rafmacalaba/datause-encoder-data huggingface dataset:
EVenTs) messaging standard, which is used to track parcels (packages up to 30 kg), letters (letter-post items and packages up to 2 kg), and express mail flows in the UPU network.

_Table 1: Events in the UPU database_

|**Message ID**|**Event Description**|
|---|---|
|**Exporting event**<br>|**s**<br>|
|A|Posting/Collection|
|B|Arrival at outwards office of exchange|
|C|Departure from outward office of exchange|
|**Importing even**|**ts**|
|D|Arrival at inward office of exchange|
|E|Held byImport Customs|
|F|Departure from inward office of exchange|
|G|Arrival at deliveryoffice|
|H|Attempted/Unsuccessful delivery|
|I|Final delivery|
|**Transit events**||
|J|Arrival at transit office of exchange|
|K|Departure from transit office of exchange|

The processing and handling sequence of cross-border shipments is reported in Table 1. For an e- commerce item, after a shopper places an order, the shipper hands the item to the origin post (code A). The post inducts the item into its domestic network where it passes through several handling, sortation, and transport processes (code B). At the origin Office of Exchange (OE), the item is placed in a receptacle for international dispatch to the destination OE, in which it departs from the countryof-origin (code C). After two potential transiting events (coded J-K), the item arrives at the destination (code D), where it is unloaded and handed over to the destination post. The E event denotes the process of separating items from a bundle that they were shipped in, as well as item retrieval and clearance through customs. The destination post inducts it into their domestic network for processing and potential relocation to the delivery office, from which a final delivery to the customer happens (code I).

Alternatively, records of unsuccessful deliveries are recorded using event H. Each of the different handoffs described above is supported by data capture methods such as barcode scanning and computerized entries. The different supply chain partners use this data to generate and exchange the applicable EDI messages in compliance with agreed standards and business rules designed by UPU.

With about 1 billion records per year, EMSEVT provides data for building quality-of-service KPIs that are used by postal operators for assessments of their efficiency. The time difference between A and B means collection time performance, the difference between B and C denotes the processing time at origin, C-D informs about international transit, D-F contains information on the dwell time at destination office of exchange, including customs clearance. The difference between D and H/I informs about performance of postal activities at destination.

For the LPI, the focus was on the availability and performance at destination, making events D, H/I the primary events of interest for assessing the performance of postal services and last mile deliveries. This indicator has also the best country coverage. The KPI published in 2023 only retain parcel, not letter, data.

5
**Figure 1: Structure of Russian Enterprise Debts, 1994-1999**

<!-- Start of picture text --> 60<br>50 Bank Loans and Credits<br>OTax Arrears<br>*Wage Arrears<br>40 -<br>O Arrears to Suppliers<br>30<br>20 -<br>10<br>1994 1995 1996 1997 1998 1999'<br><!-- End of picture text --> <!-- Start of picture text --> Source: Russian Economic Trends<br><!-- End of picture text -->

***** As of March, 1999

Meanwhile, although the fiscal situation of the Russian government has been actually improving since early 1998, the enterprise sector has remained in a lowinvestment, low-productivity trap. Large enterprises have been able to build up significant wage, supplier, and tax arrears, while bartering whatever goods they did produce in their place. According to _Goskomstat's_ own figures, by mid 1999, overdue accounts receivables on enterprise balance sheets amounted to 40% of GDP, while barter reached approximately one-half of all industrial sales.1<sup>4</sup> The evolution of different arrears can be seen in figure 1 above, which shows the composition of the main enterprise liabilities for four sectors (industry, agriculture, construction, and transport). Figure 2 below illustrates the rise in overdue liabilities and receivables as percentages of their respective totals. Meanwhile total enterprise receivables and payables (represented by the dashed lines) have more than doubled as a share of GDP between 1995 and 1998. Most firms have resorted to arrears as a source of trade credit at a time when the volume of bank credit has been shrinking. Moreover, the growing gap between payables and receivables indicates the increasing indebtedness of these four sectors to other sectors in the economy, mainly to the utilities (Gasprom and UES).

14 _Russian Economic Barometer_ and _Russian Economic Trends._ World Bank, "Dismantling Russia's Nonpayments System: Creating Conditions for Growth" 1999.

