rafmacalaba/datause-encoder-data huggingface dataset:
# **Acknowledgements**

This report was prepared by Paul B. Siegel and Jeffrey Alwang (consultants).

Panos Varangis (ARD) was the Task Team Leader (TTL).

The authors acknowledge the technical assistance of Erin Bryla (consultant, ARD), who provided some of the background materials used for the report, and also provided helpful comments and suggestions. Additional inputs were received from Bryan Lewin (consultant, ARD), Jorge Tiemier (EDE Consulting), Paul Trupo (USAID-DR). Comments and suggestions were provided by Steve Jaffee (PRMTR) and Francisco Pichon (LCSER) Some of the information in this report was collected for the Commodity Risk Management Group’s Phase II Study on the coffee and cocoa sectors, including the survey of producers. During the team’s mission, from May 27 – June 1, 2003, meetings were held with representatives of the coffee and cocoa sectors. These meetings were coordinated with the Coffee Council (Consejo Dominicano del Café) and the Cocoa Department and Cocoa Commission to represent the public and private sectors, producer associations, exporters and processors. The authors thank the participants at these meetings for generously giving of their time and opinions, and to Pedro Pablo Pena (CEDAF) who provided assistance for the team mission to Dominican Republic.

The authors also would like to acknowledge the useful comment by the peer reviewers Donald Larson (DECRG) and Nadim Khouri (LCSER).

Finally, the authors would like to express thanks to Cicely Spooner, Elaine Wylie, and Melissa Williams (ARD) for providing administrative support for this study.

# **Note from the Authors**

Some inconsistencies exist in Dominican coffee and cocoa data. In some cases, discrepancies result from rounding, use of different units of measure, different reporting years, etc. In other cases, discrepancies and inconsistencies are more difficult to reconcile. The authors have attempted to use consistent data series and reconcile inconsistencies, but this was not always possible.

5
Table C.5. Impacts of Extremism on Mental Health Excluding Provinces with more than 50% of Taliban Control

|Variables are indicators|Mental Health<br>Issues<br>Index|Felt Little<br>Interest<br>in Doing<br>Things|Felt<br>Depressed<br>or Hopeless|Had<br>Eating<br>Problems|Suicidal<br>Thoughts|Ask<br>Mental<br>Health<br>Assistance|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Panel A. Taliban Exposure_|||||||
|_ETi_|0.115***|0.066|0.147**|0.187***|0.018|1.750***|
||(0.034)|(0.083)|(0.072)|(0.055)|(0.019)|(0.445)|
|FDR q-values|[0.002]|[0.165]|[0.031]|[0.002]|[0.161]|[0.001]|
|R-squared|0.034|0.027|0.035|0.041|0.032|0.406|
|_Panel B. Contested Territories Exposure_|||||||
|_ECi_|0.037***|0.009|0.014|0.112***|0.006|0.476***|
||(0.013)|(0.032)|(0.028)|(0.022)|(0.007)|(0.137)|
|FDR q-values|[0.007]|[0.655]|[0.587]|[0.001]|[0.490]|[0.003]|
|R-squared|0.033|0.027|0.034|0.044|0.032|0.397|
|_Panel C. U.S. - Allies Exposure_|||||||
|_EAi_|-0.032***|-0.010|-0.021|-0.082***|-0.005|-0.399***|
||(0.010)|(0.025)|(0.022)|(0.017)|(0.006)|(0.107)|
|FDR q-values|[0.003]|[0.513]|[0.300]|[0.001]|[0.300]|[0.001]|
|R-squared|0.033|0.027|0.034|0.043|0.032|0.402|
|Mean Dep. Variable|0.161|0.301|0.199|0.108|0.011|0.768|
|Observations|5,381|5,346|5,369|5,364|5,359|259|
|_Controls in All Panels_|||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Mental Issues Index is constructed using the average of the variables in columns (ii) to (vi). The average is calculated with the number of variables available per individual, the index do not take into account the variables with missing values. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

65
**_<u>________________________________________________________</u>_**

The UNCTAD maintains a database of tariff and non-tariff measures. This database, the Trade Analysis and Information System (TRAINS) database, contains tariff-line information on Trade Control Measures (tariff and non-tariff measures) classified according to the UNCTAD Coding System of Trade Control Measures.

