rafmacalaba/datause-encoder-data huggingface dataset:
**Table B2.   Distribution of estimated collected tariff rates in Russia in 2001-2005**

||||**_200_**|**_1_**|**_200_**|**_2_**|**_20_**|**_03_**|**_200_**|**_4_**|**_200_**|**_5_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Actua _**|**_l rate ra_**|**_nge, %_**|_Frequencya_|_Percentb_|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|
|_0_|_to <_|_5_|2,285|20.63|2,357|21.14|2,305|20.65|2,400|21.39|2,378|20.92|
|_5_|_to <_|_10_|3,223|29.1|3,228|28.96|3,272|29.32|3,213|28.64|3,280|28.86|
|_10_|_to <_|_15_|2,228|20.12|2,214|19.86|2,185|19.58|2,253|20.08|2,261|19.89|
|_15_|_to <_|_20_|2,174|19.63|2,022|18.14|2,028|18.17|2,012|17.94|2,127|18.72|
|_20_|_to <_|_25_|850|7.67|889|7.97|824|7.38|854|7.61|871|7.66|
|_25_|_to <_|_30_|157|1.42|186|1.67|194|1.74|181|1.61|178|1.57|
|_30_|_to <_|_35_|47|0.42|79|0.71|66|0.59|84|0.75|75|0.66|
|_35_|_to <_|_40_|16|0.14|38|0.34|77|0.69|48|0.43|46|0.4|
|_40_|_to <_|_45_|30|0.27|35|0.31|49|0.44|33|0.29|27|0.24|
|_45_|_to <_|_50_|18|0.16|21|0.19|26|0.23|17|0.15|24|0.21|
|_50_|_to <_|_55_|7|0.06|9|0.08|19|0.17|17|0.15|13|0.11|
|_55_|_to <_|_60_|6|0.05|9|0.08|19|0.17|14|0.12|6|0.05|
|_60_|_to <_|_65_|2|0.02|6|0.05|17|0.15|21|0.19|18|0.16|
|_65_|_to <_|_70_|5|0.05|9|0.08|9|0.08|3|0.03|3|0.03|
|_70_|_to <_|_75_|2|0.02|2|0.02|10|0.09|4|0.04|2|0.02|
|_75_|_to <_|_80_|4|0.04|6|0.05|2|0.02|9|0.08|2|0.02|
|_80_|_to <_|_85_|4|0.04|7|0.06|14|0.13|16|0.14|18|0.16|
|_85_|_to <_|_90_|1|0.01|2|0.02|4|0.04|2|0.02|4|0.04|
|_90_|_to <_|_95_|2|0.02|1|0.01|3|0.03|1|0.01|3|0.03|
|_95_|_to <_|_100_|1|0.01|2|0.02|4|0.04|0|0|3|0.03|
|_100_|_to <_|_150_|11|0.1|11|0.1|18|0.16|16|0.14|14|0.12|
|_150_|_to <_|_200_|1|0.01|6|0.05|4|0.04|12|0.11|5|0.04|
|_200_|_to <_|_250_|0|0|1|0.01|3|0.03|3|0.03|2|0.02|
|_250_|_to <_|_300_|0|0|4|0.04|2|0.02|5|0.04|3|0.03|
|_300_|_to <_|_350_|0|0|3|0.03|5|0.04|0|0|1|0.01|
|_350_|_to <_|_400_|0|0|1|0.01|1|0.01|0|0|1|0.01|
|_400_|_to <_|_450_|1|0.01|0|0|0|0|0|0|0|0|
|_450_|_to <_|_500_|1|0.01|0|0|0|0|0|0|0|0|
||_>_|_500_|0|0|0|0|1|0.01|0|0|0|0|
|||**_Total_**|11,076|100|11,148|100|11,160|100|11,218|100|11,365|100|

_Notes: a Number of lines at 10-digit level b Percentage out of total number of product lines_ 43
are largely similar to the base model; however, two noticeable differences are the female structural advantage in the age of the manager and the female structural disadvantage in agricultural capital. If we take age to be a measure of experience, then this would suggest that female managers are better able to put their experience to use in plot cultivation (and in this case perhaps preventing crop loss). However, they see lower returns to the use of agricultural capital.

# <<<<<< TABLE 8 AROUND HERE >>>>>>

# **5.3 Crop Level Analysis**

The plot level analysis has indicated that there is a significant gender differential in overall agricultural yields. However, these results could mask significant crop level variation in the gender differential and its root causes. For instance, male managers may have an advantage for some crops, but females do for others. In addition, the characteristics that explain the gender differential could vary across crops. We conduct a crop level analysis to examine variations across the four main crops and all others.

