rafmacalaba/datause-encoder-data huggingface dataset:
PEB[ Working Paper Series

||TIL.<br>|~~~~~Author||Contact<br>tof paper|
|---|---|---|---|---|
|WPS596|The Mexican Sugar Industry:<br>Problems and Prospects|Brent Borrell|February 1991|P. Kokila<br>33716|
|WPS597|Rent Sharing in the Multi-Fibre<br>Arrangement: Theory and Evidence<br>from U.S. Apparel Imports from<br>Hong Kong|Refik Erzan<br>Kala Krishna<br>L.ing Hui Tan|February 1991|G. llogon<br>33732|
|WPS598|Africa Region Population Projections<br>1990-91 Edition|: Patience W. Stephens<br>Eduard Bos<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|0. Nadora<br>31091|
|WPS599|Asia Region Population Projections:<br>1990-91 Edition|Eduard Bos<br>Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|0. Nadora<br>31091|
|WPS600|Latin America and the Caribbean<br>Region Population Projections:<br>1990-91Edition|My T. Vu<br>Eduard Bos<br>Patience W. Stephens<br>Rodolfo A. Bulatao|February 1991|0. Nadora<br>31091|
|WPS601|Europe, Middle East, and North<br>Africa Region Population Projections<br>1990-91 Edition|Eduard Bos<br>: Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatac,|February 1991|0. Nadora<br>31091|
|WPS602|Firm Output Adjustmen**t**o Trade<br>Liberalization: Theory with<br>Application to the Moroccan<br>Experience|Mark A. Dutz|February 1991|S. Fallon<br>38009|
|WPS603|The Role of Officially Supported<br>Export Credits in Sub-Saharan<br>Africa's External Financing|Asli Demirguq-Kunt<br>Refik Erzan|February 1991|G. Ilogon<br>33732|
|WPS604|Foreign Trade and Its Relation to<br>Competition and Productivity in<br>Turkish Industry|Faezeh Foroutan|February 1991|S. Fallon<br>37942|
|WPS605|Overview of Contractual Savings<br>Savings Institutions|Dimitri Vittas<br>Michael Skully|March 1991|W. Pitayato-<br>nakarn<br>37666|
|WPS606|Adjustment Policies and Investment<br>Performance in Developing<br>Countries: Theory, Country Experie<br>and Policy Implications|Luis Serven<br>Andr6s Solimano<br>nces,|March 1991|E. Khine<br>39361|
This project is part of the response of the World Bank to the issues raised by Japanese and other East Asian officials regarding the appropriateness of government intervention in stimulating 3 industrialization and economic development.

It focuses on the role of government in the financial sector and seeks to establish under what conditions can government interventions make a positive contribution to economic growth and development. It also discusses the replicability of East Asian experience in other developing countries. The second section discusses the theoretical underpinnings of policy-based finance and suggests a framework in which this study attempts to inyestigate the role of government. The third section introduces the main findings of individual country studies (Japan, Korea, China and India), while the fourth section analyzes the similarities and differences in the credit polices among these countries in a comparative perspective. The final section briefly summarizes and concludes the paper.

-' This project is complementary to the "East Asian Miracle" project (World Bank 1993) and, in a less direct way, to the project on the "Main Bank System of Japan", sponsored by the Economic Development Institute. All three projects have received generous support from the Government of Japan and have relied extensively on inputs and contributions form Japanese and other East Asian officials, economists and practitioners. The paper also draws on the 1989 World Development Report on "Financial Systems anJ Development" (World Bank 1989).

2
31 relatively higher wages offered there. As shown in the regression in Table 22, women working for individual households earn significantly less than those employed in the public sector.