UNCTAD also produces <u>a</u> _<u>Handbook of International Trade and Development Statistics</u>_ . This Handbook, based on existing international and national data sources, is intended to provide a complete basic collection of statistical data relevant to the analysis of world trade and development for the use of the UNCTAD, government officials, university and research specialists, and others interested in these subjects.

A paper by _<u>Francis Ng and Alexander Yeats</u>_ has a statistical data appendix on trade barriers in 80 developing countries. The tables provides details as to how this information was used to compute country specific information on trade and non-tariff barriers, and also gives detailed statistics on these national trade barriers and the profiles of protection in the developing countries.

# _<u>SITPRO</u>_

SITPRO is the facilitation body of the U.K. SITPRO started a project in 2002 on measuring and comparing the regulatory cost of international trade on a consistent basis. This initiative called International Trade Regulatory Costs Survey (ITReCS, and recently changed to Interact) develops benchmarks (indicative costs for specific activities) by gathering regulatory costs data of international trade from individual companies in different sectors. Its methodology can allow companies to calculate their own costs of compliance and then compare them with benchmarks covering the core costs that impact the majority of cross-border movements of goods. Costs include direct costs of third parties, such as a forwarder’s or broker’s account for entry/clearance services, and costs incurred by companies in employing personnel and facilities to achieve customs/trade law compliance and planning. While the pilot surveys are of US companies, SITPRO intends to expand the surveys to the EU, Brazil, India, China, Japan, South Korea, and Eastern Europe.

# _<u>PERINOM Database</u>_

Perinorm is a bibliographic database, viable on annual subscription, dedicated to standards and technical regulations enquiries. It is the world's leading bibliographic database of national, European and international standards from, <u>21 countries, a total of more than 1,000,000 records.</u> Available in three languages both on CD and online (English, French & German), Perinorm comes with a monthly update. The quality of information is guaranteed by the monthly direct delivery realized by each institute.

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15 ____________________________________________________________________________________________________________
sufficient loans from banks or other credit institutions; (c) did not apply for loans because of no collateral, complex paperwork, outstanding loans, or likelihood of being rejected.

There are significant differences in the victimization ratio between those two types of urban households. Compared to non-credit-constrained urban households, the probability of becoming a fraud victim is 2.6 percentage points higher (significant at the 1 percent level) for the credit-constrained urban households, corresponding to a 49 percent ([0.0790.053]/0.053) increase of the average victimization ratio for the non-credit-constrained urban households.

# **4. Methods**

# **4.1 Baseline model**

We employ the following model to estimate the relationship between credit constraints and fraud victimization: F F v − (1) Here F F v indicates the fraud victimization status of household ii ii ii cc ii _i_ . Two outcome FF v vvmm = αα+ β FFββFFv vvc ss + γγXX + θθ + FF variables are used, ii v and v − . v is a binary variable which FF v vvmm equals to 1 if households experienced economic losses after being approached by fraudsters, ii ii ii VVv mm llccVVv mm llccs VVv mm and 0 otherwise. v − refers to the total amount of economic losses (in logarithm) due to being subject to fraud schemes. As defined earlier, ii − is the llccVVv mm llccs key explanatory variable, indicating whether household is credit constrained. ii ββFFββFFv vvc ss is a vector of control variables, including household income, the household head’s vv age and the age squared, gender, years of schooling, health status, and the living ii XX arrangement. Household income (in logarithm, _Lincome_ ) is also included and consists of labor income, income generated by operating enterprise/agriculture, investment income, transfer income from parents or relatives, and other income. As it is established in the literature that elder people are more likely to become victims of fraud (Alves and Wilson, 2008), we include the age (and its squared term) of the household head to control for the possible (nonlinear) relationship between age and fraud victimization. As a bad health condition may make people more susceptible to health-related fraud schemes (such as fake drug fraud, magic medicine fraud, and witch fraud), we include a binary variable to indicate 13
<!-- Start of picture text --> 0.60 0.60 :<br>£0.55 \ ~— Goss <—<br>E80.50 ss 7 €J os9 ae<br>c &<br>0.45 oS 0.45<br>0.40 0.40<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Hi Southern Cone Extended Ml Argentin- urb a n Hi Paraguay Hi Andean region Hi colombia BM Peru<br>Brazil Chile Uruguay - urban Bolivia Ecuador<br><!-- End of picture text --> <!-- Start of picture text --> 0.60<br>20.55 ———___\<br>iG<br>0.45<br>0.40<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Hi Mexico and Central Ame.. Ml Costa Rica li Guatemala<br>I Mexico Dominican Republic Nicaragua<br>Honduras EI Salvador Panama<br><!-- End of picture text -->
household substitutes for that of other household members who have migrated.