The results of this analysis are presented in Table 9. The “Other” columns account for the production of all crops excluding the four listed in the table. The results indicate that female managers do not have a productivity advantage for any crop. While males do have a significant productivity advantage in millet and other crops, there was no significant gender difference in the production of sorghum, cowpeas and peanuts though the estimated differential in sorghum is large in magnitude. The gap is largest for other crops (50 percent) but also sizeable for millet (16 percent). There are some important differences in which factors explain the gender differential for these three crops. In the case of millet, negative 68 percent of the differential is due to a female endowment advantage while 168 percent is due female structural disadvantages. These results also largely mirror the overall results when aggregating crops. However, we do see a large difference for other crops. In this case the differential is almost entirely due to a female endowment disadvantage (80 percent of the gap). The individual factors that contribute to the differential are largely comparable to the aggregated results. The main difference is that female managers do not have an endowment advantage in female household labor for the production of other crops. This is surprising and suggests that there is not a large difference in the amount of female household labor used on male versus female managed plots or that the difference has no impact on productivity. This analysis has highlighted some important differences across crops, but the crop level results are largely similar to the plot level results.

21
found that Mexico's internal migration rate in 2010, when defined at the primary geographic level, was similar to that of other LAC countries, such as Argentina and El Salvador, and to that of Canada, Spain, and the Philippines. In addition, they found that internal migration rates vary significantly by education level. In Mexico, people with primary education are almost twice as likely to migrate internally as those without education, those with secondary schooling are 2.5 times as likely, and those that completed tertiary are threefold as likely (Figure 1).

Figure 1: Internal migration rate at the primary countries’ geographic level, selected countries, five-year period, 1999-2010 <!-- Start of picture text --> Total Less than primary Tertiary<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Sub- Saharan Africa Northern Central/ Eastern/South- Oceania Latin America/Caribbean Europe/Northern America<br>Africa/ Southern eastern Asia<br>Western Asia<br>Asia<br>Mozambique Ghana Senegal South Africa Cameroon Ethiopia Malawi Guinea Kenya Botswana Egypt Armenia Iraq Morocco India Iran, Isl. Rep. Nepal Kyrgyzstan Indonesia Philippines Thailand Vietnam Malaysia Cambodia China Fiji Haiti Cuba Brazil Nicaragua El Salvador Mexico Argentina Colombia Dominican Rep. Uruguay Ecuador Costa Rica Peru Bolivia Chile Jamaica Trinidad/Tobago Paraguay Romania Slovenia Portugal Canada Spain Greece Switzerland France Belarus United States<br><!-- End of picture text --> Source: Own elaboration based on Bernard et al. (2018)

# **Moreover, there is heterogeneity in interstate migration patterns.** The states with the highest number

of emigrants in 2010 were Mexico City, Veracruz, Puebla, Michoacán, and Oaxaca. Regarding immigration, Mexico state, Mexico City, and Baja California accounted for the highest number of internal immigrants. It is worth noting the number of emigrants from Baja California was significantly lower than that of the other two states. In addition, internal immigration rates were higher for men than women in all 32 states (Varela Llamas et al., 2017).

**The high levels of spatial inequality across states and regions reflects the internal migration patterns observed during the last decades.** Many macroeconomic and socioeconomic factors may determine the probability of internal migration and explain the heterogeneous migration patterns observed across Mexico. Mexico displays high levels of spatial inequality across states in both income and human development indicators. Indeed, there are significant disparities in labor market outcomes across states (2). States in the Northern and Central regions (Chihuahua, Sonora, Nuevo León, Baja California, and

4
in CGE-based assessments of potential multilateral liberalization and regional integration schemes.<sup>17</sup> An implication for empirical efforts to determine the effects of policy is that it is not enough to focus on modeling price-cost margins as that does not allow one to disentangle the two effects. Better econometricallybased decomposition of the various price and cost margins linked to international service transactions is an important and sorely needed building block in guiding policy formation. In addition, recent focus has also shifted to the potential cost burdens imposed by regulations on domestic and foreign firms alike. As Figure 3 illustrates, domestic firms may benefit (or at least not be hurt) from liberalization when it actually involves lifting burdens on both home and foreign firms.

