%<br>-63%<br>-50%<br>-100%<br>National US$1.25 a day US$2.50 a day<br>Growth Redistribution<br><!-- End of picture text --> Source: Own estimates based on comparable regions in HIES 2002, 2009/10 and 2012/13.

# Figure 4: <u>Sri Lanka. Population Structure</u>

<!-- Start of picture text --> 2001 2012<br>75+ 75+<br>70-74 Female 70-74 Female<br>65-69 Male 65-69 Male<br>60-64 60-64<br>55-59 55-59<br>50-54 50-54<br>45-49 45-49<br>40-44 40-44<br>35-39 35-39<br>30-34 30-34<br>25-29 25-29<br>20-24 20-24<br>15-19 15-19<br>11-14 11-14<br>5-10 5-10<br>0-4 0-4<br>1,000,000 500,000 0 500,000 1,000,000 1,000,000 500,000 0 500,000 1,000,000<br><!-- End of picture text --> Source: Sri Lanka Department of Census and Statistics. Census of Population and Housing 2001 and 2012 provisional results based on 5% sample.

Note: 2001 and 2012 are the last two years of Census available.

17
# **I. Introduction**

Women’s participation in the labor force has increased significantly throughout the world over the past 25 years (Duflo _,_ 2012). Despite this, research shows that systematic gender gaps in wages and productivity persist across both developed and developing countries (Blau and Kahn, 2000; Jarrell and Stanley, 2003; Terrell, 1992). Ensuring parity in male-female labor outcomes is not only a matter of equity but also one of smart economics and efficiency (Doepke, Tertilt, and Voena, 2014; Duflo, 2012). The 2012 World Development Report, _Gender Equality and Development,_ notes that eliminating gender discrimination against female workers could increase productivity by 25 to 40 percent (World Bank, 2012). Moreover, removing barriers faced by women in the labor market has implications for the next generation as well as for the inclusivity and representativeness of institutions and development policies (Hallward-Dreimeier and Gajigo, 2013).

Gender wage discrimination is a well-researched area and one that has been examined using a variety of estimation techniques. Previous studies on this topic have used various worker characteristics such as age, experience, education, occupation, and industry of employment in predicting male-female wages.<sup>4</sup> However, while recent research in labor economics has documented the critical role of skills, broadly defined, in predicting key life outcomes, including the associations between skills and earnings (Hanushek et al., 2013; Heckman and Kautz, 2011), the mechanisms through which cognitive and noncognitive skills are related to wages have received limited attention,<sup>5</sup> and most of the research has been conducted in countries belonging to the Organisation for Economic Co-operation and Development (OECD). Further, few studies have applied these skills measures to examine earnings differences between men and women.

This study aims to conduct a comparative analysis across seven low- and middle-income countries, examining the gender wage gap in these countries using data from the STEP Skills Measurement surveys. It is the first study to use measures of cognitive and noncognitive skills in wage decomposition analysis in this set of countries. The multi-dimensional conceptualization of skills in the STEP survey data offers a broader definition of human capital and thereby allows us to explore the potential role of other dimensions of human capital in understanding the gender wage gap.

> 4 Additionally, the structure of the labor market and women’s legal (property) rights have also been examined in gender wage discrimination research (Blau and Kahn, 2003; Doepke, Tertilt, and Voena, 2011).

> 5 Mueller and Plug (2006) hypothesize that personality can either directly (through productivity differences) or indirectly (through occupational segregation) affect gender differences in earnings. Further, personality differences can also affect occupational choices and thus lead to differences in wages.

4
- 15 -

The Planner will choose to produce the planned output and pay a wage Just exactly suffiLient to keep the Workers from delivering zero effort. The Dictatorial solutlon can hence be interpreted as a solution where the Planner maxLmizes the utility Up glven that the WorkersI utility is squeezed as much as possible.

We now return to the monitoring problem. How can the Planner be sure thet the Workers will actually deliver the agreed upon effort, Zw,,, for the DictatorLal solution? The only thing the Planner can monitor is the actual production. To avoid the Workers receiving the payment W' but delivering, for example, no effort, the Planner pays the Workers according to the pLece-rate system described above. The Planner chooses a piece-rate a such that the sum of the base wage and the piece-rate payment equals the agreed wage W; Wb.+*Zp4,,, - W1 (3.81 This implementatione of the Dictatorial solution is illustrated in flgure 3.2. The Planner will choose a piece-rate **_0a_** such that the 1ine W - Wb+OZ passes through the point D.