# **5. Conclusions and Discussion**

The movement of rural laborers out of agriculture into urban and coastal areas has been an important feature of China’s economic transition. While the opportunity to migrate has raised living standards in many areas of rural China, access to migrant employment appears to create a disincentive for rural youth to enroll in high school. This finding may be important for two reasons: first, rural children may not recognize the real return to education, and second, there may be negative externalities associated with growing inequality between Chinese residents of urban and rural origins.

Much of the information received by rural children and their families regarding the returns to education flows through networks of migrants with middle school education. While the real private return to high school may be higher as it is an input into post-secondary education (Appleton Hoddinott and Knight 1996), this return may not be observed or may be viewed as unattainable by potential migrants who do not expect to enter university. College tuition rose nearly 600 percent between 1996 and 2001 (Du and Giles 2006) and student loans and scholarships were only available at a few national key universities, thus placing college attendance outside the reach of most rural families. From the census, for example, it is evident that school enrollment of urban men at age 20 rose from 33 percent in 1990 to 58 percent by 2005, while enrollment of men with rural registration increased from roughly 5 to 10 percent over the same period (Appendix Figure A.6). While the increase in rural areas is large in percentage terms, it suggests that the prospects of enrolling in college and realizing the returns to investments in high school are remote. Expanding access to student loans and scholarship support for students outside of key universities may ease credit constraints, and providing rural families information about both the feasibility of enrolling in university and the returns to postsecondary careers, may encourage more rural children to continue on to high school.

Apart from the possibility that rural residents do not recognize the full return to high school, China’s policy makers remain concerned about possible negative externalities created by increasing inequality, which is reflected within urban areas increasing gaps in the living standards between residents of urban and rural origins (World Bank and Development Research Center 2012). Policy-oriented researchers have also recently expressed fears that rural born 28
|**_Outcome variable_**|||Pov|erty|||
|---|---|---|---|---|---|---|
|**Drought Impact**|0.197***|0.251***|0.137**|0.201***|0.224***|0.424***|
|**S.E.**|(0.066)|(0.082)|(0.038)|(0.051)|(0.059)|(0.074)|
|**_Outcome variable_**|||ln(Core Co|nsumption|||
|**Drought Impact**|‐0.129***|‐0.149**|‐0.051|‐0.071|‐0.128***|‐0.195***|
|**S.E.**|(0.048)|(0.075)|(0.038)|(0.051)|(0.043)|(0.057)|
|**_Controls_**|No|Yes|No|Yes|No|Yes|
|**_Observations_**|1,511|1,456|1,087|1,035|1,319|1,277|
|**_R‐squared_**|0.054|0.508|0.029|0.507|0.065|0.561|

Source: Authors’ calculations.

Note: ***p<0.01, **p<0.05, *p<0.1. Poverty status results estimated using Probit, Consumption results estimated using OLS. Drought effect expressed in standard deviations of NDVI loss.

# 6. Discussion and conclusions

The results show that the 2016/17 drought severely affected consumption in rural households. The magnitude of the drought impact is generally in line with findings in the literature, but on the upper end of the reported effects. For example, Hill and Porter (2016) find that a moderate drought shock leads to a 9 percent reduction in consumption in Uganda, while this analysis finds an effect almost double that size. However, given the severity of the 2016/17 drought in Somalia, the results appear consistent.