Another question in quantifying the effects of trade agreements, whether WTO or regional, is the extent to which policies on the ground correspond to commitments on paper. This is important if the impacts of new commitments are to be assessed. Both the OECD, with its services trade restrictions database, and the World Bank with a similar exercise, offer evidence on this question. Gootiiz and Mattoo (2009), for example, have created an index measuring the restrictiveness of actual policies in services trade suitable for comparison to the commitments made by countries in the WTO or in regional trade agreements. In the case of the GATS, their work suggests that currently applied policy regimes are much less restrictive than the regimes to which countries are formally committed. That is, the policy bindings negotiated under the GATS allow countries to substantially increase the level of applied trade restrictiveness if they desire to. An implication is not just that there is great scope to lock in the more liberal applied policy regimes, but that modeling the changes in commitments made by countries as a result of a WTO negotiation will overstate the extent of the liberalization achieved in the negotiation. In such cases, new commitments may limit room for policy maneuver, but they imply little change in actual policy.

# **4.2 Approaches to modeling changes in policy**

There have been two basic approaches followed in CGE modeling of changes in policies, once prevailing polices have been somehow benchmarked. The first approach involves a focus on cross-border delivery of services. This includes not only modes 1 and 2 under the GATS, but also mode 3, to the extent this involves cross border sales through affiliates. For example, from the U.S. data we know that exports through affiliates are a prominent aspect of cross-border trade. Consistent with this observation, there is also evidence that FDI restrictions in services do lead to less cross border trade as well.<sup>18</sup> A common approach in CGE modeling of barriers to cross-border trade in services involves using estimates

> 17See e.g. Dee and Hanslow (2001), Hoekman and Konan (2000, 2001) for early CGE applications analyzing the importance of whether services policies generate waste (red tape) or rents.

> 18See for example Table 7 in Francois and Hoekman (2010).

18
- 20 -

costs for the period of investigation, such prices are to be considered to provide for the recovery of costs within a reasonable period of time. The URAA further clarifies that the extended period of time should normally be one year, but in no case less than six months.<sup>85</sup> The URAA further clarifies when "sales below per unit costs" are construed to have been made in substantial quantities."<sup>86</sup> The URAA requires costs to be calculated on the basis of records kept by exporter under investigation if they are in accordance with the generally accepted accounting principles of the exporting countries and reasonably reflect the cost associated with production and sale of the product under consideration.87 There is also a provision about cost allocation. AD authorities shall consider evidence made available by exporter on cost allocation provided that such allocations have been historically utilized, in particular, in relation to amortization and depreciation periods and allowances for capital expenditures and other development costs. Adjustment in costs shall be made, among others, for circumstances in which costs during the period of investigations are affected by start-up operations. In this respect again URAA makes a significant improvement over the Tokyo Code.<sup>88</sup> The use of arbitrary statutory minimum for ASGC and profits by national antidumping authorities in computing constructed domestic sale price will now no longer be possible under the URAA. Article 2.2.2 of URAA requires that ASGC and profits shall be based on actual data of the exporter or producer under investigation. If such a determination is not possible, resort may be had, as prescribed, to the amounts incurred or realized by exporter or producer in question for sales of the same general category of products, or the weighted average of amounts incurred or realized by other exporters under investigation for the domestic sale of like product. The authorities may use any other reasonable method also, but the amount of profits so established shall not exceed the profit normally realized by other exporters or producers on sales of product of the same general category in the domestic market.

> 85 Article 2 footnote 4.

> 86 Article 2 footnote 5.

> **87** On accounting, Boltuck and Litan felt that respondent's task to comply with the information required by Commerce may be "complicated by the fact that foreign companies may use different accounting conventions than those required for data submission by the Commerce Department." See Boltuck, Richard and Robert E. Litan supra note 3, 1 at 17.

> 88 However, the failure of URAA to define start-up period has not gone unnoticed. See, Horlick, Gary N. and Eleanor C. Shea, "The World Trade Organization Antidumping Agreement" 29 J.W.T. 5 at 26 (1995, February).

> **89** See on this supra note 82.
**Table 11** : Decomposition of changes in log real per capita consumption, from 1990/91 to <u>1995/96</u>

|**Explanatory Variables**|Due<br>changes<br>in level|**Rural**<br>Due<br>changes<br>in return|Total|Due<br>changes<br>in level|**Urban**<br>Due<br>changes<br>in return|Total|
|---|---|---|---|---|---|---|
|S|-1.08|1.71|0.63|-0.06|2.14|2.08|
|Head sex (male)|0.00|0.02|0.02|0.00|-0.04|-0.04|
|Head education, secondary or<br>higher|0.01|-0.03|-0.02|0.02|-0.14|-0.12|
|Household size|0.05|-0.18|-0.13|0.03|-0.14|-0.11|
|# Boys|0.00|0.03|0.02|0.00|0.01|0.00|
|# Girls|-0.01|0.05|0.04|0.00|0.01|0.01|
|# Male adult with secondary<br>or higher|0.00|0.00|0.00|0.00|-0.04|-0.03|
|# Female adult with secondary<br>or higher|0.00|-0.01|0.00|0.01|0.01|0.02|
|Per capita asset holding|-0.03|-0.06|-0.09|-0.01|-0.08|-0.09|
|Access to credit|0.04|0.05|0.09|-0.04|0.18|0.14|

