rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Status of health facilities before and after abolition of user fees**

||**Governm**<br>|**ent hospita**<br>|**ls**<br>|**Private**<br>|**hospitals**<br>|
|---|---|---|---|---|---|
|**Variable**|**Before**|**Aft**|**er**|**Before**|**After**|
|**Proportion of communi**<br>|**ties in which pat**<br>|**ients had**<br>|**to pay for:**<br>***|||
|Consultation (%)|79.10|3.36||52.43|52.43|
|Antibiotics (%)|22.37|4.11|***<br>|95.24|96.60|
|Malarial drugs (%)|27.31|3.87|***|95.66|96.75|
|**Availability of medicine**|**and facilities**|||||
|Antibiotics (%)|97.80<br>|93.39|***<br>|94.09|94.20|
|Malarial drugs (%)|99.45<sup>***</sup>|96.56|***|99.39|99.46|
|Bed capacity (%)|64.06|76.59||24.77|21.63|
|Hours open per day|18.40|18.12||16.84|16.23|
|Observations|676|696||434|463|

Source: Own calculation from the 2002 UNHS community survey. *Significant at 10%; **Significant at 5%; ***Significant at 1%.

**Table 5: Impact of policy change based on administrative data**

||**All Uganda**||**Reg**|**ion**||**Observations**|
|---|---|---|---|---|---|---|
|||**Central**|**East**|**North**|**West**||
|**New cases and referrals**|||||||
|Under 5 years)|0.185***|0.263***|0.301***|-0.107|0.253***|1,902|
||(5.83)|(5.49)|(6.36)|(1.29)|(4.11)||
|5 years and above|0.309***|0.348***|0.429***|-0.031|0.494***|1,903|
||(9.59)|(7.78)|(7.60)|(0.33)|(10.39)||
|Referrals|0.259***|0.336***|0.359***|0.029|0.439***|1,878|
||(5.23)|(3.30)|(4.00)|(0.24)|(5.01)||
|**Ante- and perinatal cases**|||||||
|New antenatal cases|0.122***<br>(3.36)|0.018<br>(0.22)|0.241***<br>(3.16)|(0.021<br>(0.27)|0.270***<br>(4.19)|1,746|
|Re-attendances/referrals|0.165***|0.248***|0.520***|0.273**|0.413***|1,815|
||(5.37)|(3.09)|(6.52)|(2.35)|(3.71)||
|Deliveries at unit|0.278***<br>(7.98)|0.261***<br>(3.35)|0.286***<br>(5.15)|0.250***<br>(3.25)|0.459***<br>(6.76)|1,884|
|Deliveries handled by|-0.156***|-0.196*|-0.128*|-0.088|0.027|1,777|
|traditional birth assistants|(3.66)|(1.97)|(1.67)|(0.88)|(0.31)||
|Post natal cases|0.341***|0.057|0.304***|0.061|0.252|1,724|
||(7.13)|(0.89)|(6.05)|(0.89)|(4.28)||
|**Children’s care**|||||||
|Total weighed|0.382***<br>(7.44)|0.681***<br>(5.34)|0.352***<br>(4.13)|0.308**<br>(2.22)|0.337***<br>(4.21)|1,667|
|Vitamin A supplementations|0.613***|0.744***|0.785***|0.320**|0.787***|1,700|
||(12.28)|(7.45)|(9.10)|(2.51)|(9.15)||

t-statistics in parenthesis.

** significant at the 5% level; *** significant at the 1% level.

23
30

**Table 2**

**Testing Bequest Motives and Tax-Smoothing Approaches Fixed (Country & Time) Effects Panel Regressions Dependent Variable: Central Government Budget Surplus Share**

