rafmacalaba/datause-encoder-data huggingface dataset:
employees and sales are positive, but are with large standard errors and are not statistically significant. These results are consistent with many of the experiments surveyed by McKenzie and Woodruff (2014), which find business training improves business practices, but that it is harder to measure impacts on business performance in smaller samples.

Panel B of Table 11 examines heterogeneity in treatment effects by WTP. These results are of course affected by the same problems as for the average effect, and suffer even more from the lack of statistical power. Then it is not surprising that we find that most interaction coefficients are not statistically significant. We find only one statistically significant interaction: the treated with higher WTP are less likely to provide correct answers to the knowledge test.<sup>27</sup> In addition, the sign of 5 out of the 8 interaction coefficients goes in the opposite direction (i.e. positive). We therefore cannot say anything definitive about whether those with higher willingness to pay benefit more or less from training.

# **5 Conclusion**

Using two different approaches for experimentally eliciting the demand for business training, we find evidence for both the pros and cons of charging business owners to take part in training. We do find that the majority of business owners that express interest in training are prepared to pay to take part. Demand for training is downward sloping, but price-inelastic, so that increasing prices results in a less than proportional reduction in the proportion of business owners purchasing training. This shows the potential for business service providers to recover at least part of the cost of training through charging a price for the course.

Nevertheless, despite demand being inelastic, willingness to pay for most business owners is far short of the full cost of providing business training. In our BDM sample, demand falls from 76 percent of entrepreneurs attending at least one class when free, to only 11 percent when charged the full cost. Charging prices that recover costs will therefore exclude a large share of entrepreneurs, and also increase the cost of recruiting firms for training. We find higher prices are more likely to exclude those with smaller firms, more risk-averse entrepreneurs, and poorer individuals. Offering the chance to pay for the course in installments does not increase demand. However, while we do not have sufficient statistical power to determine whether those who are willing to pay more, benefit more from the

> table is the one on positive profits (the adjusted p-value is 0.034). We obtain family-wise adjusted p-values using the implementation by Jones et al. (2018) of the free step-down procedure of Westfall and Young (1993). Detailed results are available upon request.

> 27After adjusting the p-values for multiple hypotheses, the p-value for this coefficient becomes 0.043.

26
12. The Penn World tables estimates, when appliod to the published growth rates, imply income levels for China that are too high 3. Kravis initially published an estimate of China's real GDP per capita in 1975 as 12.3% that of the U.S. (lndia was 6.6%/o). However, that figure, '-upled vith China's growth rate during the last decade, would have put China at well over 20% of the U.S. per capita GDP in 1988, which most experts on Chin's economic development believe is too high.

13. The Cenos study, which is a production-based PPP comparison, was completed by Jeffrey Taylor in 1991. Taylor used producer price information from a variety of sources to estimate the average sectoral dolar/yuan price ratios for 1981. The price information for calculation of secoral PPP in the service sectors are iWrred by introducing input-output relationships between the services sectors and non-service sectors. His approach differs accordingly from the ICP approach (Taylor, 1991).

14. In an annex to the World Bank report entitled _China: Long-Term DeveIpment Issues and Oplions_ (World Bank, 1985), an input-output table for 1981 was compiled on the basis of a vaiety of published data. To identify "real" stnctural dfferences and sinilaiities between China and other counties, menmbers ofthe mission attempted to correct for these price differences between China and other cuntries on the very limited information. They estimated price adjustment coefficients in each sector and used them to modify the input-output table. In principle, each price adjustment coefficient should be the ratio of the average price of gross output in the sector in question in a typica **l** ow-income counfy to its average price in China, suitably cored for differences in commodity composition 3 The World Bank (1989b) reports a per capita growth rate figure of 5.5% from 1965 to 1988, implying adoubling every 13 years.

6
1. Find the maximum annual change in sectoral share from the LRS. If change over two consecutive observations occurred over several years due to gap in data, arrive at an average by dividing the total change by number of years. If the maximum annual change is less than or equal to 5 percentage points, go to step 2. Else drop the country from the analysis. Assume a maximum annual change of 5 percentage points for a country that has a single observation in its LRS.