**Table 12** <u>: Econometric tests for instrumental variables approach</u>

|**Test/Equation**||**Ru**|**ral**||**U**|**rban**|
|---|---|---|---|---|---|---|
|||S|N|F||S|
||1990/91|1995/96|1990/91|1995/96|1990/91|<br>1995/96|
|Relevance test: F (2, N-23)|12.0|66.70|153.0|219.0|17.3|23.2|
|statistic (p-value)|(0.00)|(0.00)|0.00)|(0.00)|(0.00)|(0.00)|
|Overidentification test:|1.7|0.04|1.9|2.01|1.48|1.2|
|Chi2 (1) statistic (p-value)|(0.19)|(0.85)|(0.20)|(0.19)|(0.22)|(0.28)|
|Durban-Hausman-Wu test:|192.6|307.4|246.8|393.5|47.6|53.8|
|Chi2 (21) statistic (p-value)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Observations(N)|9342|9910|9342|9910|4561|7177|

26
27 Icafé, however, does not necessarily have p*, the true output price received by mills * for their coffee. Rather, they have the contract price _reported_ by the mill/exporter, p R . As explained in the previous section, mills must register their output sales contracts with Icafé. The price reported on these contracts is p*R . The mill-specific APCIcafé is deducted from reported contract prices, to yield the mandatory producer prices published by Icafé. That is: Rearranging, If the mill reports the true output price, then and APC<sup>Icafé</sup> is constant, then for the same p*, clearly the producer price that results from (6) is less than that which results from (5). Formally, _* Proposition 1: (p* - p R ) > 0 measures the margin that a mill/exporter has earned above its minimum long run average processing costs, APC_<sup>_Icafé_</sup> .

# <u>Proof</u>

p* - p*R > 0 (6) Substituting (4), this becomes p* - APCIcafé - pp > 0 this can be rewritten as p* - pp > APCIcafé and the mill/exporter has extracted a marketing margin greater than the minimum of their long run average processing cost. �

* This establishes that the measure (p*p R ) > 0 is a measure of mill/exporter marketing margins greater than APC<sup>Icafé</sup> . This is the first important result and links the intuitive * exposition of Figure 6 to the empirical measure (p*p R ) used in the next section.

> 27 * For a detailed exposition on the identity _p R_ ≡ APCIcafé + pp  , see the data description of  Icafé producer price calculations in Ronchi (2005).
# Table 7s Family background effects on Grade 8 mathematics achievement gain in Thailand, 1981-82a

|Independent|School|Location|
|---|---|---|
|<br>Variables|Rural|Urban|
|YMOREEDC|71***|92***|
||(4.31)|(5.14)|
|YPARENC|.01<br>|- 05<br>|
||(.04)|(.30)|
|YPERCEV|-1.30***|-1.66***|
||(6.76)|(9.62)|
|YFUTURE|1.17***|1.27***|
||(6.21)|(7.21)|
|YDESIRE|*75***|1.47***|
||(3.38)|(6.76)|
|YMHWXL|.11**|16***|
||(2.74)|(3.92)|

Notes

- (a) This table summarizes the results of 12 regression analyses, identical in design to those reported in Table 2.

- (b) Numbers are parameter estimates with t-statistics in parenthesis.

- (c) Effects controlling for student age, sex, paternal occupation, maternal education, household calculator ownership, household language use, and district per-capita income.

- 30 -
time _t_ as dependent variables.<sup>16</sup> Results are reported in Table 8 and show no significant effect on the number of bonds or in the size of corporate bond financing at the same time as, and in the two years after, MDB participation in a syndicated loan (columns 1 and 5). In the remaining columns we show that results are robust to the inclusion of controls for the presence of the top 10 banks and ODA in the previous 2 years. Only when we control for Chinese lending (columns 3 and 7) do we observe a marginally significant increase in the size of bond financing at _t+1_ . The cumulative effects are never significantly different from zero.<sup>17</sup> Overall, there is no evidence of crowding-out in the corporate bond market.