**Table 20. Wage Earners – Monthly Earnings, by Living Area, Gender, and Type of Employer**

**(in thousands of MRO)**

||<br>|_All_<br>|_R_<br>|_ural_<br>|_U_<br>|_rban_<br>|<br>|_Male_<br>|_F_<br>|_emale_<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||_Mean_|_Median_|_Mean_|_Median_|_Mean_|_Median_|_Mean_|_Median_|_Mean_|_Median_|
|_All_|30<br>_(3,5_|23<br>_31)_<sup>*</sup>|25.3<br>_(1,1_|20<br>_41)_<sup>*</sup>|32.7<br>_(2,_|25<br>_390)_<sup>*</sup>|32.2<br>_(2,_|25<br>_904)_<sup>*</sup>|19.5<br>_(_|15<br>_627)_<sup>*</sup>|
|_Public_|34.4<br>_(1,5_|28<br>_67)_|30.5<br>_(3_|25<br>_02)_|35.4<br>_(1,_|30<br>_265_|36.4<br>_(1_|30<br>_,265)_|26.2<br>_(_|23<br>_302)_|
|_Private_|39.2<br>_(5_|30<br>_56)_|35.5<br>_(1_|26<br>_30)_|40.8<br>_(4_|30<br>_26)_|40.7<br>_(_|30<br>_505)_|25.1|30<br>_(51)_|
|_Individual/_<br>_Household_|20.1<br>_(1,1_|15<br>_37)_|20.4<br>_(5_|15<br>_81)_|19.8<br>_(5_|15<br>_56)_|22.4<br>_(_|20<br>_901)_|10.1<br>_(_|8<br>_236)_|
|_Other_|30.8|20|25.5|20|36.4|20|33|20|16.1|10|
||_(2_|_43)_|_(1_|_14)_|_(1_|_29)_|_(_|_212)_||_(31)_|

Source: EPCV 2004. Numbers in parenthesis correspond to unweighed numbers of observations. * The number of observations is greater than the sum of observations over all employer types. This is due to missing data on employers.

_Wages appear to be higher in urban centers than in rural areas, although the effect is weak._ Average urban wages are approximately 30 percent higher than rural ones (see unadjusted wages in Table 20). Wages in the public sector are also greater in urban centers than in rural regions. The same is true for the private sector. But, wage earners employed by an individual or a household (e.g. maids) are offered similar wages regardless of whether they live in urban or rural areas. The coefficient of the rural variable included in the male and female wage regressions in Table 22 is not significant. This indicates that once other characteristics are controlled for, such as education or the sector of activity, the mere fact of living in a rural region has no impact on wage levels.

**Table 21. Wage Earnings – by Gender and Education (in thousands of MRO)**

||_All_<br> <br>|_Male_<br> <br>|_Female_<br> <br>|
|---|---|---|---|
||_Mean_<br>_Median_|_Mean_<br>_Median_|_Mean_<br>_Median_|
|_All_|30<br>23<br>_(3,531)_<sup>*</sup>|32.2<br>25<br>_(2,904)_<sup>*</sup>|19.5<br>15<br>_(627)_<sup>*</sup>|
|_None_|21.5<br>18<br>_(946)_|24.1<br>20<br>_(740)_|10.8<br>9<br>_(206)_|
|_Primary_|25.1<br>20<br>_(620)_|25.4<br>20<br>_(517)_|23.5<br>18<br>_(103)_|
|_Secondary_|32.9<br>28<br>_(972)_|35.4<br>30<br>_(758)_|23.4<br>23<br>_(214)_<br><br>|
|_Higher_|56.1<br>39<br>_(380)_|60.2<br>40<br>_(323)_|33.9<sup>a</sup><br>27<sup>a</sup><br>_(57)_|

Source: EPCV 2004. Numbers in parenthesis correspond to unweighed observations. * The number of observations is greater than the sum of observations over all education levels. This is due to missing data on education.
2020). This underscores the need for more carefully designed, streamlined and communicated economic assistance programs.

**Figure 7: SMEs rely significantly more on government grants and equity**

<!-- Start of picture text --> Percent of firms using equity finance as the main source<br>Percent of firms using government grants as the main source<br>Percent of firms using loans from commercial banks as the<br>main source<br>Percent of firms delaying payments to suppliers or workers<br>-15 -10 -5 0 5 10<br><!-- End of picture text -->

## Source: World Bank Enterprise Follow-Up Surveys

Note: Chart illustrates the average difference between SMEs and large firms within a country-sector pair with their standard errors. Country-sector fixed effects are included. Stratification weights adjusted for the followup enterprise surveys according to median eligibility were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market Finally, SMEs resort to payment delays to suppliers or workers as much as other competitors in the same location and sector.<sup>21</sup> This is despite their low bargaining power and a greater need for SMEs to retain their existing relationships with suppliers and workers. Likely reasons for this include the knock-on effects of late payments from large and other businesses and a lack of penalty, which could incentivize SMEs to delay payments from a practical standpoint. The former remains a long-standing issue for many SMEs. In Europe, for instance, about a third of SMEs in Poland, Italy and Bosnia and Herzegovina claim that late payments threaten the survival of their business (Intrum, 2020). The COVID-19 crisis exacerbates this issue and the liquidity problems faced by SMEs.