2. Start with the data point that is nearest to the last/first observation in the LRS. Select the data point if the average annual change in sectoral share for all sectors is lower than the maximum annual change obtained in the previous step. However, if there is more than a year between the data point and the first/last data point from the LRS make sure that the total deviation between them remains below 7.5, 10 and 12.5 percentage points for gaps corresponding to 2, 3 and 4 or more years respectively. Exclude the data point if it fails to meet either of the two criteria. Proceed to the next data point and repeat the process but instead of using the nearest data point from the LRS, consider the nearest data point from the appended series that includes data points that have passed the inclusion criteria before.

The selection criterion leads to exclusion of just under 40% of the total observations. A significant portion of exclusion is driven by the fact that for roughly 20% of the 183 countries, there does not exist a data source that is independent of the concerns raised before.

I illustrate the how the selection process works with the help of an example. Consider the case of Bolivia for which sectoral allocation of workforce is available for 30 years from 1980 – 2009 with data missing for only one intermediate year. However, the series is constructed using different surveys for different years. For example, the data for 1980 – 1988 comes from official estimates and for 2004 – 2009 comes from Household/Labor Force Survey. But more importantly the data for 1989 – 1997 comes from surveys that were geographically constrained in main cities or urban areas.

The raw series from the WDI for Agriculture (red solid circles) and Services (blue solid squares) are shown in figure A.1. The share of employment in Agriculture during 1980 – 1988 hovers around 47% while the share of employment in Services rises gradually to just under 40% by 1988. However, both series observe a massive change in 1989 as the source used to construct the series changes. The massive jump in sectoral share of Services is driven by the fact the survey in 1989 from which the share is calculated was restricted in the main cities of the country. This led to enormous over representation of workforce in Services and an equally colossal under representation of workforce in Agriculture. The shares again return to trend levels beginning 1999 with a change in source survey used. The selection process outlined above helps in filtering out the spurious data 45
Table 8: Quarterly Drivers of Portfolio Debt Outflows by Sector, 2004–2014

||Pan|el A: All C|ountries|||
|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Reserves<br>Only|
|log(VIX_t−_1)|-1.469<sup>_∗∗∗_</sup>|-0.502|-0.615<sup>_∗∗_</sup>|-0.351|0.115|
||(0.503)|(0.388)|(0.300)|(0.384)|(0.485)|
|GDP Growth_it−_1|0.00391<br>(0.0159)|0.0135<br>(0.0100)|-0.00389<br>(0.00930)|-0.00571<br>(0.00573)|0.0280<sup>_∗∗∗_</sup><br>(0.0100)|
|Observations|1408|1408|1408|1408|1408|
|_R_<sup>2</sup>|0.011|0.008|0.005|0.003|0.007|
|CountryFE|Yes|Yes|Yes|Yes|Yes|
||Panel B:|Advance|d Economies|||
||(1)|(2)|(3)|(4)|(5)<br>|
||Total|Public|Banks|Corp.|Reserves<br>Only|
|log(VIX_t−_1)|-2.232<sup>_∗∗_</sup>|-0.455|-0.992|-0.784|0.951|
||(0.958)|(0.734)|(0.595)|(0.778)|(0.583)|
|GDP Growth_it−_1|0.0329|0.0334|0.00661|-0.00711|-0.00203|
||(0.0467)|(0.0291)|(0.0276)|(0.0166)|(0.00951)|
|Observations|660|660|660|660|660|
|_R_<sup>2</sup>|0.018|0.010|0.008|0.006|0.021|
|CountryFE|Yes|Yes|Yes|Yes|Yes|
|||Panel C:|EM|||
||(1)|(2)|(3)|(4)|(5)<br>|
||Total|Public|Banks|Corp.|Reserves<br>Only|
|log(VIX_t−_1)|-0.775<sup>_∗∗_</sup>|-0.545|-0.310<sup>_∗∗_</sup>|0.0796|-0.683|
||(0.351)|(0.316)|(0.132)|(0.171)|(0.774)|
|GDP Growth_it−_1|-0.00891|0.00358|-0.00840|-0.00409|0.0358<sup>_∗∗_</sup>|
||(0.00879)|(0.00559)|(0.00529)|(0.00392)|(0.0129)|
|Observations|704|704|704|704|704|
|_R_<sup>2</sup>|0.014|0.018|0.010|0.003|0.017|
|CountryFE|Yes|Yes|Yes|Yes|Yes|