A further concern could be that MDBs’ entrance in a given country-sector pair may attract the private sector, but at the same time, crowd-out other sources of development financing. To mitigate this concern, we run the same exercise explained above but using either the number of projects financed by ODA flows or the number of officially-financed Chinese projects as dependent variables. In both cases, we do not find any statistical association between MDB participation and changes in official aid flows by OECD’s Development Assistance Committee (DAC) countries and official Chinese lending (see Table A9).

# **6 Conclusions**

Filling the investment gap to achieve the SDGs by 2030 is a major development challenge. As foreign aid and domestic revenue mobilization alone cannot meet the target, leveraging private investment is essential to make progress towards inclusive growth. In this respect, MDBs can play an important role in attracting private capital flows towards investment in developing countries.

We use granular data on international syndicated lending to evaluate whether MDBs can mobilize private capital flows. Our results indicate that once an MDB enters a country-sector pair through the participation in a loan syndication, the number of total syndicated loans and the associated bank lending flows increase. In addition, access to credit improves, since the average number of lending banks per loan and the average loan maturity also increase. These effects last over time and are economically sizable: for each dollar that MDBs invest in a country-sector pair over a three-year period, commercial banks lend almost 7 dollars.

These results are robust to a large set of tests controlling for the potential role of several con-

> 16As for the size of syndicated loans, we winsorize the bond size variable at 1%.

> 17Since the sample used to analyze effects on bonds is smaller, we test whether the absence of effects is driven by the sample construction, and re-estimate mobilization effects on syndicated loans in this same sample. Results are robust—see Table A8.

17
credit history for the borrower, these deals enhance the ability to obtain and reduce the costs of accessing capital markets in the future.<sup>32</sup> We estimate the potential size of future flow securitizations for various kinds of flows, including remittances, for Sub-Saharan Africa based on the methodology of Ketkar and Ratha (2001, 2005) using an over-collateralization ratio of five to one and average flows in 2003-06. These calculations indicate that the potential future flow securitization is $17 billion annually, with remittance securitization alone in the range of $2 billion (table 3). These include only the securitization of remittances recorded in the balance of payments. The actual unrecorded remittances through formal and informal channels are estimated to be a multiple in several countries (Page and Plaza 2006).

Remittances are a large and stable source of external financing that can be creatively leveraged for Sub-Saharan Africa’s development goals. Remittances can improve capital market access of banks and governments in poor countries by improving ratings and securitization structures (Ratha 2006). Hard currency remittances, properly accounted, can significantly improve a country’s risk rating. It may even encourage many poor countries that are currently not rated to obtain a credit rating from major international rating agencies (see the following for more discussion).

Table 3: Securitization potential in Sub-Saharan Africa (US$ billion)

||Sub-Sah|aran Africa|Low incom|e(excl. India)|All de|veloping|
|---|---|---|---|---|---|---|
||Receivable<br>($ bil.)*|Potential<br>securitization<br>($ bil.)|Receivable<br>($ bil.)*|Potential<br>securitization<br>($ bil.)|Receivable<br>($ bil.)*|Potential<br>securitization<br>($ bil.)|
|Fuel exports*|44.1|8.8|47.7|9.5|417.5|83.5|
|Agricultural raw<br>materials exports*|5.7|1.1|4.4|0.9|46.3|9.3|
|Ores and metals<br>exports*|13.4|2.7|5.0|1.0|106.6|21.3|
|Travel services**|12.4|2.5|4.8|1.0|168.5|33.7|
|Remittances**|8.4|1.7|23.6|4.7|179.4|35.9|
|Total|84.0|16.8|85.5|17.1|918.3|183.7|

* Average for 2003-05; ** average for 2003-06.

_Source:_ Authors’ calculations using over-collateralization ratio of 5:1. Data on exports are from the World Bank’s World Development Indicators. Worker remittances, defined as in Ratha (2003), are calculated from the IMF’s Balance-of Payments Statistics Yearbook 2007.

The African Export-Import Bank (Afreximbank) has been active in facilitating future flow securitization since the late 1990s. In 1996, it co-arranged the first ever future-flow

> 32 Obtaining a rating is important for raising not only bond financing or bank loans, but also foreign direct investment and even official aid (Ratha, De and Mohapatra 2007). Any improvement in sovereign rating is likely to translate into an improvement in the rating of sub-sovereign borrowers whose foreign currency borrowing is typically subject to the sovereign rating ceiling.

23