7
# **_G. SMEs and Productivity – Evidence of Resource Misallocation_**

While the focus in the above work is on firm size and growth, a recent literature has argued that the large differences in productivity between rich and poor countries can be explained by heterogeneity in firm-level productivity, which can in turn be attributed to the resource mis-allocation in developing countries (Bartelsman, Haltiwanger, Scarpetta, (2004), Banerjee and Duflo (2005), Jeong and Townsend (2007), Restuccia and Rogerson (2008), Hsieh and Klenow (2009), Alfaro, Charlton, and Kanczuk 2008; and Midrigan and Yi Xu 2014). Bartelsman, Haltiwanger, and Scarpetta (2013) show that the within-industry covariance between size and productivity is a robust measure of this mis-allocation and that this size/productivity relationship is stronger in the more advanced economies.

The underlying logic behind this measure is as follows. In the absence of any distortions, the traditional models of firm size distribution (e.g. Lucas, 1978 and Melitz, 2003) predict a positive correlation between size and productivity so that larger firms are more productive. However, distortions in developing countries affect both resource mis-allocation (too many resources are devoted to small unproductive firms) and selection processes (highly productive firms may exit and low productivity firms may be allowed to operate), which lead to a great deal of variation in the size-productivity relation across countries. This variation is then captured by the cross-country variation in the covariance between size and productivity. Ayyagari, Demirgüç-Kunt, and Maksimovic (2015b) in their cross-country study using Enterprise Survey data show that the covariance term is largely negative in developing countries. They also find that the resource mis-allocation seems to decrease with age, since the average and median size-productivity appears to 23
# **<u>Data Appendix</u>**

# **Table A1. List of Variables, Definitions and Sources**

|**Variables**|**Description**|**Source**|
|---|---|---|
|Dependent<br>Variable|National unemployment rates:the average unemployment rates of MENA<br>countries between years 2000 and 2017, defined as the number of unemployed<br>persons (aged 15 and over) as a percentage of the total number of persons in the<br>labor force|International Labor Organization Department of Statistics;<br>https://www.ilo.org/ilostat\|
|Digital<br>Economy<br>Variables|Internet users:the average proportion of individuals using the Internet based on<br>results from national household surveys spanning 2000 to 2017<br>Internet payments: percentage of population aged 15 or more that used the<br>Internet to pay bills or buy online in the past 12 months, using data from 2011,<br>2014, and 2017. For countries with more than one data point, we use the average.|International Telecommunication Union;<br>https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx<br>World Bank Global Findex Database;<br>https://globalfindex.worldbank.org/|
|Informality<br>in non-<br>agricultural<br>sectors|Informal employment:the percentage of people who, during a given reference<br>period, were employed in at least one informal sector enterprise, irrespective of<br>their status in employment and whether it was their primary or secondary job; the<br>data spans 2006 through 2018 but without the inclusion of High-Income countries|International Labor Organization Department of Statistics;<br>https://www.ilo.org/ilostat\|
|Other<br>controls|GDP deviation from trend:reported in constant local currency using the Hodrick<br>Prescott filter and was obtained via staff calculations. The original data used in<br>these calculations can be found at the World Development Indicators database,<br>covering 2000 through 2017.<br>Account severance pay for dismissal:reported as a percentage of one's yearly<br>salary which covers 2015, 2016, 2017, 2018, 2019.<br>Bank Credit: Ratio of private credit by deposit money to GDP, covering the years<br>2000 through 2016|World Development Indicators;<br>https://datacatalog.worldbank.org/dataset/world-development-<br>indicators<br>Doing Business Database; https://www.doingbusiness.org/<br>Financial Development and Structure Database;<br>https://www.worldbank.org/en/publication/gfdr/data/financial-<br>structure-database|
|Instrumental<br>Variables|Telephone subscriptions 75-99:the log of average number of fixed telephone lines<br>per capita, 1975-1999<br>Bank Credit:Ratio of private credit by deposit money to GDP, covering the years<br>2000 through 2016|International Telecommunication Union;<br>https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx<br>Financial Development and Structure Database;<br>https://www.worldbank.org/en/publication/gfdr/data/financial-<br>structure-database|