<!-- Start of picture text --> FLgure 3.2<br><!-- End of picture text --> <!-- Start of picture text --> u(O)+v(W>./P)<br>z-J,<br><!-- End of picture text --> <!-- Start of picture text --> zpw<br><!-- End of picture text --> The Dictatorial solution implemented via a piece-rate system (given by the equations [3.5], [3.6], and [3.7]) is, however, unstable. In this set-up the Workers de-ive nothing from the bargain yet have the possibility of reneging on the outcome 3. Once the Workers realize that _(a_ ,Zp1.) from equations [3.6](3.8] is the outcome of the bargain they can react by playing the game in a noncooperative manner. This means that, given the piece-rate _a_ the Workers will S The piece-rate is _a'_ - (W'-Wtw)/Z&,.

X Note that wb> is exogenous and predetermined. If this was not the case the Planner could always adjust the base wage so as to make whatever combinatlon of wage and productivity level a stable solution.
- 13 -

characteristics, and an analysis of dispersion of MWRs. The next section compares MWRs for annuitants in Chile with those estimated for annuitants in other countries, computed both with the risk-free rate and a higher discount rate.

|**Table 4: Summary**|**Statistics**|**of Datase**|**t by Subgr**|**oups**||
|---|---|---|---|---|---|
||**1999**|**2002**|**2003**|**2004**|**2005**|
|_Single Life – Males_||||||
|<br>Number|82|139|114|144|108|
|Average Age of Males|59.22|57.49|57.81|58.13|59.74|
|Average Purchase Price (UF)|1,475.85|1678.00|1,544.60|1,631.88|1973.34|
|Number of cases with deferment|7|22|14|22|25|
||(8.5%)|(15.8%)|(12.3%)|(15.3%)|(23.1%)|
|O/w: - 12 months|6|16|12|17|22|
|- 24 months|1|6|1|5|2|
|- 36 months and longer|0|0|1|0|1|
|<br>Number of cases with a guaranteed term|52|102|85|101|75|
||(63.4%)|(73.4%)|(74.6%)|(70.1%)|(69.4%)|
|O/w: - 5 years|0|5|4|7|7|
|- 10 years|39|68|56|52|41|
|<br>- 15 years|10|19|18|27|17|
|<br>- 20 years andlonger|1|10|7|15|8|
|_Single Life – Females_||||||
|Number|185|309|256|373|520|
|Average Age of Females|57.89|56.46|57.51|58.66|60.99|
|<br>Average Purchase Price (UF)|1,779.28|1,619.47|1,984.87|2,007.26|2,187.79|
|<br>Number of cases with deferment|44|69|71|113|175|
||(23.8%)|(22.3%)|(27.7%)|(30.3%)|(33.7%)|
|O/w: - 12 months|<br>37|<br>57|<br>56|<br>81|<br>132|
|- 24 months|7|12|12|27|38|
|- 36 months and longer|0|0|3|5|5|
|Number of cases with a guaranteed term|151|250|208|310|416|
||(81.6%)|(80.9%)|(81.3%)|(83.1%)|(80.0%)|
|O/w: - 5 years|1|3|2|5|8|
|- 10 years|89|149|120|175|217|
|- 15 years|53|82|70|104|138|
|<br>- 20 years andlonger|5|16|16|26|41|
|_Joint Life_||||||
|Number|670|1,069|823|973|763|
|Average Age of Males|57.66|56.92|57.77|57.64|59.42|
|Average Age of Females|55.17|54.39|54.94|55.01|56.73|
|Average Age difference (male age less|2.49|2.53|2.83|2.62|2.69|
|<br>female age) in years||||||
|<br>Average Purchase Price (UF)|2,085.47|1952.69|2237.30|2202.07|2705.19|
|Number of cases with deferment|148|240|222|274|219|
||(22.1%)|(22.5%)|(27.0%)|(28.2%)|(28.7%)|
|O/w: - 12 months|<br>121|<br>202|<br>170|<br>224|<br>161|
|- 24 months|24|36|47|43|51|
|- 36 months and longer|3|2|5|7|7|
|<br>Number of cases with a guaranteed term|504|839|655|742|602|
||(75.2%)|(78.4%)|(79.6%)|(76.3%)|(78.9%)|
|O/w: - 5 years|<br>9|<br>11|<br>11|<br>6|<br>8|
|<br>- 10 years|293|484|335|409|301|
|- 15 years|181|286|247|249|198|
|- 20 years andlonger|14|58|62|78|75|

