rafmacalaba/datause-encoder-data huggingface dataset:
The survey-based LPI captures the ease of establishing reliable and effective supply chain connections to export target countries. The survey does this by pooling the information from the six LPI components, as assessed by logistics and freight forwarding professionals. LPI respondents rate countries according to their supply chain experience when providing their macro-level qualitative knowledge of them. Micro-level tracking data, however, informs about the performance outcomes along the LPI dimensions except for affordability, as cost information is not available in this data. Attributes of supply chain performance accessible from the data include two main categories.

The first category comprises indicators of timeliness and reliability. Domestic and international supply chains have a skewed lead time distribution with a long tail. Therefore, the significance of lead time at a port or along a corridor is not captured just but its average but by how likely much longer delays are. The shape of the right side of the distribution, i.e., the length of the “tail”, reflects reliability. Corresponding KPIs include:

- Mean/Median lead time for legs of the supply chain (between two subsequent events), with focus on time at and within border.

- Indicators of reliability as statistical dispersion such as interquartile range defined as the difference between the 75<sup>th</sup> and 25<sup>th</sup> percentile.

The second category includes indicators of connectivity. The structure of the network matters. More connections mean more options to access markets, also contributing to reliability and performance. This is like human mobility where more options to reach destinations increase accessibility of individuals to reach jobs or services. Connectivity indicators are provided by mode: container shipping, aviation, postal. They mostly consist of simple counts of connections in the network.

# Definition of the KPI

The set of indicators below focuses on (i) delays experienced at the same place or dwell time at ports or airports, (ii) delays in motion, and (iii) connectivity information. From a policy standpoint, dwell time is an important KPI. Time spent at hubs and gateways can be improved by productivity investments, fluidization of information, or simplification of processes. Furthermore, dwell time at these hubs and gateways has considerably more dispersion than in international freight transport. While planes and ships have consistent travel time (at least under normal circumstances), lead time at key facilities or to some extent land corridors is affected by a high degree of dispersion that impacts the logistics of the consignee at destination. The KPI produced for the report are in Table 2.

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# **Table 9. Characteristics of Land Titling firms**

The sample consists of all nonfinancial listed firms that have allocated land before 2012. The table presents variable average and standard deviation (in the bracket below) for the entire sample, as well as a subsample of firms that are affiliated to the government ( _SOE_ ) and firms that do not ( _Non-SOE_ ). The difference between the values for _SOE_ and None _SOE_ are presented in Column (4). The t-statistics in brackets of column (4) are for the null hypothesis that there is no difference between _Titling firms_ and _Non Titling firms_ . _Historical Annual Stock Market Return_ is the annualized average stock market return between 2008 and 20012. Δ _Historical Annual Stock Market Return_ is the difference in annualized average stock market return between 2008/2012 and 2013/2017. _SA Measure_ is calculated using the formula of firm size and age. _External Finance Reliance_ is constructed using the index of US industries’ averaged reliance on external finance. _Incentive Package_ is averaged quantity of shares held by management as an incentive package between 2008 and 2017. _CAR in QE announcement_ is the Cumulative Abnormal Returns (CARs) between 5<sup>th</sup> and 11<sup>th</sup> Nov, 2008, when the “4 Trillion” fiscal stimulus plan was released. *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||(1) All Titling firms|(2) SOE|(3) Non-SOE|(4) Difference|
|---|---|---|---|---|
|**Panel A: Land Characteristics**|||||
|Land Area (Hectare)|4.986|4.775|5.358|-0.583|
||(4.210)|(3.390)|(5.458)|(0.012)|
|Land Value (Billion RMB)|1.307|1.328|1.286|0.042|
||(1.172)|(1.074)|(0.956)|(0.113)|
|Land Value (% of Market|0.573|0.579|0.583|-0.004|
|Capitalization)|(0.377)|(0.345)|(0.447)|(-0.057)|
|**Panel B: Cost of Equity**|||||
|Historical Annual Stock Market|-0.350|-0.321|-0.423|-0.102|
|Return|(0.130)|(0.134)|(0.085)|(-1.460)|
|Δ Historical Annual Stock Market|0.032|-0.029|0.190|0.219|
|Return|(0.220)|(0.203)|(0.188)|(1.241)|
|Dividend / Total sales|0.038|0.049|0.009|0.0406|
||(0.161)|(0.189)|(0.019)|(0.74)|
|**Panel C: Financial Constraints**|||||
|Size-Age Measure|1.019|0.779|1.43|-0.651|
||(3.808)|(1.691)|(6.001)|(-0.54)|
|External Finance Reliance|0.334|0.351|0.308|0.043|
||(0.245)|(0.256)|(0.225)|(0.440)|
|**Panel D: Corporate Governance**|||||
|Incentive Package|2.223|2.146|2.421|-0.275|
||(5.015)|(4.999)|(5.274)|(-0.16)|
|Chairman-CEO duality|0.837|0.839|0.833|0.00538|
||(0.374)|(0.374)|(0.389)|(0.04)|
|% of Independent board|0.358|0.350|0.379|-0.0290|
||(0.050)|(0.042)|(0.061)|(-1.23)|