Droughts are cyclical events in Somalia and the Horn of Africa region in general. Recently, severe droughts affected Somalia in 2011 and 1991. A renewed drought shock is therefore likely very to occur at some point in the future. The detailed results from the regression analysis allow to simulate how a renewed income shock of the same magnitude as the 2016/17 drought would affect rural households. To model another income shock, the quantile regression estimates of the drought’s effect on household consumption at different points along its distribution are applied to the SHFS data. Based on this simulation, a renewed income shock could increase rural poverty by nine percentage points, from 65 to 76 percent (Figure 6).

9
# **Figure A.7 Evidence on the Age Structure and Working-Age Adults in RCRE Villages**

# A. Migrant Share of Five-Year Age Cohorts

<!-- Start of picture text --> 0.400<br>0.350 Age<br>Cohorts<br>0.300 16-20<br>21-25<br>0.250<br>26-30<br>0.200 31-35<br>36-40<br>0.150<br>41-45<br>46-50<br>0.100<br>51-55<br>0.050 56-60<br>0.000<br>1995 1997 1999 2001 2003<br>Year<br>Share of Cohort<br><!-- End of picture text -->

- B. Five-Year Age Group as a Share of the Registered Village Population

<!-- Start of picture text --> 0.17<br>0.15 Age<br>Cohort<br>0.13 16-20<br>21-25<br>0.11<br>26-30<br>31-35<br>0.09<br>36-40<br>41-45<br>0.07<br>51-55<br>0.05 56-60<br>0.03<br>1995 1997 1999 2001 2003<br>Year<br>Share of Population<br><!-- End of picture text --> 63
<!-- Start of picture text --> Model name Return period<br>bece-csm1-1 CanESM2 CMCC-CM CNRM-CMS CSIRO-Mk3-6-0 GFDL-ESM2G inmcm4 MIROCS MPI-ESM-LR MRI-CGCM3 NorESM1-M 20<br>500 Miso<br>440 452 i 100<br>404<br>400 388<br>359<br>321<br>300 285 283<br>3a232 a9244 2418<br>$S200 192 197 198<br>Fs 170 163 172<br>Fyo 143 114 139 127 113 139<br>32 100 80 34<br>; _/- _ [1<br>-100 1-97 ; 7<br>-126<br>ee)<br>ro)<br>250M 2 S in<br>3 += aR a ©<br>200M © 3% o oin2 xo ¢8<br>moO <= 8 oon Qa a<br>nf a © S & a<br>2 oA a a 8 o 8<br>150M 5 Sf ee sk TM 2 m a 2<br>n oR uo = 3 & x 4 8 5 2<br>co)S5 don3> ad4 aoRP ao woNu%“ » aBGS$ anwmiS nv8 © n SownSs ONoaHRw a!)o=|iS a5+ ¢ ~xs=} P=)¢&@2 828 goOa<br>Ss 100M i)oS To) oO ° a1 mM ao 8 fo) a) a; ad ”9 Nu @22 SG8 ¢an)oi m<br>rea ntay o”» 8&8tt Fw© kwi o s FgoS aRRnN wD nNAW aa Nnnw nN© 0~ pelwn ©a 8L woow<br>3 6 oc o N 2 dl 3 6 4 ¢ S$ ©<br>% S$ 42 YS ¢ ag 3 w * ag Vv<br><!-- End of picture text -->

<!-- Start of picture text --> om = s | =<br>|Da ©<br>-50M NnBQvuoRg©oiq<br>-100M a0i) “<br><!-- End of picture text -->
<!-- Start of picture text --> /<br>ae<br>SN<br>2Pe<br>- 4<br>od yf<br>= a<br>ZC Pe<br>3 a “<br>Fd —<br><!-- End of picture text --> <!-- Start of picture text --> o alain<br>La geen<br>££ ee<br>7 _e---<br>4 a<br>~<<br>0 20 40 60 80 100<br>per capita consumption percentile<br>——— General equilibrium (baseline) --—=—- Partial equilibrium<br><!-- End of picture text -->
_30_ **_PoPth fthal Crb_** added in finished goods are what we typically observe.) For simplicity, assume that the price indexes available are period-average indexes. A weighted price index should therefore be t = ( **~~P112 +** Pll + Pi + P, ) / 4 = ( pf2 + Pj + 2p, /4 Figure A2.1 Grossed-up Inventories in Months of Turnover 1989-90 <!-- Start of picture text --> 4.5<br>.4-,<br>2<br>1999.1 1989.4 1999.7 1989.10 1990.1 1990.4 1980.7 1990.10<br>,..Method 1 *Method 2<br><!-- End of picture text -->