Sample is from 2004q1-2014q4, countries as listed in Appendix A.4. Portfolio Debt outflow data is primarily from IMF BOP, as described in the text. Public outflows are defined as the sum of General Government and Central Bank outflows. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 39
45

1. The nominal yield curve.

2. The return on a 5-year bond.

3. The technical discount factor.

> As has been explained, this rate is _not_ regulated per se, but corresponds to the customary rate used in the computation of pension liabilities and reserves. It should reflect

> an average _long-run_ return on capital. Most pension providers now use a rate of 3.5 percent (or 3.25 percent), a number below the semi-official rate of 4 percent that had been used until recently.

Our _nominal yield curves_ are sourced from the central bank and are depicted in Figure 19 for end of June since 2000. The low level of interest rates reflects three particularities of the Swiss economy:

1. The traditionally low average inflation rates in Switzerland. Inflation has been even lower than usual in the last few years. As the yield curves suggest, inflation expectations are equally low.

2. A low real interest rate compared to neighboring countries. The Swiss real interest rates have been approximately one to two percentage points lower, on average, than in Germany.<sup>44</sup> 3. The extended period of sluggish growth since the early 1990s. Switzerland has experienced very low growth rates in recent years.

_Real yield curves_ cannot be computed due to the nonexistence of inflation-indexed bonds in Switzerland. The lack of the latter financial instrument reflects the high stability of monetary policy in Switzerland and thus the low demand for an inflation hedge. While this shortcoming does not pose a problem for estimates of MWRs since approximately 2000, it makes it difficult to value the annuities before 2000, when second pillar benefits were factually indexed to inflation.

As a consequence, we use _nominal_ yield curves or nominal discount rates together with nominal (non-indexed) annuities to compute the MWRs. Inflation has been very low in recent years, and indexing is rare, especially in funds using the statutory conversion factor. However, the use of the nominal yield curve is not free of problems either. Firstly, due to the interest rate differential with neighboring countries, a higher return on assets can be achieved at relatively low cost. The exchange between the Euro zone and Switzerland has been very stable for a long time, including the pre-Euro time. A well diversified portfolio of bonds can thus generate a considerably higher return at almost no additional risk. Secondly, it ignores one of the main objectives of the Swiss pension system to keep benefits relatively stable over time for different cohorts of pensioners. To get a more precise picture one would have to average out MWRs over a longer spell. Thirdly, the use of the nominal yield curve ignores the possibility that annuities may be partially adjusted to inflation if the latter reaches a sufficiently high level.

The MWRs computed with a constant nominal interest rate of 3.5 percent understate