Source: SVS and Staff Analysis
# **Box 1: The Euro‐area debt crisis and the sovereign‐bank nexus**

# **Context**

The Euro‐area debt crisis led to financial market fragmentation and revealed the flaws of an incomplete currency union in which responsibility for financial stability remains _de facto_ national. The 2010‐12 European debt crisis erupted before the Euro area could recover from the global financial crisis and was characterized by overly high government structural deficits and accelerating public debt levels. During 2012‐16, the average consolidated public debt to GDP ratio of the EU‐28 slightly decreased from 83.9% to 83.3% and subsequently 81.6 % of GDP in 2017. The Euro area also faced a wave of defaults by property developers and homeowners. Loan defaults, structural revenue shortfalls, and the currency union’s incomplete architecture left many Euro area economies with the challenges of managing liquidity risk, establishing fiscal sustainability, and addressing a tightening the sovereign – bank nexus.

To strengthen their balance sheets, EU banks had received guidance to improve their risk‐ weighted capital ratios. This was perhaps a contributing factor for banks to increase their sovereign debt holdings further. Despite having passed stress tests conducted by the Committee of European Banking Supervisors, banks in some countries continued to struggle with large impaired assets and nonperforming loans. Concerns also emerged that the stress tests did not fully incorporate sovereign default risk. Based on data from the European Banking Authority, sovereign exposures of large banks in the Euro area: i) did not change substantially between end‐2013 and mid‐2015; ii) banks still invest more than their own funds in sovereign debt; and iii) banks’ sovereign debt holdings exhibit a strong home bias, particularly in the southern European countries and in Germany.

# **Crisis management**

Crisis responses include: _ECB liquidity and credit support_ through traditional and unconventional facilities aimed at avoiding market dislocations; _European Financial Stability Facility_ (EFSF) was created to assist Euro area sovereigns for the resolution of difficulties in their banking systems; _The Outright Monetary Transactions (OMTs)_ framework of the ECB was designed to address distortions in sovereign bond markets and thereby increase the effectiveness of monetary policy; the creation of the _European Stability Mechanism (ESM)_ firewall fund; and _official loan maturities to program countries_ (e.g., Greece and Cyprus).

# **Towards a banking union**

The crisis spurred efforts to build a banking union with a common supervisory framework, a pan‐European deposit guarantee scheme, and a bank resolution authority to break the adverse loops between sovereigns and banks and protect taxpayers. These efforts have resulted in, _inter alia_ , the adoption of the Bank Recovery and Resolution Directive (BRRD) and the Single Resolution Mechanism Regulation (SRMR), the Single Supervisory Mechanism (SSM) and the 8
Table 3: Frequency Tabulation of Best Performing Methods: sMAPE criterion

|Mdl|5 year f|orecast horizon|10 year|forecast horizon|
|---|---|---|---|---|
|oe|Count|Frequency|Count|Frequency|
|VAR3 / VEC3|15|14.15%|30|28.57%|
|VAR2 / VEC2|21|19.81%|20|19.03%|
|GARCH|39|36.79%|34|32.35%|
|ARIMA|13|12.25%|9|8.55%|
|HOLT-WINTERS|6|5.66%|8|7.62%|
|UCM-RWD|3|2.83%|2|1.90%|
|UCM-RWC|9|8.49%|2|1.90%|
|Total|106|100%|105|100%|