29
FIGURES

|Figure 1t|Taxonomy of Vocational Education<br>and Training|PAGE<br>5|
|---|---|---|
|Figure 2:|<br>Conceptual Model of Skill Development<br>and Employment|8|
|TABLES|||
|Table 1:|Sample Project and Component Cost<br>and Size, InLome Level, All Projects|12|
|Table 2:|Sample Project and Component Cost and Size,<br>By Region,. All Projects|12|
|Table 3:|Project Size, Cost and Performance, By<br>Income Level, Completed Projects|13|
|Table 4:|Project Size, Cost and Performance,<br>By Region, Completed Projects|14|
|Table 5:|Average Size of Institutions Supported,<br>By Mode and Region|16|
|Table 6:|Average Size of Institutions Supported,<br>By Mode and Income Level|17|
|Table 7:|Average Planned Total Investment Cost<br>Per Place By Mode and Region|19|
|Table 8:|Average Planned Total Investment Cost<br>Per Place, By Mode and Income Level|19|
|Table 9:|Average Institut'onal Size and Unit Tctal<br>Investment Costs Per Place, By Mode,<br>Region and Income Level|20|
|Table 10:|Categories of Expenditure, By Mode, Region<br>and Income Level, Completed Components|22|
|Table 11:|Changes in Allocations by Category of<br>Expenditure, Two Time Periods,<br>By Region|23|
|Table 12:|Relationship of Sector Work to Subsequent<br>Investments|28|
|Table 13:|Percentage Distribution of Component<br>Objectives By Region, Mode and Country<br>Income Level|31|
|Table 14:|Changes in Percentage Distribution of<br>of Client Objectives, Three Time Periods:<br>Summary Data|33|
|Table<br>15:|<br>Pere~entage<br>Distribution<br>of Client<br>Objectives, Three Time Periods:<br>By Region|34|
|Table 16:|<br>Percentage Distribution of Client<br>Objectives, Three Time Periods:<br>By Mode|35|
|Table 17:|Incidence of Linkages, By Mode<br>Sample of Completed Industry<br>VET Components|36|
**Table 3** Impact of Treatments on Male IPV Attitudes and Perpetration <u>(Male</u> Data)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|Violence attitudes index|||||
|treatment x post|-0.290**|-0.066|-0.000|2,314|
||(0.118)|(0.086)|||
|treatment x post x Goal|0.179<br>(0.125)||||
|Tolerate violence from<br>husband/partner|||||
|treatment x post|-0.174***<br>(0.050)|-0.014<br>(0.038)|0.099|2,314|
|treatment x post x Goal|0.069<br>(0.062)||||
|Men can beat women under<br>certain circumstances|||||
|treatment x post|-0.039<br>(0.072)|-0.025<br>(0.053)|0.169|2,314|
|treatment x post x Goal|0.056<br>(0.070)||||
|IPV often index|||||
|treatment x post|0.019<br>(0.094)|-0.049<br>(0.087)|0.000|2,314|
|treatment x post x Goal|-0.090<br>(0.139)||||
|IPV in last year index|||||
|treatment x post|0.118<br>(0.146)|-0.020<br>(0.153)|0.000|2,314|
|treatment x post x Goal|0.058<br>(0.222)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. For each outcome defined, the coefficients from a single regression are presented in two sub-rows, with estimates of _β_ 1 in column 1 and _β_ 2 in column 2, labeled treatment x post, and the estimate of _γ_ 1 in the second of the two rows, labeled treatment x post x Goal. Column 3 presents the outcome mean at endline in among males whose connected female is in a ELA only community and was not assigned to the _Goal_ treatment and column 4 shows the number of observations in the model. All specifications include controls for age of the male, highest grade completed, a binary indicator that the male never talking to his father about sexual reproductive health topics, a binary indicator that the male’s household owns the house he lives in, and region fixed effects. Tolerate violence from husband/partner is a binary indicator that male agrees that a female should tolerate violence, Men can beat women under certain circumstances is a binary indicator for the male disagreeing that “a man should not beat a woman under any circumstance,” and the Violence Attitudes index is generated by taking the unweighted mean of the indicators after they have been standardized to the mean and standard deviation among males whose connected female is in a ELA only community and is not assigned to the _Goal_ intervention at baseline and endline separately. Higher values of the index indicate greater taste for violence. IPV often (in last year) index is defined as for the females in Table 2, except the underlying questions are about the male’s own perpetration of psychological, physical, and sexual abuse. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Male baseline and endline data, balanced panel.

50
(somewhat surprisingly) to be a _negatively_ related to the existence (a) of an index of institutional restraint on the executive and (b) to a right-wing government and _positively_ to the number of staff in the pre-reform telecom incumbent. In most cases, as one would expect, taking account of endogeneity reduces the t-statistics on the regulatory variable. However, the instrumental variable (IV) estimate for the impact of regulatory independence on network penetration is _increased_ relative to the OLS estimate (but only at a 10% significance level)<sup>40</sup> .