|Developed|Developing|Developed|Developing|
|---|---|---|---|
|Years : 1975 -1992<br>Reg3a|Reg3b|Reg4a|Reg4b|
|Control Variables||||
|1. Dependent Variable Lag 1<br>0.4326***<br>(0.127)|0.718***<br>(0.085)|0.4332***<br>(0.128)|0.7164***<br>(0.085)|
|2. Index of Political Rights<br>0.0242***|-0.0009|0.024***|-0.0006|
|(0.009)|(0.002)|(0.009)|(0.002)|
|3. Agricultural Share (-)<br>-0.0527<br>|-0.0939<br>|-0.0531<br>|-0.0957<br>|
|(0.273)|(0.075)|(0.277)|(0.074)|
|4. Manufacturing Share (+)<br>0.2484|-0.2389|0.2559|-0.2336|
|(0.22)|(0.215)|(0.221)|(0.216)|
|5. Trade Share (+)<br>-0.0035<br>(0.053)|0.0473*<br>(0.024)|-0.0047<br>(0.053)|0.0469*<br>(0.024)|
|6. Urban Population Share (+)<br>-0.0129***<br>(0.003)|-0.0039***<br>(0.001)|-0.013***<br>(0.003)|-0.0043***<br>(0.001)|
|7. Log of per capita Real GDP<br>0.191***|0.0462***|0.1896***|0.0517***|
|(0.046)|(0.017)|(0.046)|(0.017)|
|8. Government Crises (-)<br>-0.0002|-0.0029|-0.0001|-0.0031|
|(0.001)|(0.002)|(0.001)|(0.002)|
|9. Cost of Debt Servicing (-)<br>0.0347|0.0135|0.035|0.0133|
|(0.065)|(0.03)|(0.065)|(0.029)|
|10. Seigniorage (-)<br>-0.0897|-0.1209|-0.0856|-0.1236|
|(0.1)|(0.078)|(0.099)|(0.079)|
|Tax-Smoothing Variables||||
|11. Unanticipated changes<br>-0.0489*|-0.0913***|-0.049*|-0.0912***|
|<br>in real per capita govt exp (-)<br>(0.027)<br>(4 year, simple average)|(0.021)|(0.027)|(0.021)|
|12. Unanticipated changes<br>-0.0687|0.1097***|-0.0686|0.1089***|
|in per capita real GDP (+)<br>(0.051)<br> (4year,simple average)|(0.026)|(0.051)|(0.026)|
|Bequest-Motives Variables||||
|13. Expected per capita real<br>-0.6021**|0.0336|-0.586*|-0.0121|
|GDP Growth Rate (-)<br>(0.297)<br>(25 year construction)|(0.498)|(0.299)|(0.501)|
|14. Gini Coefficient (-)<br>0.0010<br>(adjusted, smoothed)<br>(0.0007)|-0.0015<br>(0.0034)|0.0010<br>(0.0007)|-0.0021<br>(0.0035)|
|15. Life Expectancy at age 65 (-)||||
|16. Youth Population Share (-)<br>0.0922<br>(0.193)|-0.2844*<br>(0.161)|||
|17. Working-Age Population Share (+)||-0.0488|0.2943*|
|||(0.209)|(0.171)|
|18. Elderly Population Share (-)<br>0.5076*|-1.1418|0.4291|-1.2178|
|(0.294)|(0.982)|(0.288)|(0.976)|
|19. constant<br>-1.1902***|0.0181|-1.1109***|-0.2338*|
|(0.401)|(0.148)|(0.36)|(0.136)|
|R-squared<br>0.8691|0.8350|0.8690|0.8464|
|Adjusted R-squared<br>0.8375|0.7917|0.8374|0.8060|
|F-Statistic<br>45.96|44.94|45.21|70.35|
|F-Stat df 1<br>51|65|51|65|
|F-Stat df 2<br>211<br>Number of Countries<br>20|251<br>35|211<br>20|251<br>35|
|Number of Observations<br>263<br>Standard Errors are in parentheses.<br>*, **, *** denotes significance at the 90, 95 and 99 percent confidence leve<br>Robust standard errors obtained using White's correction for heterosceda|318<br>l respectively<br>sticity.|263|318|
conclusive. In general, these results confirm that our baseline findings are not driven by one or two particular regions.

This relationship is also observed across almost all income groups except for lower-middle income countries(table 5). In high income countries, 3 years after a disaster episodes associated with an official declaration of emergency, NPLs see a cumulative increase by 0.72 percentage points. In upper-middle income countries, the estimate is around 0.77 percentage point one year after an episodes with multiple types of disasters. The effects of climate and environmental related disasters on system-wide NPLs seem to be much larger in low income countries than in other income groups: one year after an episode with official declaration, there is a 5.8 percentage point increase in NPLs. Result for lower-middle income countries is not conclusive as it depends on the This paper also finds that several different types of disasters are estimated to have a significant effect on the level of system-wide NPLs, including volcanic activity, wildfires, landslides and droughts(table 6). To study the effect of individual types of severe disaster episodes on NPLs, we look at episodes of each of the eight types of disasters in our sample associated with an official declaration of emergency. Two years after a volcanic activity episode associated with an official declaration of emergency, system-wide NPLs are estimated to have increased by 0.65 percentage points. One to two years after a wildfire associated with an official declaration, the cumulative increase of NPLs is estimated to be between 0.5-0.85 percentage point. The magnitude of the effects are estimated to be similar for landslides and droughts as well: for the former, the estimates is 0.83 percentage points two years after impact; for the later, the estimate stands at 0.54 percentage point after three years. In this specification, results are inconclusive regarding the other disaster types in our sample, i.e. storms, extreme temperature, earthquakes and floods. However, we think this is mainly due to data limitations at the country level. Studies using more granular data and anecdotal evidences show that these types of disasters may also have a significant impact on system-wide NPLs and other important financial sector variables.