55
|**Contiguity**|-0.063|-0.107|.|.|
|---|---|---|---|---|
||(0.130)|(0.137)|.|.|
|**Common Language**|0.057|0.0467|.|.|
||(0.094)|(0.097)|.|.|
|**Colony**|0.690***|0.748***|.|.|
||(0.150)|(0.153)|.|.|
|**RTA**|-0.166***|-0.015|0.486***|0.490***|
||(0.065)|(0.073)|(0.069)|(0.074|
|**Common Legislation**|0.249***|0.262***|.|.|
||(0.057)|(0.058)|.|.|
|**Common Currency**|0.123|0.319*|0.637***|0.640***|
||(0.180)|(0.188)|(0.150)|(0.157)|
|**Importer /Exporter dummies (i/j)**|YES||||
|**Year dummies (t)**|YES||||
|**Importer/Exporter-time-varying dummies (it/jt)**||YES|YES|YES|
|**Symmetric pair dummies (ij)**|||YES||
|**Asymmetric pair dummies (ij)**||||YES|
|**Observations**|16,718|16,718|16,718|16,718|
|**R**<sup>**2**</sup>|0.734|0.780|0.866|0.901|

The dependent variable is the log of bilateral exports. All regressions include importer and exporter fixed effects. Superscripts ***, **, * denote statistical significance at 1, 5 and 10% respectively. Robust standard errors are provided in parentheses.

When the _pair_ or symmetric fixed effects are additionally included, the results change dramatically. The RTA and common currency dummies are now highly positive and similar to those obtained by Glick and Rose (2002) who found a coefficient of 0.74 and Baier and Bergstrand (2007) who estimated the impact of RTAs on trade to be 0.68. Since Rose (2002) uses symmetric fixed effects, these results would vindicate his approach.

Most importantly for the purposes of this paper however, are the results on both migration variables. No effect of emigrants is found _whatsoever_ and the coefficient on the immigrant variable is actually negative, 20
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **4. Raw Cocoa Dominates Cote d’Ivoire’s Trade**

**While Cote d’Ivoire’s trade links have diversified, its export products have not.** In the last section, we showed that over the last forty years Cote d’Ivoire has greatly expanded the list of countries that it trades with, and reduced its dependency upon the traditional trade partner, France. Here in this section we re-iterate some of the evidence that the reverse is true so far as the list of export products is concerned.

**In fact, Cote d’Ivoire has become heavily specialized in cocoa** . Figure 9 (next page) shows four graphs which document this claim. Cote d’Ivoire’s cocoa production grew to over 1.4 million metric tons in 2000; it is the world’s leading producer. The real price of raw cocoa grew from the early years to a peak in 1976 and then declined; at the same time Cote d’Ivoire’s output per worker followed the same boom-and-bust profile. Cocoa prices are volatile. Over the years, Cote d’Ivoire has devoted an ever-increasing amount of land to cocoa output. Cote d’Ivoire’s share of world output grew from less than 7% in 1960 to over 41% in 2000.

**Raw cocoa now dominates the Ivorian economy and exports, even more than in the past** .  Figure 8 below shows data which supports this claim. By 2000, raw cocoa was 80% of the country’s commodity exports, over 50% of all exported goods and services, and 21% of the entire GDP.  When one considers that a great deal of output and economic activity in other sectors is cocoa-related, it is clear that this country has become quite dependent upon one raw commodity, cocoa.<sup>7</sup>

**Figure 8: Cocoa Exports as a Percentage of Commodity Exports, All Exports (Including Services), and Gross Domestic Product**

<!-- Start of picture text --> Cote d'Ivoire: Cocoa Exports as a Percent of Basic Commodity Exports, of<br>Exports of All Goods & Services, and as a Percent of Gross Domestic<br>Product<br>90%<br>% Commodities % All Exports Percent GDP<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Years: 1984 to 2002<br>Percent (Based on 1987 US$)<br><!-- End of picture text -->

> 7 Cote d’Ivoire’s number two export is coffee; coffee and cocoa prices correlate highly on world markets. Number three is cotton, another raw agricultural commodity.  They offer little in the way of benefits of diversification from cocoa.