> 44 See, for example, Kugler und Weber (2002) and (2003)
**Table 4: Heterogeneity: COVID-19 demand shock effect on gender employment gap by sector – top 2%**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||⍙**female**<br>**emp. AC**|⍙**male**<br>**emp. AC**|**gender**<br>**emp. **⍙**AC**|%⍙**male**<br>**emp AC**|%⍙**female**<br>**emp. AC**|**gender difference**in %<br>⍙in**emp. AC(M-F)**|
|Demand shock|-0.457*|-3.768***|-3.311***|-0.270***|-0.0789**|-0.183***|
||(0.254)|(0.914)|(0.923)|(0.0595)|(0.0388)|(0.0537)|
|Female sector (top|||||||
|<br>2%)|-1.737|-0.568|1.169|-0.200**|-0.125*|-0.0486|
||(2.075)|(2.506)|(3.204)|(0.0909)|(0.0667)|(0.0769)|
|Demand shock x|||||||
|female sector|-0.228|0.104|0.332|0.255**|-0.00331|0.230**|
||(2.120)|(2.705)|(3.403)|(0.103)|(0.0808)|(0.111)|
|Formal|<br>0.0694|<br>-1.976|<br>-2.045|<br>-0.193|<br>0.0387|<br>-0.232|
||(0.333)|(1.272)|(1.294)|(0.163)|(0.0438)|(0.163)|
|Export status BC|0.924|-2.319|-3.242|0.0990**|0.0877***|0.00759|
||(1.252)|(2.899)|(3.010)|(0.0463)|(0.0297)|(0.0465)|
|Medium size|-0.823***|-4.187***|-3.364**|-0.0136|-0.133**|0.119*|
||(0.290)|(1.307)|(1.332)|(0.0657)|(0.0525)|(0.0662)|
|Large size|<br>-2.717***|<br>-12.14***|<br>-9.425***|<br>-0.110|<br>-0.120**|<br>0.00194|
||(0.584)|(1.822)|(1.774)|(0.0680)|(0.0484)|(0.0624)|
|Private|1.450|-1.481|-2.932|0.0261|0.0533|-0.0350|
||(1.006)|(2.018)|(1.827)|(0.0703)|(0.0521)|(0.0571)|
|Ln(age)|-0.00572|-0.113|-0.107|-0.0161|-0.0269|0.0122|
||(0.171)|(0.636)|(0.634)|(0.0339)|(0.0233)|(0.0290)|
|Indus. Zone|0.821**|2.041*|1.220|0.0144|0.0572|-0.0359|
||(0.357)|(1.127)|(1.158)|(0.0582)|(0.0481)|(0.0638)|
|Female manager|-0.784|-0.857|-0.0724|0.0305|-0.0151|0.0423|
||(0.648)|(1.317)|(1.339)|(0.0546)|(0.0355)|(0.0610)|
|Technology|-0.163|2.551**|2.714***|0.179**|0.0789|0.107**|
||(0.398)|(1.037)|(1.038)|(0.0716)|(0.0516)|(0.0527)|
|R & D|1.158*|2.346*|1.188|0.0374|-0.0359|0.0721|
||(0.649)|(1.379)|(1.429)|(0.0734)|(0.0526)|(0.0629)|
|Good manag.|||||||
|<br>practices|-0.161|-0.555|-0.394|-0.0167|-0.0191|0.00161|
||(0.372)|(0.793)|(0.864)|(0.0700)|(0.0707)|(0.0689)|
|Training|0.00914|1.343|1.334|0.201***|0.0146|0.191***|
||(0.306)|(0.995)|(1.040)|(0.0623)|(0.0560)|(0.0666)|
|Constant|<br>-0.806|<br>3.911|<br>4.717|<br>1.979***|<br>2.096***|<br>-0.131|
||(0.983)|(3.239)|(3.213)|(0.208)|(0.108)|(0.192)|
|Gov. dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,143|2,143|2,143|2,142|2,130|2,129|
|R-squared|0.038|0.054|0.038|0.175|0.159|0.143|

*Demand shock is the sum of indicators of reported decline in quantity demanded and in export demand value. Gender gap is the change in the difference of the number of male and female employees after COVID ( ⍙ Male employment - ⍙ Female employment) . Weights are used. The method of estimation is ordinary least squares with Huber-robust standard errors (reported in parentheses). Significantly different from zero at *10% significance, **5% significance level, ***1% significance level.