Lastly, we try to validate our results for NPLs using a couple of additional placebo test/robustness checks. First, we use the whole sample of disaster episodes, including type i and type ii under the EM-DAT classification and finds that our estimates on the impact on NPLs become statistically and economically insignificant. This is consistent with our finding that severe natural disasters are linked to a rise in NPLs. Second, we run the same regression but lag the dependent variable by one period, and find that disasters happening at time t do not affect the outcome at t-1.

11
conditions contribute to the health improvements realized from the program, or were they unnecessary? We carry out exploratory analyses to shed further light on both questions.

Appendix Figure A2 presents four village-level scatterplots, each containing 80 data points: 40 showing treatment village averages (in black circles), and 40 showing control village averages (in grey squares), with separate linear fits for each set of 40. We plot on the y-axis the average change in the number of sick days in the last month between baseline and follow-up (positive numbers indicate an increase in sick days, negative numbers a decrease). On the x-axis, sub-figure (a) has the village average number of clinic visits per person at baseline, while sub-figure (b) has the share of the village already complying with the health conditions at baseline. Each sub-figure features a plot for each of the two follow-up surveys.

Sub-figures (a) and (b) tell a similar story; reductions in the intensive margin of illness (sick days) show up at midline for a subset of the population (even if there are no significant reductions in the aggregate, as shown in our regressions): those already complying with or exceeding program health conditions at baseline and those with more clinic visits at baseline (call these “the compliers”). This suggests that it was not the conditions driving initial (midline) health improvements; it was the income effect of the transfer on individuals already visiting clinics frequently. However, by endline, the story reverses. It was those who were not complying with the conditions at baseline and those with fewer clinic visits at baseline (call these “the non-compliers”) who saw the greatest reductions in sick days.

Our regressions showed that treatment boosted health clinic visits at midline. Appendix Figure A3 further reveals that this midline increase in clinic visits was larger among noncompliers than among compliers.<sup>28</sup> This suggests that the CCT program helped individuals in two waves. First, compliers experienced a mild reduction in sick days at midline, likely due to income effects of the CCT. Second, non-compliers experienced health benefits, but with a lag. By midline they were induced to increase their health clinic visits—likely due to both the conditions of the program and the income from the transfer permitting them

> 28This is also the case at endline, where despite no aggregate increase in clinic visits at endline due to treatment, we see increases for those with low rates of compliance with health conditions at baseline.

24
Table 5. Variability and growth rates for export volumes, values, and unit values and domestic production of commodity exports for Malawi.'

|||19|61-73|||1|974-87|_|
|---|---|---|---|---|---|---|---|---|
||Volume|Value|Unit<br>Value|Production|Volume|Value|Unit<br>Value|Production|
|Coffee|28<br>(2.2)|34<br>(2.6)|16<br>(0.2)|23<br>_(5.5)_|61<br>(23.8)|59<br>(20.0)|39<br>_(-5.0)_|47<br>(15.6)|
|Cotton|50<br>(8.2)|44<br>(5.4)|13<br>(-2.8)|25<br>(4.6)|90<br>(6.0)|81<br>(-7.2)|30<br>(-9.3)|28<br>_(3.5)_|
|Sugar|157<br>(33.3)|201<br>(35.8)|23<br>(7.0)|39<br>(25.1)|30<br>(6.6)|49<br>(-1.1)|30<br>(-10.0)|18<br>(6.7)|
|Tea|8<br>(5.3)|8<br>(0.9)|_5_<br>(-4.2)|9<br>(5.0)|7<br>(2.9)<br>|36<br>(-O.1)<br>|30<br>(-3.0)|10<br>(2.8)|
|Tobacco|12<br>(7.0)|12<br>(7.8)|11<br>(0.6)|23<br>_(5.9)_|17 <br>(5.7)j|114<br> (1.6)|18<br>(-5.1)|11<br>(5.9)|
|Groundnuts|40<br>_(5.0)_|34<br>(4.5)|12<br>(-1.3)|10<br>(3.3)|52<br>(4.6)|_50_<br>(-9.7)|34<br>(-3.7)|3<br>(7.6)|
|Maize|103<br>(6.6)|98<br>(7.5)|27<br>(2.2)|12<br>(4.1)|137<br>(19.1)|148<br>(16.3)|84<br>(-16.4)|10<br>(9.0)|
|TotalEx-<br>**ports**||8<br>_5.4|_|||14<br>(0.7)||_|