# Period t is weighted more heavily because of the value added in that period.

To implemen **t** his, I weight the price level in the last 1/3 of the time in inventory double the weight of the price level in the first 2/3 of the time in inventory. The price index used is the industrial sales price index, an end-month index. To get the average price level for the first 2/3 and the last 1/3 of time in inventory I assume a straight-line increase in the price level. The inventory price indexes are calculated as { (average price level in the first 2/3 of time in inventory) + 2*(average price level in the last 1/3 of time in inventory **)** + 3.

Three separate indexes are calculated, corresponding to inventory lives of one month, two months, and three months. These price indexes are applied to the inventory series to get a time series for real inventories in constant prices. Real inventories are used to calculate monthly real inventory accumulation in constant prices; this is converted into current prices using the industrial price index to get monthly real inventory accumulation m the prices of that month. Monthly inventory accumulation is then summed to get annual inventory accumulation. The resulting estimate of "real annual inventory accumulation in current year prices" is litfle changed if instead we calculated real annual inventory accumulation and converted this into current year prices using an annual average price deflator.
# **Table 8.  Regression Analysis of Leverage**

||(1)|||(2)|||(3)|||(4)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Small|-0.187|_-12.4_|***|-0.188|_-9.86_|_***_|-0.217|_-11.49_|_***_|-0.154|_-5.62_|***|
|Medium|-0.076|_-6.27_|***|-0.082|_-5.6_|_***_|-0.080|_-6.67_|_***_|-0.075|_-3.73_|***|
|Sales growth||||0.000|_-0.17_||||||||
|Operating income/ TA|||||||-0.694|_-2.84_|_***_||||
|Dummy 1996|0.004|_0.19_|||||-0.002|_-0.09_|||||
|Dummy 1997|0.006|_0.29_||-0.001|_-0.03_||0.000|_0.01_|||||
|Dummy 1998|-0.026|_-1.2_||-0.012|_-0.47_||-0.033|_-1.66_|_*_||||
|Dummy 1999|-0.057|_-3.08_|***|-0.047|_-2.18_|_**_|-0.071|_-3.96_|_***_||||
|Dummy 2000|-0.067|_-3.56_|***|-0.062|_-2.88_|_***_|-0.085|_-4.47_|_***_||||
|Trend||||||||||-0.011|_-2.8_|***|
|Small * Trend||||||||||-0.014|_-1.65_|*|
|Medium * Trend||||||||||-0.001|_-0.09_||
|Mining|0.000|_0_||-0.028|_-0.42_||0.006|_0.1_||0.005|_0.08_||
|Construction|0.174|_7.1_|***|0.177|_6.28_|_***_|0.148|_5.92_|_***_|0.174|_7.16_|***|
|Manufacturing:|||||||||||||
|Food|0.026|_0.98_||0.026|_0.82_||0.022|_0.89_||0.025|_0.96_||
|Textile|0.198|_2.66_|***|0.195|_2.1_|_**_|0.122|_2.18_|_**_|0.201|_2.69_|***|
|Apparel|0.085|_2.3_||0.077|_1.69_|_*_|0.071|_1.86_|_*_|0.087|_2.37_|**|
|Chemicals|-0.050|_-1.92_||-0.054|_-1.78_|_*_|-0.026|_-1_||-0.049|_-1.91_|**|
|Rubber and Plastic|0.032|_1.07_||0.029|_0.81_||0.030|_1.06_||0.032|_1.08_||
|Stone, Clay, Glass|-0.001|_-0.02_||0.000|_0_||-0.009|_-0.35_||0.001|_0.03_||
|<br>Electrical|0.010|_0.35_||-0.014|_-0.44_||0.041|_1.52_||0.009|_0.34_||
|Other Manufacturing|-0.019|_-0.74_||-0.035|_-1.14_||-0.020|_-0.87_||-0.019|_-0.76_||
|Utilities|-0.027|_-0.74_||-0.054|_-1.31_||-0.034|_-0.96_||-0.029|_-0.78_||
|Trade|0.091|_2.81_|***|0.103|_2.5_|_***_|0.053|_1.54_||0.092|_2.86_|***|
|Finance|-0.200|_-5.62_|***|-0.172|_-3.74_|_***_|-0.219|_-6.04_|_***_|-0.198|_-5.61_|***|
|Real Estate|0.111|_4.24_|***|0.105|_3.23_|_***_|0.093|_3.54_|_***_|0.112|_4.32_|***|
|Services|-0.021|_-0.82_||-0.026|_-0.87_||-0.037|_-1.49_||-0.022|_-0.9_||
|Constant|0.601|_23.33_|***|0.600|_19.2_|_***_|0.682|_18.3_|_***_|0.605|_25.17_|***|
|No. Obs/RSQ|2438|0.140||1705|0.133||2438|0.200||2438|0.140||