# 4. DFIs are better positioned to help SMEs in the recovery rather than the crisis

The COVID-19 crisis is expected to plunge many countries across the world into recession in 2020. Notwithstanding unprecedented policy responses, the baseline forecast predicts a 5.2 percent shrinkage in global GDP in 2020—the deepest global recession in eight decades (World Bank, 2020). The magnitude of the recession is expected to vary across regions, income groups, and by fragility status. The baseline scenario foresees a contraction in GDP of about 7 percent in advanced economies compared with only 2 percent in low and middle-income countries, the largest declines in GDP are expected to be recorded in the Latin America and Caribbean and in Eastern

> 21 The survey also indicates that small firms are on average as likely as large firms to stop repaying their loans to banks.

14
ures A.7 and A.9 show for the treatment variables and of the location histograms maps of origin of refugees in our sample. They confirm that there is sufficient variation in the treatment variables as well as on the province of origin in Afghanistan of the refugees surveyed in the census used for this study. The vast majority of changes in Taliban and U.S. allies territorial control occurred during 2020 and 2021 (see Figures 2 and 3). As such, the variation in our measure of exposure comes primarily from these two years.

# **IV.B Identification strategy**

We examine the impacts of Taliban exposure, U.S. allies exposure, and contested territories exposure on refugees’ outcomes using the following specifications: where _Yij_ represents our outcomes of interest including measures of i) integration (our primary outcome), ii) educational attainment, iii) mental health, iv) income and consumption, and v) labor market outcomes (ii to v describe our secondary outcomes). For each outcome, we evaluate the effects on multiple related variables and also combine them in an index to gain precision. The variables included in each of the indexes are summarized in Panels B, C, and D of Table 1, and described in Appendix B.A.<sup>13</sup>

> 13Indexes for dichotomous variables were constructed as the average of all outcomes. Indexes that include continuous variables were constructed standardizing each variable, averaging all variables, and standardizing the average once again. The methodology to construct indexes was adopted from Kling, Liebman and Katz (2007).

19
profiles: The Indifferent, the Copreneurs and the Regulators, depending on the degree to which the husband showed helpful and hindering behaviors. The Copreneurs are husbands who show low levels of constraining behavior and high levels of supportive behaviors; the Regulators show high levels of both constraining and supportive behaviors; the Indifferent show rather low levels of both constraining and supportive behaviors. Women entrepreneurs in Ethiopia are most successful (higher profits and more employees) when married to husbands who provide resources for the business (cf. the Copreneurs and Regulators) – even when those husbands at the same time engage in constraining behavior (cf. the Regulators). Women perform worst when their entrepreneurial efforts are ignored by their husband (cf. the Indifferent). Husbands who share their financial, social, and human capital resources with their wives can make a significant difference.

Nearly half of married female owners report having received some type of help in their business from their husband, but this help is not statistically significantly different between crossover and non-crossover women. However, some types of spousal help can increase the probability of crossing over while other types of help may hinder it. Specifically, contributing with money or giving moral support negatively impacts the probability of being a crossover in 2.4 percent and 12 percent respectively, while helping to procure a license, being a co-owner or helping with several aspects of the business is associated with a significantly higher probability of being a crossover.

**Table 5. Support from husband**

||(1)|(2)|
|---|---|---|
||Female C<br>|rossover<br>|
|Age of the respondent|-0.002***<br>(0.00)|-0.002***<br>(0.00)|
|Uniersit or collee|0.000|-0.003|
|vy g|(0.04)|(0.04)|
|More than secondar vocational trainin or arenticeshi|-0.046|-0.049|
|y, g ppp|(0.04)|(0.04)|
|Sd|-0.038|-0.038|
|econary|(0.04)|(0.04)|
|Pi|-0.059|-0.059|
|rmary|(0.05)|(0.05)|
|_Reason to start business_|||
||0.115***|0.112***|
|It is my family business|(0.02)|(0.02)|
||0.038*|0.037|
|I saw an opportunity in the market|(0.02)|(0.02)|
|I had an idea for a new roduct or invention|0.024|0.025|
|p|(0.03)|(0.03)|
|To be more creative|0.006|0.007|
||(0.03)|(0.03)|
||0.044*|0.050**|
|To make more money|(0.02)|(0.02)|
||0.035*|0.038*|
|To be more independent|(0.02)|(0.02)|