aCVs for instability and average annual growth (in parenthesis).

22
$US, base year 2000) and real imports (in constant $US, base year 2000) from the World Development Indicators since 1960, four events are identified: 1975, 1982, 1991, and 2001. These events are readily seen as the sharp downturns in Figure 1. In the remainder of this note, the downturn year is denoted as year zero.

# **III.  What Happens to Trade During Downturns?**

To estimate trade effects of recessions, we need an estimate of the elasticity of trade to income—the percentage by which trade falls for a one percent change in income. A number of papers have estimated income elasticities of imports or exports for individual countries and generally find them to lie between 1 and 31⁄2 (see, for example, Hooper et. Al 2000 and Kwack et. Al. 2007). Most forecasters use an elasticity of about 2: real trade growth is expected to slow by 2 percentage points for each 1 percentage point deceleration in real income growth. This leads to relatively small estimates of the decline in trade relative to what we have seen. For example, the IMF<sup>1</sup> predicts a 2.8 percent decline in real trade. In June, the World Bank<sup>2</sup> revised its March forecast from a 6.1 percent decline in real trade to a 9.7 percent contraction, only after reducing sharply the forecast for global growth. The World Bank estimates are larger in magnitude than the IMF estimate because the predicted declines in income growth are greater. In addition, the Bank’s estimate is augmented with potential effects from trade finance problems.

In Table 1, we reexamine the relationship between trade growth and income growth. Specifically, we estimate a very simple regression of world real trade growth on

> 1 See http://www.imf.org/external/pubs/ft/weo/2009/update/01/index.htm.

> 2 See http://web.worldbank.org/external/default/main?theSitePK=659149&pagePK=2470434&contentMD K =20370063&menuPK=659159&piPK=2470429.

5
country-product-year or country-destination-year cell includes a single firm whose individual information cannot be revealed.<sup>30</sup>

**Table 1: Definition of Measures**

||||**LEV**|**EL**<br>**Country**|**– Year –**|
|---|---|---|---|---|---|
|**CODE**|**MEASURES**||**Country**<br>|<br>**Pro**|<br>**duct**|
|||**Country**<br>|**- Year -**<br>|||
|||**- Year**|**Destinati**<br>**on**|**HS2 &**<br>**HS4**|**HS6**|
|**A**|**BASIC CHARACTERISTICS**|||||
|**1,2,3,4,5**|N (Exporters, Entrants, Exiters, Survivors, Incumbents)|||||
|**6,7,8,9,10**|MMS & Q1Q3 (TEV per Exporter, Entrant, Exiter, Survivor, Incumbent)|||||
|**11**|MMS & Q1Q3 (Growth of Incumbents)t= MMS (ln(TEV of incumbenttin t) –<br>ln(TEV of incumbenttin t-1))|||||
|**12**|MMS & Q1Q3 (Growth of Survivors)t= MMS (ln(TEV of survivortin t+1) -<br>|||||
||ln(TEV of survivortin t))|||||
|**B**|**CONCENTRATION/DIVERSIFICATION**|||||
|**1**|Herfindahl Index|||||
|**2**|Share of top 1%, 5%, 25% Exporters in TEV|||||
|**3**|MMS (N. HS6 Products per Exporter)||||NA|
|**4**|MMS (N. Destinations per Exporter)||NA|||
|**5**|MMS (N. Exporters per HS6 Product)||||NA|
|**6**|MMS (N. Exporters per Destination)||NA|||
|**C**|**FIRM DYNAMICS**|||||
|**1**|Firm Entry Ratet= N. Entrantst/ N. Exporterst|||||
|**2**|Firm Exit Ratet= N. Exiterst/ N. Exporterst-1|||||
|**3**|Firm Survival Ratet= N. Survivorst/ N. Entrantst|||||
|**4**|Share of Entrantst= TEV of Entrantstin t / TEV in t|||||
|**5**|Firm 2-year Survival Ratet= N. 2-year Survivorst/ N. Entrantst|||||
|**6**|Firm 3-year Survival Ratet= N. 3-year Survivorst/ N. Entrantst|||||
|**D**|**PRODUCT DYNAMICS**|||||