Guasch, Laffont and Straub do not treat the existence of an independent regulator as endogenous; rather, they use the existence of the regulator as an instrument for the concession contract features that they believe are endogenous. As they recognize, this causes problems in the IV estimates of the impact of the regulator when the latter is included as a separate independent variable in addition to its use as an instrument. This points to the problem (emphasized both by Guasch et al and Fink et al) of the acute difficulty in finding appropriate instruments for the estimation of utility and regulatory reform outcomes.

# **5.1.3 The Errors-in-Variables Problem where the Informal Governance Characteristics are Unobservable**

We have earlier identified that there is a potential errors-in variables problem from having data only on the formal characteristics of regulation (the legal framework) rather than data for countries _both_ on the legal characteristics _and_ on the actual development of regulatory practice.

In order to gain an idea of the potential quantitative effect of using only formal measures of the quality of the regulatory institutions in practice, we explored data from Stern and Holder (Table 2 in Section 2.2.1 above) on a range of 12 regulatory institutions in six developing Asian countries in 1998. This study divided the six effects into two groups:

- (a) a group of 3 _formal_ measures setting out features of the legal framework (Clarity of Roles and Objectives, Autonomy, and Accountability) ; and

- (b) a group of 3 _informal_ measures (Participation, Transparency and Predictability) .

In fact, as we discussed earlier, the actual measures in (b) had a relatively high weight of legal aspects and planned developments relative to actual experience e.g. in price and other regulatory views, not least because so many of the regulatory agencies considered had been established only 1-2 years before the date of the survey. Hence, the “informal” aspects contain much less practice and rather more “formal” elements than one would like and this should be borne in mind when considering the simulation results reported below.

We took the true data generating process to be: Yi = 1.0* (TF + TI)/6 + ui

> 40 See Gual and Trillas op cit, pp 9-18.

30
Table 2: MATCHING IN TRANSFER NETWORKS AT BASELINE

||Ultra poor<br>(1)|Other poor<br>(2)|Middle class<br>(3)|Upper class<br>(4)|
|---|---|---|---|---|
|_Panel A:_|_Percentag_|_e of households_|_receiving trans_|_fers from ..._|
|Ultra poor|17.60|9.70|4.41|1.26|
|Other poor|32.58|39.51|17.49|6.00|
|Middle class|65.57|66.08|68.78|23.52|
|Upper class|21.33|23.50|21.51|26.14|
|_Panel B:_|_Percent_|_age of househo_|_lds giving trans_|_fers to ..._|
|Ultra poor|14.05|8.83|7.25|11.51|
|Other poor|21.60|31.43|21.76|28.89|
|Middle class|28.30|36.09|60.25|51.74|
|Upper class|3.12|5.26|11.23|28.76|
|Observations|6,732|7,340|6,742|2,215|

**Source:** Author’s analysis based on original survey data.

**Notes:** The sample includes observations from the baseline survey. In column 1, the sample is restricted to ultra-poor households; in column 2, to other poor households; in column 3, to middle class households; and in column 4 to upper class households. Panel A gives the percentage of households receiving transfers from the relevant group; Panel B gives the percentage of households giving transfers to the relevant group. The four rows within Panel A (B) show the percentage of respondents whose household received (gave) any transfers to an ultra-poor (other poor, middle class or upper class) in their village. Equality of means across the four wealth classes are all rejected at 99% confidence (based on one-sided t-tests).