15
Chen, Q., D. Gray, P. N’ Diaye, H. Oura, and N. Tamirisa (2010). “International Transmission of Bank and Corporate Distress,” _IMF Working Paper_ WP/10/124.

Chudik, A., and M. Fratzscher (2011). “Identifying the global transmission of the 2007-2009 financial crisis in a GVAR model,” _European Economic Review_ , 55, 325-339.

Chudik, A., and M. H. Pesaran (2014). “Theory and Practice of GVAR Modeling,” _Federal Reserve Bank of Dallas Working Paper_ , No. 180.

Cunado, J. and F.P. de Gracia (2005). “Oil prices, Economic Activity and Inflation: Evidence for Some Asian Countries,” _The Quarterly Review of Economics and Finance_ , Volume 45, Issue 1, 65-83.

Dees S, di Mauro F, Pesaran MH, Smith LV (2007). “Exploring the International Linkages of the Euro Area: a Global VAR Analysis,” _Journal of Applied Econometrics_ , 22, 1-38.

Dees S, Holly S, Pesaran MH, Smith LV (2007). “Long-Run Macroeconomic Relations in the Global Economy,” _Economics: The Open-Access, Open-Assessment E-Journal_ 3, 1-56.

Duval, R., K. Cheng, K.H. Oh, and R. Saraf (2014). “Trade Integration and Business Cycle Synchronization: A Reappraisal with Focus on Asia,” _IMF Working Paper_ , WP/14/52.

Eickmeier, S., and T. Ng (2015). “How do US credit supply shocks propagate internationally? A GVAR approach,” _European Economic Review_ , 74, 128-145.

Favero, A. (2013). “Modelling and forecasting government bond spreads in the euro area: a GVAR model”, _Journal of Econometrics_ , 177, 2, 343-356.

Fischer, Andreas M. (1993). “Weak Exogeneity and Dynamic Stability in Cointegrated VARs,” _Economics Letters_ , Volume 43, Issue 2, 167-170.

Gray,D., M.Gross, J.Paredes, and M. Sydow (2013). “Modeling Banking, Sovereign, and Macro Risk in a CCA Global VAR,” _IMF Working Paper_ , WP/13/218.

Koop, G., M. H. Pesaran, and S. M. Potter (1996). “Impulse Response Analysis in Nonlinear Multivariate Models,” _Journal of Econometrics_ , 74(1) 119-147.

Hamilton, J. (1983). “Oil and the Macroeconomy Since World War II,” _Journal of Political Economy_ , 228-248.

Hamilton, J. (1996). “This is What Happened to the Oil Price-Macroeconomy Relationship,” _Journal of Monetary Economics_ , Volume 38, Issue 2, 215-220.

Hamilton, J. (2003). “What is an Oil Shock?,” _Journal of Econometrics_ , Volume 113, Issue 2, 363-398.

Han, F., and T. H. Ng (2011). “ASEAN-5 Macroeconomic Forecasting Using a GVAR Model,” _ADB Working Paper Series on Regional Economic Integration_ No.76.

37
direction of selectivity depends on the level of migration prevalence in a community, studies which estimate the average direction of selection will give different estimates if they draw on surveys from communities with differing levels of networks, coupled with differences in the extent to which they account for undercounting of more educated migrants who move with their entire families.

The results of this paper also suggest that as migration networks continue to develop, we should expect to see more negative educational selection of migrants from Mexico, due to the low returns to education in illegal jobs in the U.S. In contrast, if proposals to allow more legal immigration of unskilled workers are enacted, the higher returns to education should result in greater degrees of positive selectivity.