Table 4: Frequency Tabulation of Best Performing Methods: RMSE criterion

|Mdl|5 year f|orecast horizon|10 year|forecast horizon|
|---|---|---|---|---|
|oe|Count|Frequency|Count|Frequency|
|VAR3 / VEC3|15|14.15%|33|31.41%|
|VAR2 / VEC2|23|21.69%|19|18.09%|
|GARCH|29|27.35%|33|31.41%|
|ARIMA|17|16.02%|7|6.65%|
|HOLT-WINTERS|7|6.60%|10|9.52%|
|UCM-RWD|5|4.72%|2|1.90%|
|UCM-LLTM|1|0.94%|0|0.00%|
|UCM-RWC|9|8.49%|1|0.95%|
|Total|106|100%|105|100%|

Tables 5 and 6 show how well the forecasting methods perform compared to benchmark models, Näıve and Näıve2. For each forecast horizon, these tables report the median percentage better measure (see subsection 2.4) as well as the percentage of times the difference between the forecast errors is statistically significant based on the Diebold and Mariano (1995) forecast accuracy test.

Table 5 compares the accuracy of forecasting methods relative to the Näıve model, which assumes that electricity demand is a random walk. We see that the best performing model based on sMAPE criterion yields considerable improvement over Näıve model. The median sMAPE forecast error of the Näıve model is 77 percent higher than forecast error of the best performing model over the 5-year forecast horizon and 74 percent higher over the 10 year forecast 11
<!-- Start of picture text --> Figure 17 – Land Cover Classes and Distance from City Center ‐ Dakar<br><!-- End of picture text --> <!-- Start of picture text --> Dakar Land Cover 2015 (Percent) Dakar Land Cover 2015 (Total)<br>100% 140<br>120<br>80%<br>100<br>60% 80<br>60<br>40%<br>40<br>20%<br>20<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance to CBD (in Kilometers) Distance to CBD (in Kilometers)<br>Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Percent Land cover (sq km) Total Land cover (sq km)<br><!-- End of picture text -->

# **Box 2– Manual digitizing vs semi‐automated approach in Dakar**

Traditionally planners have relied upon manual digitizing satellite imagery to create acurate city level maps. While usually this produced very precise maps, this approach remains tedious and time intensive. A semi‐automated approach presented in this study could be a time saving alternative. The screenshots below highlight the discrepencies and similaries between a land classification generated from a semi‐automated approach and a land cover map that was manually digitized. On the left hand side are the layers generated by a semi‐automated classification. The layer has been given a slight transparecy so that the 2015 image beneath it can be seen. On the right hand side is that same area, but with a classified layer that has been manually digitized from a 2008 image. In all the maps, purple areas represent commerical/industrial land, while the yellow represents residental land, green is vegetation, beige is bare soil. The semi‐automated approach clearly picks up the vegetation more accurately, and is able to identify small patches of trees. On the other hand, it appears slightly more gridded with pockets of residential within the commerical, and some minor roads being missed. In contrast the manual approach has cleaner lines that are generalized to follow roads. Overall though, it is important to note the large amount of agreement between these two layers. In both approaches the industrial area is clearly being acurately identified and the residental land is almost identical. Regardless of the methodology, it is clear the outputs are telling the same story.

27
<!-- Start of picture text --> Dynamic Efects of Tariff Reform. Im 0. O Dynamic Effects of Tariff Relrnm tm 010<br>On Employment On Capital Stocks<br>RAIIO RATIO<br>3.03 ~~~~~~~~~~~~~~~~~~~~~~~1.04<br>a.02 1.0<br>3030 serice 10<br>.00 I------ 3.00- . - . .<br>0 99 ... ,. ,,..,O,_____-----<br>099 I | * W W z | * | * | W 099<br>Gsa ...- Uonufor~~~~~~~~~~~~~~~~~~~ ~~~~0.98a<br>0 9 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 0 9 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>0 2 4 6 0 10 n2 34 16 i8 20 0 2 4 e 8 30 12 14 t6 a 20<br>TIME PERIODS TIlE PERIODS<br>Dynamic Effects of Tanif Refor. tma * 0. 10 Dynamic Effects of Tanr Weform. tm -0 10<br>On Labor Income On Cpi ta Income<br>RATIO RATIO<br>3.03 3 03<br>Seaces<br>3 2.. .. - - - - - - 102 sene- -0 -tvc<br>- =<br>IP 0== I 5k1<br>1.01 PrIn<br>1. 00 ------------ ------------ ------------ ------------ ------------<br>os9 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~10 --------------------------- _._ ._------------<br>0 989 099 Monu~~~~~~~~~~ ~ ~~~gc- ~ ~ ~ ~ 09 9 4 t<br>0 97 0 so<br>.: 4 o .i i., 31 14 3, '8 o8 '0 4 a 14 3 14 16 38 .'0<br>1imE PERIODS IIME PERIODS<br><!-- End of picture text -->
**36**