37
|If yes, is sexual orien-<br>tation included?<br>Yes|Labor Code (1992),<br>Art. 8 Para. 3<br>Protection Against<br>Discrimination Act<br>(2004), Art. 12 Para. 1,<br>Art. 4 Para. 1|<br>Yes|Ley Federal para Preve-<br>nir y Eliminar la Dis-<br>criminación (2003), Art.<br>9 Para. III<br>Ley Federal del Trabajo<br>(1970), Arts. 2, 3, 133|No|Yes|Equal Treatment Act<br>(1994), Sec. 1 (1)(b)|N/A|No||
|---|---|---|---|---|---|---|---|---|---|
|If yes, is gender identi-<br>ty included?<br>Yes|Protection Against<br>Discrimination Act<br>(2004), Art. 12 Para. 1,<br>Art. 4 Para. 1|No||No|No||N/A|No||
|Prohibit an employer<br>from asking about an<br>individual’s sexual<br>orientation or gender<br>identity during the<br>recruitmentphase?<br>Yes|Protection Against<br>Discrimination Act<br>(2004), Art. 12 Para. 2|No||No|No||No|No||
|Prescribe equal remu-<br>neration for work of<br>equal value for LGBTI<br>individuals?<br>Yes|Protection Against<br>Discrimination Act<br>(2004), Art. 14 Para. 1|Yes|Constitución Política de<br>los Estados Unidos Mex-<br>icanos (1917), Art. 123<br>Para. VII<br>Ley Federal del Trabajo<br>(1970), Art. 56|<br>No|Yes|Equal Treatment Act<br>(1994), Sec. 5 (1)(e), (h)|No|Yes|Decree No.<br>10/2012/QH13 Labor<br>Code (2012), Arts. 5<br>Para 1 (a), 8 Para. 1 ,<br>90 Para. 3|
|Prohibit the dismissal<br>of employees on sex-<br>ual orientation or<br>gender identity<br>Yes|Protection Against<br>Discrimination Act<br>(2004), Art. 21|Yes|Ley Federal del Trabajo<br>(1970), Arts. 3, 133|No|Yes|Equal Treatment Act<br>(1994), Secs. 5(1), 8|No|No||
|grounds?<br>Access to Remedy<br>Does the law allow an employ|ee to bringa claim for em|ployme|nt discrimination on sexual|orientation orgend|er identity grounds:|||||
|In the public sector?<br>Yes|Protection Against<br>Discrimination Act<br>(2004), Art. 50, 71.|Yes|Ley Federal del Trabajo<br>(1970), Arts. 2, 3, 133<br>Ley Federal para Preve-<br>nir la Discriminación<br>(2003), Art. 43|<br>N/A|Yes|Equal Treatment Act<br>(1994), Secs. 10, 12 (2)|N/A|No||
|If yes, is the employee<br>required to prove loss<br>or injury?<br>No|Protection Against<br>Discrimination Act<br>(2004), Art. 9|No|Ley Federal del Trabajo<br>(1970), Art. 784|N/A|No|Equal Treatment Act<br>(1994), Sec. 10 (1)|N/A|N/A||
|If there is a law, does<br>it prohibit retaliation<br>by the employer for<br>bringing a complaint<br>or participating in any<br>manner in an em-<br>ployment discrimina-<br>tionproceeding?<br>Yes|Protection Against<br>Discrimination Act<br>(2004), Art. 5, addi-<br>tional provisions, Sec. 1<br>(3)|<br>No|No provisions could be<br>located|N/A|Yes|Equal Treatment Act<br>(1994), Sec. 8(a)|N/A|No|No provisions could<br>be located|

Anti-Discrimination Law and Shared Prosperity 70
consequently, the tax system’s burden on firms may also increase compliance. Thirdly, firms that face substantial competition from the informal sector are more likely to report higher levels of tax evasion. This is likely because formal firms in competition with informal firms have a disincentive to comply, as this will lower their competitiveness in the market (such as by lowering their profit margins). Interestingly, this result suggests that there may be a positive externality from registering informal firms if this leads to formal firms being more likely to comply. Notably, there were limited differences in tax evasion rates across other dimensions. As such, any efforts by tax collectors to reduce the rate of tax evasion by most types of firms are likely to raise revenue.

This study has also shown the value of embedding double-list experiments in large-scale surveys. While single-list experiments have grown popular over time, there has also been increasing recognition of some of their shortcomings, particularly regarding the possibility of specific non-sensitive items in a list influencing the likelihood of respondents also counting the sensitive item. Double-list experiments verify if this issue is present and can help illustrate the internal consistency of findings. Our examination of heterogeneity across 27 dimensions with a large sample size is also an important learning exercise as it shows the value of combining machine learning algorithms with indirect solicitation techniques. However, the double-list experiment approach also raises important questions about the best way to report heterogeneity in instances that are not substantial and/or consistent across both list experiments. At a minimum, our study shows how, at a low marginal cost, a double list experiment can provide much more credible insights about the prevalence of a sensitive behavior than a single list experiment and this approach can identify the dimensions of heterogeneity in the prevalence of a sensitive behavior.

Future research on this topic could take several directions. Firstly, additional research could be conducted measuring levels of tax evasion in Indonesia to validate our findings by analyzing tax administrative data with third-party information and/or survey data that directly captures tax evasion and tax morale. In addition, there may be value in exploring 17