> 30 For such cells the number of exporters shows a value of 1 and most measures except those based on exiter firms in Table 1 are missing. The developed countries in our sample applied their own confidentiality rules (often stricter). Due to confidentiality reasons, the information in the CYH6.dta file is not provided for Brazil, Belgium, Egypt, New Zealand, Spain, Sweden, the information in the CYH4.dta files is not provided for Brazil, New Zealand and Sweden, the information in the CYH2.dta and the CYD.dta files is not provided for New Zealand. A file with the percentage of total exports corresponding to the hidden values by country and year, for the developing countries for which we have the raw exporter-level data is available at http://data.worldbank.org/data-catalog/exporter-dynamics-database.

16
is debt financed. It is the absence of TFP growth in this sector that has generated the need for greater credit growth for the same increase in GDP. Of course, part of the decline in TFP reflects inadequate measurement of output in the sector. Capital used in housing and government-related infrastructure produces a real return but, by design, this return is not included in the official measure of GDP.

Overall, these estimates of sectoral trend growth rates combine to show a potential growth rate of the whole economy in line with government target for overall GDP growth of around 61⁄2 percent (Table 11). A noteworthy feature of these estimates is that while the level of business sector employment has been flat for the past decade, the quality-adjusted labor force has risen by nearly 7%. This reflects the continuing out movement from agriculture.

**Table 11 Estimated trend GDP growth rate by sector**

||Business<br>sector|Housing,<br>Government,<br>Infrastructure|Whole<br>economy|
|---|---|---|---|
|2012|8.2|7.1|8.1|
|2013|8.3|6.7|8.1|
|2014|7.8|6.4|7.6|
|2015|6.5|6.6|6.5|

_Source_ : Author estimates _Note_ : The whole economy trend is the result of adding the estimated level of trend GDP in the two sectors.

In the next decade, the latest UN population projections suggest a marked fall in the labor force aged 20-54. The scale of outflows from agriculture will need to increase in order to give a bigger boost to the quality of the labor force. In addition, policies designed to keep people in the labor force longer could take advantage of the increase in the 54 to 64 age-group likely in the next decade. The activity rate in this age-group is lower than the younger age-group but even so, it is likely to be sufficiently high to ensure that the quality-adjusted labor force available continues to grow in the period to 2030, with a slowdown of only a few tenths of a percentage point.

The prospects for the growth of the capital stock are more difficult to evaluate since they are clearly endogenous to the performance of the economy. The growth of the business sector capital stock was abnormally low in 2015 due to a poor performance in the industrial sector, some rebound is likely in the short-term, but in the longer-term a rebound in capital formation in the business has to be driven by an increase in total factor productivity.

27
seminal paper, Limao and Venables (2001) look at the quality of transport infrastructure as a whole. Other papers take up specific aspects of infrastructure such as port efficiency (Sanchez _et al._ 2003), different port characteristics (Wilmsmeier _et al._ 2006), or port infrastructure endowments (Wilmsmeier and Hoffmann 2008). The latter paper also addresses aspects of connectivity between ports,<sup>2</sup> as do Marquez-Ramos _et al._ (2006). In contrast, few papers investigate public policy. For instance, Wilmsmeier and MartinezZarzoso (2010) focus on the impact of being an open registry country whereas Clark _et al._ (2004) study the impact of anti-competitive practices in the liner shipping sector. Fink _et al._ (2002) quantify the effect of certain policies relative to other determinants of trade costs and find that both public policy—in the form of mandatory port services—as well as private anticompetitive practices have a substantial effect on transport costs.

This paper makes three principal contributions: first, we estimate the impact of restrictions on maritime transport costs and seaborne trade flows, highlighting in particular the role of investment barriers which have not been studied before. Second, we examine how distance affects maritime transport costs and, hence, seaborne trade flows. Third, we trace out how the impact of policy barriers on maritime transport costs itself varies with distance. To the best of our knowledge, this is the first paper to focus attention on the cost-inflating effect of a comprehensive set of measures, and to disentangle the various channels linking policy, distance, transport costs and trade flows.