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**Table 1: Summary of IPRs Provisions in Legally Enforceable IP-Related PTAs**

|**Panel A: all IPR provisions as of 2015**|**US**<br>|**LE IPAs**<br>|**(13)**<br>|**EU/EFT**<br>|**A LE IP**<br>|**As (45)**<br>|**Other**<br>|**LE IPA**<br>|**s (42)**<br>|
|---|---|---|---|---|---|---|---|---|---|
||Avg|Min|Max|Avg|Min|Max|Avg|Min|Max|
|Accession/Ratification (n = 15)|11.5|2|14|3.3|0|13|2.0|0|13|
|National Treatment (n = 2)|2.0|2|2|0.9|0|2|0.6|0|2|
|Trademarks (n = 15)|9.4|4|15|1.6|0|7|1.5|0|11|
|Geographical Indications (n = 7)|2.6|0|4|2.0|0|7|0.7|0|3|
|Patents (n = 14)|4.8|1|13|1.0|0|3|0.7|0|10|
|Data Protection (n = 5)|2.8|0|5|0.9|0|2|0.1|0|5|
|Copyrights (n = 14)|10.5|4|14|2.0|0|12|1.9|0|12|
|Enforcement (n = 23)|17.2|4|20|7.6|0|17|4.8|0|17|
||**US**|**LE IPAs**|**(13)**|**EU/EFT**|**A LE IP**|**As (45)**|**Other**|**LE IPA**|**s (42)**|
|**Panel B: BTRIPS provisions as of 2015**|Avg|Min|Max|Avg|Min|Max|Avg|Min|Max|
|Trademarks (n = 4)|2.3|2|4|0.2|0|2|0.4|0|4|
|Geographical Indications (n = 3)|0.9|0|1|0.8|0|3|0.3|0|2|
|Patents (n = 5)|1.2|0|4|0.4|0|1|0.2|0|3|
|Data Protection (n = 5)|2.8|0|5|0.9|0|2|0.1|0|5|
|Copyrights (n = 6)|5.6|1|6|1.0|0|6|0.8|0|6|
|Enforcement (n = 10)|7.1|1|9|3.1|0|7|2.0|0|7|

8
are available, we impose a penalty for above average FX lending at the individual bank as well as the asset class level. We assume that LGDs are correlated with PDs where the correlation is specified to be stronger in crisis times (Stress scenario) so that the effect of changing macroeconomic conditions is translated, through the PDs and LGDs correlation, from probabilities of default to losses given default (Altman, Resti and Sironi, 2002). We also consider the concentration of bank lending in each asset class as well as the average time-to-maturity in the individual asset classes in the computation of the effective PDs and therefore the effective capital buffer (see Section 6).

# **4.1. TTC Macroeconomic Scenarios, PDs and LGDs**

The purpose behind using the TTC values of the macroeconomic variables and the TTC PDs and LGDs is to ensure that the cost of credit for banks in the steady-state of the macroeconomy is adequately accounted for. Most developing countries lack adequate data describing historical aggregate PDs and LGDs over a sufficiently long time period that includes a number of business/credit cycles. We thus suggest to use the TTC PDs and LGDs constructed by the Fifth Quantitative Impact Study (QIS5) of the Bank for International Settlements (BIS) (BIS, 2006). Table 1 below shows the magnitudes of the constructed PDs and LGDs for banks belonging to CEBS Group 1, CEBS Group 2 and non-G10 Group 2 countries.<sup>17</sup> The reason why we show entries for CEBS and non-G10 Group 2 countries<sup>18</sup> here is

> 17The Committee of European Banking Supervisors (CEBS) non-G10 countries that provided data for the QIS5 study include Bulgaria, Cyprus, the Czech Republic, Finland, Greece, Hungary, Ireland, Malta, Norway, Poland, and Portugal. Banks that are classified as Group 2 banks are smaller relative to Group 1 banks and do not have any significant international activities. To be classified as a Group 1 bank, the following three criteria need to be satisfied: ( _i_ ) the bank has a Tier 1 capital in excess of e3 billion, ( _ii_ ) the bank is diversified and ( _iii_ ) the bank is active internationally.

> 18This group includes the historical experience of developing countries such as Brazil, Chile, India, Indonesia, and Peru, and of some former transition economies of Central and Eastern Europe.