17
# **Table 4: Panel A – Nested Logit Regressions**

Log Odds ratio between Renting and Owning

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**suburban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|
|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_||||||||||
|Czech Republic|-0.15|1.02 ***|-0.27 *|0.21 ***|||-0.32||-0.01|
|Estonia|-0.14|-0.21|-1.51|0.16|||-1.58 ***||-0.34|
|Hungary|0.57|-0.44|-0.96|1.20 ***|0.17|-2.26 **|-1.71 *|-0.74|-0.33|
|Latvia|-0.01|0.05|-0.45|-0.79||-0.19|0.45|-0.96 ***|-0.63|
|Lithuania||30.17||1.02|||-2.41||1.31|
|Poland|-4.36 **|0.36|-1.53|-0.65|0.29|-0.14|-0.10|-3.23 ***|-1.63 **|
|Slovak Republic|0.19|0.88 *|0.36|-0.32|-0.02|-0.72|-1.53|-0.62|-0.73|
|_EU-15 and Cyprus_||||||||||
|Austria|1.12 *|0.59|-0.48|0.84 ***|0.75 ***|-2.24 **|-2.34 **|-0.97 ***|-0.49|
|Belgium|-0.76 ***|0.42|-0.56|0.23|0.08|-1.96 ***|-2.32 ***|-0.78 ***|-0.60 ***|
|Cyprus|-0.23|0.39|-0.95|0.98 **|-0.38|0.57|2.79|-2.79 *|-2.57|
|Denmark|-0.42|0.09|-0.42 **|0.27 **|0.07|0.80|1.32|-1.22 **|-1.23 **|
|Finland|-0.18|0.62 **|0.08|0.36 ***|0.05|-4.93 ***|-5.49 ***|-0.19|0.26|
|France|-0.05|0.68|0.02|-0.02|0.05|-3.09 ***|-3.13 ***|-0.98 ***|-0.70 ***|
|Greece|-0.56|-0.33|-0.84|-0.03|-0.41|-1.64|-1.49|-1.43 *|-0.98 ***|
|Ireland|-0.90 *|0.00|0.10|0.14|0.09|-2.82 ***|-3.10 ***|-1.23 ***|-1.41 ***|
|Italy|-0.70|-0.03|-0.66 **|-0.29|-0.41|-2.22 ***|-2.10 ***|-0.44 ***|-0.09|
|Luxembourg|-0.12|1.62 **|-0.08|-0.16|0.03|-1.71 ***|-1.49 **|-0.82 ***|-0.04|
|Netherlands|-0.34 ***|-0.23|-0.45 ***|||-1.30 ***|-1.70 ***|-1.25 ***|-1.48 ***|
|Portugal|-0.45|0.30|0.07|0.53 **|0.44 **|-1.88 ***|-0.82|-0.68 ***|-0.26|
|Spain|0.87 ***|0.54 **|-0.12|-0.08|0.00|-0.53 *|-0.21|-0.52 ***|-0.83 ***|
|Sweden|-0.72 ***|0.16|-0.78 ***|-0.19 **|-0.05|-1.99 ***|-1.99 ***|-0.36 *|-0.37 *|
|United Kingdom|-1.00 ***|1.65 ***|-2.02 ***|0.10|||-2.35 ***||-1.07 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2007 8
# **1. Introduction**

In the mid-1980s, under a policy known as Doi Moi, Vietman started dismantling its command economy, creating in its place a market-oriented domestic economy, and opening its doors to international trade, foreign direct investment and development assistance. Under Doi Moi, Vietnam has achieved impressive rates of economic growth (5-10 percent per annum) and has reduced levels of absolute poverty (Glewwe, Gragnolati, and Zaman 2000). But Doi Moi has also seen a reduction in the scale and quality of public health services (at least in some areas'), the introduction of user fees, and the encouragement of a private health sector (World Bank and others 2001). Some have expressed concerns that these developments may be having damaging effects on health outcomes, especially among poorer households (Dahlgren 2000). The broad issue this paper addresses is how one dimension of human development-namely child survivalhas changed under Doi Moi. In doing so, we pay special attention to the situation facing poor households.

By international standards, and especially given its relatively low per capita income, Vietnam has achieved substantial reductions in, and low levels of, infant and under-five mortality. By the mid-i 980s, its rates were among the lowest in the developing world. The first issue we address is whether under Doi Moi this reduction in child mortality has been sustained. The Vietnamese government's own goal was to reduce the infant mortality rate (IMR) to 30%O (i.e., 30 per thousand live births) by the year 2000. There is some debate over the exact level of the IMR in Vietnam and hence over whether the government's goal for 2000 has been achieved. We assess the evidence on recent trends in child mortality from a variety of sources, and present some new estimates derived from the 1993 and 1998 Vietnam Living Standards Survey (VLSS).

The second issue we address is the issue of socioeconomic differentials in child survival. Previous research by one of the authors (Wagstaff 2000) suggested that by international standards, inequalities in infant and under-five mortality between poor and better-off children were extremely low in Vietnam. These results were based, however, on VLSS data covering survival and deaths among children over the period 1984-93. It is quite possible-and indeed the fear has been expressed by some-that gaps in child survival prospects between the poor and better-off may have started to open up in Vietnam. In this paper we use data from the 1998 VLSS to measure the extent of socioeconomic inequalities in child mortality over the period 1989-98. Comparing inequalities over the periods 1984-93 and 1989-98, we can see whether any recent reductions in child mortality have been evenly spread across the population, or whether the gains have been concentrated among better-off children.