# **Appendix**

_Proof of Proposition 2:_ To prove (a), note first that<sup>_s_</sup> _L_<sup>and</sup> _sU_ are solutions of the following equation: μ 0 + δ 0 _s_ ( _n_ ) = μ 1 + δ 1 _s_ ( _n_ ) − _e_ μπ −γ 1 _s_ ( _n_ ) −γ 2 _n_ Differentiating (8) with respect to _n_ , we have: and therefore The marginal effect of an increase in network size on the two critical schooling thresholds is therefore given by: 19
<!-- Start of picture text --> ARG BOL BRA CHL COL CRI DOM ECU HND MEX PAN PER PRY URY<br>150-<br>z<br>é<br>e 100 -<br>g<br>iO mA. mF li " is<br>© em FS OO ee BS cs<br>ec otoS SF£€ OF€S OSSS OSSS EFCSS ESCS SSCS 8SGSS 88SS FSSS ESSS SS€$& SSS$<br>(> Income gap 1) Endowments Mill Returnsto endowments<br><!-- End of picture text --> <!-- Start of picture text --> ARG BOL BRA CHL coL CRI DOM ECU HND MEX PAN PER PRY URY<br>= 100 ~<br>|.i me. 2s "a "on<br>a) A pec FB Fes gach a<br>Ey CPee LEoo LFod LSoo EFoo EE8 BPSS OFPo BFoo OFOS OSPS OSPo OSoo EFOe<br>[5 Income gap Endowments lll Returnsto endowments<br><!-- End of picture text -->

<!-- Start of picture text --> BOL BRA CHL coL CRI DOM ECU HND MEX PAN PER PRY<br>2°<br>z<br>8 200 -<br>a oeo & aeoe osoeaeoe oe & oe aeoe oeoeoe oe oeoe & etoeoe 2 aeoe<br>[> Income gap lll Endowments Mill Returnsto endowments<br><!-- End of picture text --> <!-- Start of picture text --> ARG BOL BRA CHL COL cRI DOM ECU HND MEX PAN PER PRY URY<br>150—<br>=<br>E s0-<br>Ez<br>8LAFLPFLPLPELLPLPLELELELELFLF<br>[Income gap lll Endowments fll Returnsto endowments<br><!-- End of picture text -->
**Table 2: Can Human Experts Predict Employment After 3 Years?**

|||Non-Winners<br>|||Wi<br>|nners<br>||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Judges|McKenzie|FaWo|Judges|Judges|McKenzie|FaWo|
|Business Plan score|-0.0021|0.0009|0.0047|0.0897<sup>**</sup>|0.0490|0.0809<sup>*</sup>|0.0676<sup>*</sup>|
||(0.0168)|(0.0181)|(0.0181)|(0.0310)|(0.0306)|(0.0355)|(0.0335)|
|Female||-1.0909<sup>*</sup>|-0.7578|||-0.5887|-0.0360|
|||(0.4781)<br><sup>**</sup>|(0.4858)<br><sup>*</sup>|||(0.8149)<br><sup>*</sup>|(0.7675)<br><sup>*</sup>|
|Age||0.1060<br>|0.0790<br>|||0.1688<br>|0.1546<br>|
|||(0.0410)|(0.0395)|||(0.0801)<br>|(0.0761)|
|Graduate Education||0.9657||||-2.5224<sup>**</sup>||
|||(0.9788)||||(0.8966)||
|Worked abroad||0.1697||||-0.9984||
|||(0.8817)||||(1.0346)||
|Lottery choice||0.5653||||-0.4831||
|||(0.3496)||||(0.6551)||
|Wealth||0.0975||||0.1306||
|||(0.0681)||||(0.1316)||
|Grit||-0.4671<br>||||-0.4558<br>||
|||(0.3609)||||(0.5989)||
|Ability||0.4455<sup>*</sup>||||0.0892||
|||(0.2175)||||(0.3137)||
|Agriculture||-0.5736<br>||||-1.0380<br>||
|||(0.4540)||||(0.7790)||
|IT||-1.3354<sup>**</sup>||||-2.7006<sup>**</sup>||
|||(0.4927)||||(0.8364)||
|Manufacture||-0.4641|-0.0862|||-0.3856|0.0105|
|||(0.4545)|(0.4160)|||(0.9036)|(0.7894)|
|Retail|||0.6146||||-0.7114|
||||(0.9287)||||(0.8817)|
|Self-confidence|||0.1553||||0.1095|
||||(0.0940)||||(0.2036)|
|Motivation|||0.0909||||-0.2148|
||||(0.1141)||||(0.2026)|
|Happiness|||0.2122||||0.4886|
||||(0.1395)||||(0.2640)|
|Optimism|||0.1723||||-0.1442|
||||(0.1588)||||(0.2853)|
|Ability|||0.2808||||-0.0598|
||||(0.1860)||||(0.3199)|
|Credit|||-0.0502||||1.0291|
||||(0.1548)||||(0.5703)|
|Existing|1.6360<sup>***</sup>|1.4909<sup>**</sup>|0.4277|2.5845<sup>***</sup>|2.3326<sup>***</sup>|2.9470<sup>***</sup>|1.8239<sup>*</sup>|
||(0.4491)|(0.4674)|(0.5682)|(0.6311)|(0.6157)|(0.7506)|(0.8387)|
|Log(Total award paid)|||||3.8675<sup>***</sup>|3.8336<sup>***</sup>|3.8410<sup>***</sup>|
||||||(0.4327)|(0.4440)|(0.4459)|
|Regional Indicators|No|Yes|Yes|No|No|Yes|Yes|
|Sample Size|1,078|1,078|1,078|1,062|1,062|1,062|1,062|
|P-value||0.000|0.000|||0.042|0.018|
|AdjustedR<sup>2</sup>|0.013|0.039|0.049|0.021|0.057|0.078|0.084|