We find that more restrictive liner shipping policies are associated with appreciably higher shipping costs, and investment restrictions matter most. Specifically, maritime transport costs are between 24 and 50 percent higher compared to ‘open’ routes, depending on the level of restrictiveness. Along the extensive margin, the probability of observing bilateral trade is between 17 and 25 percentage points lower on routes with policy barriers as compared to open routes. Thus, the cost-inflating effect is substantial in magnitude and, as we show below, varies with distance. In terms of the derived effect on seaborne trade, we estimate that policy barriers lower trade flows by 28 to 46 percent, primarily through raising transport costs.

> 2  UNCTAD has pioneered the construction of composite indices summarizing the frequency, capacity and quality of services to and from countries, see UNCTAD’s ‘Liner Shipping Connectivity Index’ (LSCI).

3
|**HS**||||Year|||
|---|---|---|---|---|---|---|
|<br>**code**<br>**2 digits**|**_Type of products_**|2001|2002|2003|2004|2005|
|||85|85|85|85|85|
|11|_MILLING PRODUCTS_|10.0|10.0|10.3|10.2|10.0|
|||_(0.0)_|_(0.0)_|_(1.6)_|_(1.9)_|_(2.7)_|
|||84<br>|84<br>|84<br>|84<br>|84<br>|
|12|_OIL SEED_|5.0|5.0|4.9|4.9|4.9|
|||_(0.0)_|_(0.0)_|_(0.8)_|_(0.8)_|_(0.8)_|
|||18<br>|18<br>|18<br>|18<br>|18<br>|
|13|_LAC GUMS RESINS_|5.0|5.0|5.0|5.0|5.0|
||_, ,_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|14|_VEGETABLE PLAITING_|8<br>13.8|8<br>13.8|8<br>13.8|8<br>13.8|8<br>13.8|
|||_(3.5)_|_(3.5)_|_(3.5)_|_(3.5)_|_(3.5)_|
|||138|144|142|148|148|
|15|_FATS ANIMAL VEGIE_|10.7|11.7|12.3|12.4|11.9|
||_, ,_|_(4.9)_|_(6.6)_|_(7.7)_|_(8.6)_|_(6.7)_|
|16|_MEAT FISH PREPS_|96<br>25.0|96<br>31.2|96<br>36.6|96<br>31.8|96<br>35.2|
||_, ,_|_(17.5)_|_(49.5)_|_(46.8)_|_(28.8)_|_(50.2)_|
|||55<br>|55<br>|76<br>|76<br>|76<br>|
|17||19.6|22.7|18.5|45.8|23.5|
||_SUGARS_|_(15.9)_|_(20.9)_|_(30.1)_|_(58.3)_|_(41.5)_|
|||29|29|29|29|29|
|18|_COCOA AND COCOA PREP_|11.9|18.8|19.2|16.0|16.5|
|||_(12.2)_|_(20.1)_|_(16.5)_|_(14.5)_|_(14.4)_|
|||51|51|51|51|51|
|19|_CEREALFLOURSTARCH_|15.3|16.3|18.6|18.1|18.2|
||_,,_|_(2.2)_|_(4.3)_|_(9.1)_|_(8.3)_|_(9.0)_|
|||356|356|356|356|356|
|20|_VEGIE FRUIT PREPS_|14.5|15.2|15.0|14.8|14.8|
||_, ,_|_(2.9)_|_(4.1)_|_(3.9)_|_(4.1)_|_(4.2)_|
|||49|50|60|60|60|
|21||14.7|14.6|18.2|15.4|15.2|
||_MISC EDIBLE PREPS_|_(4.2)_|_(4.0)_|_(20.4)_|_(5.6)_|_(5.0)_|
|||176|175|176|176|176|
|22||25.1|19.8|19.6|19.7|21.5|
||_BEVERAGES, VINEGAR_|_(25.8)_|_(19.6)_|_(17.1)_|_(22.8)_|_(31.5)_|
|||67|67|68|68|68|
|23|_RESIDUES, WASTES_|7.7|7.7|7.6|7.8|7.9|
|||_(5.8)_|_(5.9)_|_(6.2)_|_(7.0)_|_(7.1)_|
||_TOBACCO_|30|30|31|31|31|

26