Notes: Source CMA (own calculations). Dependent variable is total liabilities over total assets. Small and medium are dummies variable for firms in the smallest and medium thirds. Trend is a time trend equal to zero and 1995 and increasing by one for each subsequent year. The asterisks report t-tests for coefficient significance: ***,** and * indicate significance at 1 percent, 5 percent, and 10 percent, respectively.
# **Table 9-1 The ratio of the amount allocated in the survey to the amount recorded in the budget ranges from 65% (government transfers) to 108% (for pension contributions)**

Macro-validation of the fiscal incidence analysis in the West Bank and Gaza, 2017

|**M**<br>**Scenario : Pensio**|**ACROVALIDATI**<br>**ns as Governme**<br>|**ON**<br>**nt Transfers(**<br>|**PGT)**|**Ratio**|**w.r.t.**|
|---|---|---|---|---|---|
||**Value in NI**|**S Millions**|**Ratio**|||
|||||**consu**|**mption**|
|**Fiscal instrument**|||**PECS:**|||
||**PECS**<br>**2016/17**|**Admin**<br>**2017**|**Admin**|**PECS**<br>**2016/17**|**Admin**<br>**2017**|
|**Consumption**|44 742.3|55 087.2|81.2|100.0|100.0|
|**Direct Taxes and contributions**|1 146.7|1 123.9|102.0|2.6|2.0|
|Personal Income Tax (PSE)|220.3|260.0|84.7|0.5|0.5|
|PropertyTax|12.0|14.3|84.2|0.0|0.0|
|Pension contributions|914.4|879.2|104.0|2.0|1.6|
|**Direct Transfers and contributory pensions**|2 285.7|2 803.7|81.5|5.1|5.1|
|Income from contributory pensions|1 173.1|1 093.2|107.3|2.6|2.0|
|Govt assistance (cash and in-kind)|1 112.6|1 710.5|65.0|2.5|3.1|
|Cash Transfer Program(CTP)|982.9|||||
|In-kind transfers|129.7|||||
|**Indirect Taxes**|8 505.7|10 979.5|77.5|19.0|19.9|
|VAT|3 468.2|3 646.1|95.1|7.8|6.6|
|Direct effects|1 959.9|||||
|Indirect effects|1 669.6|||||
|Customs Duties|2 926.3|4 429.9|66.1|6.5|8.0|
|Fuel excise|2 111.3|2 903.5|72.7|4.7|5.3|
|Direct effects|598.4|||||
|Indirect effects|1 512.8|||||
|**Indirect Subsidies**|333.3|*, **|n.a.|0.7|n.a.|
|Fuel|110.4|*|n.a.|0.2|n.a.|
|Direct effects|21.3|||||
|Indirect effects|89.1|||||
|Electricity (direct effects)|223.0|**|n.a.|0.5|n.a.|
|**In-Kind Benefits**|3 941.9|4 496.3|87.7|8.8|8.2|
|**Health**|**1 723.7**|**2 032.2**|**84.8**|**3.9**|**3.7**|
|Primary|424.1|500.0|84.8|0.9|0.9|
|Hospital|1 299.6|1 532.2|84.8|2.9|2.8|
|**Education**|**2 218.2**|**2 464.1**|**90.0**|**5.0**|**4.5**|
|Basic|1 362.2|1 377.8|98.9|3.0|2.5|

56