Robust Standard Errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. See Appendix A.1 for variable definitions. Dependent variable is number of employees 3 years after applying, coded as zero for applicants without an operating firm. P-value is for testing that set of controls added by MacKenzie (2015) or Fafchamps and Woodruff (2017) (abbreviated as FaWo) are jointly zero.

32
The baseline scenario assumes the continuation of past trends with no decline in trade costs, the details are presented in Annex A along with the population and GDP data in Tables 1-3. In the counterfactual scenarios we study the impacts of the BRI-induced trade cost reductions. We apply the lower bound estimates of trade cost reductions due to BRI-related infrastructure improvements from de Soyres et. al. (2018) to generate the low case scenario (BRILBD). The upper bound estimates allow for switching across transport modes leading to deeper reduction of trade costs and form our high case scenario (BRIUBD). Further gains could be realized if in addition to improving the transport infrastructure, the trade facilitation reforms would lead to halving of border delays for BRI countries (BRIUBBD). Finally, we augment the last scenario with two variants of complementary policies where BRI countries implement a 50% reduction of tariffs in trade with each other (BRIUBBTD) or reduce their tariffs in trade with all trading partners (BRIUBBTPD). The scenarios are summarized below:

|**Code**|**Description**|
|---|---|
|`BRILBD`|BRI lowerbound estimates|
|`BRIUBD`|BRIupperbound estimates|
|`BRIUBBD`|BRIUBBD andreduced borderdelays|
|`BRIUBBTD`|BRIUBBDandtariff reductionapplied ona preferentialbasis amongBRIcountries|
|`BRIUBBTPD`|BRIUBBD andtariff reduction applied on a MFNbasis byBRIcountries|

Table 4 summarizes the shocks for the regions in the BRI area. The shock occurs in two time periods. One-half of the shock is assumed by 2025, and the remainder by 2030. The fourth and last simulation involves tariff reductions applied on MFN basis. Figure 1 displays the trade weighted aggregated across sectors bilateral trade costs reductions on exports originating from countries on the left-hand side of figure in our central scenario (BRIUBD). Most of BRI-induced bilateral trade costs reductions are below 10%, but several exporters experience higher trade costs reductions to most destinations. Those biggest cost reductions on trade costs of exporting are expected in Kyrgyz Republic, Kazakhstan, Ethiopia, the Lao People’s Democratic Republic and Cambodia. We would expect those countries to be the biggest beneficiaries of the BRI. The full bilateral data set of trade costs reductions is presented in Table 4.

When analyzing the bilateral trade costs reductions by using the example of imports by China, we note that the differences across sectors of economic activity are very small, but systematically vary by the source of exports. Again, South Asia experiences the biggest trade cost reductions, while countries that are not part of the BRI such as the United States or Latin American and Caribbean countries (LAC) register very small gains (less than 1%) from better maritime connectivity of China with other regions.

- 5 -