1
28 Table 4: US Hard Care NTB Coverage Ratios for Imports from East Asian Economies

|Product(SITC)|China|Fiji|French<br>Polynesia|Hong<br>Kong|Indonesia|Rep. of<br>Korea|Macau|Malaysia|Philippines|Singapore|Taiwan,<br>China|Thailand|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|AlrGoadr(0 oY)|29.6-|48.0|0.0|41.r-3|<br>-484|- 26.5|-6..0--|-10.1|.<br>29.5|9.8|t6.8|19.3|
|Food & Live Animals (0)|_0.5_|51.3|0.0|10.8|6.4|3.8|0.0|_4.8_|28.8|3.4|15.4|37.7|
|Beverages<br>&tobacco(1)|0.0|-|-|0.0|0.0|0.0|-|0.0|0.0|0.0|0.0|0.0|
|Crude Materials (2)|_15.5_|0.0|0.0|5.4|0.0|0.3|30.9|0.1|0.0|1.5|6.4|0.1|
|Energy Producis (3)|0.0||-|0.0|0.0|0.0|-|0.0|0.0|0.0|0.0|0.0|
|<br>Anirnal & Vegetable Oils(4)|0.0|-||0.0|0.0|0.0||n.0|<br>0.0|0.0|0.0|0.0|
|All Manufactured Goods (5 to 8)|32.9|46.9|0.0|42.1|49.3|26.8|61.5|11.3|32.1|10.1|16.8|16.1|
|Chemicals (5)|0.8|0.0|0.0|0.0|0.0|0.4|0.0|0.0|0.0|0.0|0.1|0.0|
|Leather goods (61)|0.3||-|1.4|0.0|3.0|0.0|0.0|0.0|0.0|4.9|0.1|
|<br>Rubber Manufactures (62)|0.0|||0.2|0.0|0.0|0.0|0.0|0.0|62.2|0.1|0.8|
|Wood Manufactures (63)|0.0|0.0|--|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|<br>Yam & Fabrics (65)|90.9|0.0|-|92.6|97.0|63.1|92.2|98.9|72.6|91.5|92.5|43.2|
|[ron&Steel(67)|20.2|-||1.2|5.2|93.8||27.2|0.1|28.5|39.6|<br>S2.6|
|<br>Nonrerrous Metals (68)|0.0||-|0.0|0.0|0.2|--|0.0|0.0|0.0|0.0|0.0|
|Transport & Machinery (7)|0.9|0.0|0.0|0.0|0.0|11.6|0.0|0.0|0.0|3.0|_6.5_|0.5|
|<br>Nonelectric Machinery (71)|6.3||0.0|0.0|0.0|0.0|0.0|0.0|0.0|1.2|1.9|1.0|
|Electric Machinery (72)|0.0|--|0.0|0.0|0.0|20.9|0.0|0.0|0.0|5.4|11.6|0.0|
|<br>Transport Equipment (73)|0.2|0.0|0.0|0.0|0.0|3.3|0.0|0.0|0.0|7.2|0.0|3.5|
|<br>Travel Goods (83)|32.0|-|-|23.7|40.6|31.4|6.9|34.0|72.6|17.9|64.9|74.7|
|<br>Clothing (84)|93.2|68.7|0.0|94.9|98.3|70.9|99.6|77.2|80.6|9L.9|92.0|88.4|
|Footwear(85)|0.0|-||00|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Misc. Manufactures (89)|0.3|0.0|0.0|0.9|0.0|23.1|0.0|0.0|0.0|0.0|0.0|0.0|

I1 Hard Core NTBs include; tariff quotas; anti-dumping and countervailing duties; flexible import fees (variable levies); all quotas and prohibitions; voluntary export restraint; MFA quotas and consultations; and textile quotas and consultations.

Source: Computed using the World Bank - UNCTAD SMART data base.