23
In table 5, we present the distribution of tariff rates in Russia. These are the results of our calculations of tariffs at the tariff line level (the ten digit level in Russia). In 2005, we can see that there were 27 tariff lines with tariff rates of 100 percent or more. Still another 86 tariff lines have tariff rates above 50 but less than 100 percent. But about 94 percent of the tariff rates are less than 25 percent, 83 percent are less than 20 percent, and 41 percent of the tariff lines have tariff rates less than ten percent. Clearly there is a lot of variance in the tariff structure.

In Appendix table A2, we list the ten digit tariff lines with tariffs in excess of 50 percent. Used cars typically have the highest tariff rates—there are five tariff lines for used cars with tariff rates above 200 percent in 2005. There are also several categories of fish, meat, sugar, alcohol, clocks and watches and clothing with tariff lines with tariffs in excess of 50 percent.

# **7. When we aggregate tariff lines to the six digit level, there were 60 sectors with tariff rates in excess of 50 percent in 2005.**

Aggregation of the raw ten digit tariff line data, which is an averaging process, reduces the variance in the tariffs and chops down the tariff peaks. So the more disaggregated the data, the more we will find high tariffs. Nonetheless, at the six digit level of aggregation, there are 178 product lines with tariffs 30 percent of higher. The sectors of 100% or more are: caviar (280%); lobster (171%); shrimps and prawns (153%); shrimps and prawns (153%), floor coverings from other plastics (141%); beer from malt (138%); ensembles of other textile materials (117%), wrist watches, others, electrically operated (112%), used clothing (105%); undenatured ethyl alcohol with 80% or higher alcohol content (100%); ethyl alcohol, denatured (100%). Details are in table A3 of Sheptoylo and Tarr (2007).

# **8. Russia has been increasingly using specific tariffs as part of its tariff policy—most of the increase in the use of specific tariffs has been in the food industry.**

In table 6, we show that the total number of tariff lines subject to the combined system of tariffs has increased by 183 tariff lines, from 1,609 in 2001 to 1,792 in 2005. About 80 percent of the increase in the number of specific tariffs is due to the increase in specific tariffs in the food industry, where an additional 148 tariff lines are subject to the combined system in 2005 compared with 2001. We regard this as a negative trend, since 8
Beyond the small fraction of salaried workers with access to social security benefits, the profile of labor informality in Senegal is completed with the presence of a long network of family businesses among self-employed and employers that are beyond the control of the tax authorities. As Table 7 shows, the percentage of these non-agricultural enterprises with a formal accounting system is around a 2.8% – of which only a quarter transmits to the Tax Authority – 97.3% do not have a fiscal identification number and 96.4% are not even registered at the Commercial Register. Tax compliance among these family businesses is almost null, since only 0.5% have their workers registered at Social Security. However, these small firms do not complain about an excess of regulation and taxes as a main obstacle for their business. Even among formal firms, less than a quarter consider that taxes and regulations are a problem for the exercise of their activity. This evidence is consistent with data from larger firms, with at least 5 employees, since 26.5% (30.2%) of these firms consider tax administration (tax rates) as a major or severe obstacle to their growth and operations (World Bank Enterprise Survey 2014). The problem reported by these firms does not seem to be the burden of taxes but the unfair competition from informal firms as the leading constraint for formal businesses.<sup>10</sup> Whether this informality is chosen or involuntary is a key aspect to understand what are the most suitable policies to solve this problem, a matter that will be dealt with in the next section.

# **4. Involuntary informality in Senegal**

## **_Model to estimate involuntary informality_**

To estimate the size of involuntary informality and its underlying drivers, the analysis follows the approach in Alcaraz et al. (2015). In this model, there are two sectors in the labor market: formal and informal. The model abstracts from the participation decision and therefore individuals only select in which sector to work. Individuals will prefer a job in the sector that gives them the highest utility conditional on its characteristics, which include those that affect their potential labor income directly, such as education and experience, and those that affect their choice of sector and are not necessarily associated to their labor income, such as household composition or marital status. Once a worker decides in which sector to work, they apply for a job in that sector. All individuals can

> 10 More than half of respondents in Senegal find these challenges as major or severe (versus 39 percent in Sub-Saharan Africa) with more than three-quarters of the firms declaring to be in direct competition against unregistered or informal companies.