# 5.4: **GDP consequences of Tariff Changes by 2011** – percent deviation from baseline

||||Source|of Tariff Ch|ange|||||
|---|---|---|---|---|---|---|---|---|---|
||Global|USA|Japan|Europe<sup>1</sup>|OOECD<sup>2</sup>|China|India|EEFSU|ODCs<sup>3</sup>|
|United States|-1.28|-0.28|-0.05|-0.25|-0.33|-0.04|-0.01|0.01|-0.33|
|Japan|-1.69|-0.36|-0.65|-0.15|-0.02|-0.12|0.00|0.02|-0.41|
|United Kingdom|-2.12|-0.25|-0.02|-1.48|-0.18|-0.02|-0.01|-0.06|-0.09|
|Germany|-3.80|-0.33|-0.04|-2.64|-0.26|-0.04|-0.01|-0.39|-0.08|
|Euro Area|-2.93|-0.30|-0.05|-1.84|-0.18|-0.03|-0.01|-0.24|-0.26|
|Canada|-2.20|-1.76|-0.04|-0.27|-0.21|-0.02|-0.01|0.02|0.08|
|Australia|-1.36|-0.34|-0.15|-0.32|-0.17|-0.09|-0.02|0.03|-0.30|
|ROECD|-3.74|-0.37|-0.06|-1.74|-1.21|-0.03|-0.01|-0.19|-0.11|
|China|-4.26|-0.93|-0.29|-0.29|0.05|-1.12|-0.01|-0.09|-1.58|
|India|-1.55|-0.20|-0.03|-0.22|0.01|-0.03|-0.61|0.01|-0.47|
|Other Asia|-3.86|-0.98|-0.18|-0.32|-0.03|-0.42|-0.02|-0.02|-1.88|
|Latin America|-1.63|-1.32|-0.03|-0.26|0.12|-0.03|-0.01|-0.04|-0.06|
|Other LDC|-1.43|-0.54|-0.02|-0.60|0.05|-0.04|-0.04|-0.15|-0.09|
|EEFSU|-3.54|-0.61|-0.05|-2.02|-0.08|-0.07|-0.01|-0.62|-0.08|
|OPEC|-4.45|-0.90|-0.37|-1.00|-0.20|-0.16|-0.06|-0.12|-1.63|

Notes: Source G-Cubed Model;

1. Europe is UK, Germany & Euro Area;

- 2.OOECD is Canada, Australia and ROECD;

3. ODCs is Other Asia, Latin America, Other LDC and OPEC
|**City or Province**|**Total Budget**<br>**(USD/year) (millions)**|**Population**|
|---|---|---|
|Hubei Province|$5.5|58,500,000|
|Yunan Province|$3|48,300,000|
|SipingCity|$0.2|594,000|
|ChuxiongYi Prefecture|$0.4|2,684,000|
|Shanghai City|$2.7|24,280,000|
|Suzhou City|$1.1|10,720,000|

# IV. Lessons for Data Privacy in Developing Countries

As the above review demonstrates, the cost of data privacy compliance can be quite high, so high that companies avoid certain jurisdictions entirely or simply ignore the laws. The GDPR can be so difficult to comply with that more than half of the EU privacy professionals surveyed in the IAPP/EY study said that their organizations were not “fully” or even “very” compliant.<sup>223</sup> At the same time, the cost of not having data privacy protection can be quite high as well, and results in consumers and other counterparties avoiding beneficial transactions because of the risks that the information they share will be misused. Concerns over costs of compliance or costs of enforcement might be ameliorated if stronger data protection laws make it easier for local businesses to participate in global value chains.<sup>224</sup> The digital economy depends on a proper legal framework that protects privacy. Based on the studies above and our discussions with experts, we offer a few recommendations below, with the particular needs of developing countries in mind.