9
Therefore, we take a number of data intervals around the cut-off and alternative polynomial specifications that depend on the width of the data interval.<sup>26</sup> We start with 10-year intervals on both sides of the cut-off (1975–84 and 1987–96 birth cohorts). Within this interval, we use two different models to account for the time trends. While linear polynomial splines are used in model A1, quadratic polynomial splines are used in model A2. Then, we trim the tails of the interval and take 5-year intervals on both sides of the cut-off. In this case (which we call model B) we take only linear polynomial splines on both sides of the discontinuity. Model C also incorporates linear polynomial splines; however, the time frame is further reduced to include 3-year and 4-year intervals on both sides of the cut-off, which are the narrowest time-intervals that allow us to separate the effect of the policy from that of the time trend given the sample size. As can be seen in figure 1, linear polynomial splines provide a good approximation to the time trends for most subpopulations. Finally, model D includes the shortest-time interval—2 years around the cut-off—but no time trends.

This approach of taking subsamples that are clustered around the cut-off by trimming the tails of the sample interval, which puts no weight on observations at the tails, is similar in a way to non-parametric modeling. As argued by Lee and Lemieux (2010, 284), “...the procedure of regressing the outcome Y on X and a treatment dummy D can be viewed as a parametric regression, or as a local linear regression with a very large bandwidth. Similarly, if one wanted 26 It is important to note that we measure the immediate effect of the policy. If it takes time until the policy becomes fully effective, its long run effects could be somewhat different. Moreover, this could differ by gender as well as rural and urban status. In fact, as can be seen in figure 1, the time trend after the policy vis-à-vis the time trend before the policy is steeper for women than for men. (A potential reason for the changing time trend could be the establishment of new social norms with the policy.) This might imply that the trend after the policy soaks up some of the policy effect, particularly for women. However, the “bubble” around the cut-off we take by eliminating two cohorts from the analysis alleviates this problem. Moreover, since the jump at the point of discontinuity is substantial, even if the time trend after the policy captures part of the policy effect, this would be relatively small.

23
# **Returns to Education in the Russian Federation:**

# **Some New Estimates**

Ekaterina Melianova<sup>1</sup> Suhas Parandekar Harry Anthony Patrinos Artëm Volgin JEL Codes: I26, I28, J16 Keywords: Returns to Education, Russian Federation

> 1 This paper was prepared as part of the World Bank study, _Skills and Returns to Education in the Russian Federation_ (P170978). We are grateful to Renaud Seligman, Fadia Saadah, Dorota Nowak, Cristian Aedo, Ruslan Yemtsov, Husein Abdul-Hamid, Tigran Shmis, Denis Nikolaev, Polina Zavalina, Zhanna Terlyga, Vladimir Gimpelson, Eduardo Velez Bustillo, George Psacharopoulos, Chris Sakellariou and seminar participants in Washington DC and Moscow for useful comments.  All remaining errors are our own. The views expressed here are our own and should not be attributed to the World Bank Group.
capital adequacy requirement. Two banks would fall under the insolvency threshold of 2% and would be considered ”failing” banks. All other banks are able to withstand the assumed country-specific Stress scenario. To recapitalize all banks in the system to the extent that a 2% ERW-CAR would be maintained in the Stress VAR scenario requires about 18% of the annual TTC profits generated by the entire banking system. This figure is arrived at by assuming a 3% TTC Return on Assets (ROA) during normal economic conditions. This figure can also be calculated as a simple arithmetic average on historical ROA data.<sup>46</sup> Profits for each bank are then calculated as ROA times Total Assets and are subsequently summed to provide a banking system wide measure of profits. We use TTC profits here as the base, because banks would normally use retained profits to gradually build up their capital buffers to the required level.<sup>47</sup> The results of the Stress scenario based on the historical cross-country crisis experience under the ”Stress Int.” heading indicate that, on average, the capital buffers of the banking system would get reduced to 0.4%, should this scenario occur. More specifically, nine banks would use their capital buffers, falling below the 8% minimum regulatory CAR level, and six banks would become insolvent with their ERW-CARs falling below 2%. Nevertheless, four banks would still have capital buffers strong enough to withstand such a severe credit risk shock. Recapitalizing all banks so that they would withstand this Stress scenario, i.e., bringing their ERW-CARs above the 2% level, requires about 130% of the annual TTC profits generated by all banks.

The extent of the recapitalization needs at the system level is substantial at close to 1%

> 46This corresponds to through the-cycle profits because the (unconditional) averages are computed over different stages of the business cycle and also preferably over a number of different business cycles. 47However, note that capital injections, coming either from existing or new shareholders, or from public resources (e.g. the government budget), could also be applied as a viable recapitalization measure. These would be particularly relevant when the recapitalization needs of the affected bank are so high and immediate that it could be considered a ”failing” bank.

59