_Ensure clear rules_ . Rules that make clear what companies need to do both reduce costs and increase compliance. A common complaint about both EU and Chinese data privacy law among experts we spoke with was that it can be difficult to know how to comply with the law. The GDPR’s complex framework (there are 173 recitals and 99 articles, and multiple guidance documents) generally requires expensive legal counsel to navigate.<sup>225</sup> One interviewee noted that a hospital participating in a clinical research trial with a drug company might be classified as a processor, a joint controller, or a controller in its own right, depending on the which government is enforcing the rules. A recent case from the Court of Justice of the European Union will require companies to hire lawyers to give opinions on foreign intelligence laws of every country to which the companies are transferring information outside the European Union, including the few states that have received a positive adequacy decision.<sup>226</sup> For their part, the Chinese rules may be highly detailed, but that detail often exists

> 223 Hughes & Saverice-Rohan, _supra_ note 74.

> 224 The World Bank Grp., WORLD DEVELOPMENT REPORT 2020: TRADING FOR DEVELOPMENT IN THE AGE OF GLOBAL VALUE CHAINS (Caroline Freund, Aaditya Mattoo & Pol Antras eds., World Bank Publications 2020).

> 225 DataGrail, _supra_ note 73, at 9 (reporting that 56% of survey respondents indicated that the GDPR regulations are complex and/or vague, and that 45% report that regulations lack a clear path to achieving compliance).

> 226 Case C-311/18, Data Protection Commissioner v. Facebook Ireland Ltd and Maximillian Schrems, 2020 E.C.R. I-0000.

39
_availability and focus are best defined before initiating the fieldwork_ . _This is yet another reason why good infrastructure mapping is so important._ The institutional mapping should be matched with a comprehensive list of key country-specific officials, specialists, and locations, at the same time as identifying institutional repositories of data and central coordinating agencies that might constitute single data sources for a sector or a set of data. For instance, in some countries one option might be the auditor general‘s office (which compiles SOE financial accounts) while in others it might be the bureau of statistics (a good source of financial data).

In a nutshell, the interview plan prepared before the fieldwork commences should be as complete as possible and provide an exhaustive list of (i) institutions and contacts, (ii) existing secondary sources of data, and (iii) referential documents that should be collected in the preparatory work (table 1.5).

**Table 1.5 Indicative checklist of documents and sources**

|**Key documents**|**Source**|**Purpose**|
|---|---|---|
|Central government budgets (including budget,<br>release and actual) and budget speech|Ministry of Finance, the Central<br>Planning Unit|Data collection:<br>Quantitative templates 8, 9, and 10|
|Local government budgets (either consolidated total<br>or three largest authorities)|Central Planning Unit or<br>local governments|Data collection:<br>Quantitative templates 7 and 10|
|Annual reports of SOEs and special budgetary<br>funds financial accounts (income statement, balance<br>sheet, cash flow statement)|Ministry of Finance, Central Planning<br>Unit, SOEs, line ministry(ies), and/or<br>special fund administration|Data collection:<br>Quantitative template 11|
|Relevant acts or laws relating to public financial<br>management|Ministry of Finance|Country report:<br>Qualitative templates 5 and 6|
|Country Financial Accountability Assessment<br>(CFAA)|WB|Data collection:<br>Qualitative templates 5 and 6|
|Fiscal transparency—Report on Observation of<br>Standards and Codes|IMF|Data collection:<br>Qualitative templates 5 and 6|
|Recent economic developments|IMF|Country report|
|Medium-Term Expenditure Framework document|Ministry of Finance and/or Central<br>Planning Unit|Quantitative templates 8 and 9<br>Qualitative template 6|

_Source: Authors’Elaboration_ _Note:_ Ministry of Finance = Ministry of Finance; IMF = International Monetary Fund; WB = World Bank; SOEs = state-owned enterprises.

# **Mining publicly available sources**

As many quantitative template (using readily available public sources) should be completed during the pre-field stage as possible. Governments and enterprises are increasingly transparent, meaning that more national budget books, as well as annual reports and financial accounts for SOEs, are being made public and are increasingly available on-line.

Filling in the quantitative templates with as much data as possible _before_ the fieldwork stage allows more focus on validation and the research needed to fill any data gaps. For this reason, it is highly recommended.

23