rafmacalaba/datause-encoder-data huggingface dataset:
Table 3.

Some Performance Indicators for Balanced Panel by Sectors, 1996-1997

|Sectors||Revenue<br>Million Litas|Value Added<br>Million Litas|Employment|Revenue<br>per Worker|Value<br>Added per<br>worker|
|---|---|---|---|---|---|---|
||1996|12,414|<br>3,306|<br>214,683|<br>57,826|<br>15,400|
|Manufacturing|1997|15,050|<br>3,719.5|<br>200,962|<br>74,892|<br>18,509|
||_Real % change_|<br>_17.0_|<br>_8.3_|<br>_-6.4_|<br>_25.3_|<br>_16.0_|
||1996|4,461|765|<br>43,213|<br>103,233|<br>17,696|
|Utilities|1997|3,994|897|<br>32,396|<br>123,276|<br>27,690|
||_Real % change_|<br>_-16.5_|<br>_11.3_|<br>_-25.0_|<br>_13.4_|<br>_50.5_|
||1996|2,390|888|<br>63,804|<br>37,465|<br>13,911|
|Construction|1997|3,072|1,165|<br>61,895|<br>49,627|<br>18,824|
||_Real % change_|<br>_18.7_|<br>_21.5_|<br>_-3.0_|<br>_22.7_|<br>_25.5_|
||1996|14,855|<br>2,718|<br>170,571|<br>87,088|<br>15,933|
|Services|1997|17,844|<br>3,420|<br>165,811|<br>107,619|<br>20,628|
||_Real% change_|<br>_11.6_|<br>_17.4_|<br>_-2.8_|<br>_15.1_|<br>_21.0_|
||1996|970|<br>403|<br>29,491|<br>32,900|<br>13,653|
|Others|1997|1,169|<br>4782|<br>27,941|<br>41,842|<br>17,103|
||_Real % change_|<br>_12.0_|<br>_10.2_|<br>_-5.3_|<br>_18.7_|<br>_16.8_|

Note: There are 1,345 enterprises in Manufacturing, 124 in Utilities, 707 in Construction, 2,465 in Services, and 498 in Others. PPI based inflation of 4.2, 6.0, 9.8, 8.5 and 8.5 percent were used to establish the rate of real growth in Manufacturing, Utilities, Construction, Services and Others respectively.

34
_Estimates and Projections of Mortality by Cause_ _76_ of Medicine (1986:159-162) for case-fatality rates. The recommendations<sup>were age- but</sup> niot sex-specific, with age groups 0 and 1-4 combined. Older ages<sup>were separated into</sup> 15-59 and 59 and over, but the rates were identical.

The ratios of the rates from the expert assessment to the regression<sup>estimates,</sup> by age group and region, were as follows:

|||Sub-<br>Saharan<br>|||
|---|---|---|---|---|
||LAC|Africa<br>|MENA<br>|Asia<br>|
|0-4|1.65|1.48<br>|1.03<br>|1.47<br>|
|5-14|4.05<br>|0.33<br>|0.43<br>|1.15<br>|
|15-44|0.95<br>|0.07<br>|0.26<br>|0.58<br>|
|45-64|0.40|0.05<br>|0.16<br>|0.31<br>|
|65+|0.10|0.04|0.06|0.14|

The inconsistency between the expert assessment for age 65<sup>and over and reported</sup> figures for Latin America has already been noted. Either<sup>the reported deaths are grossly</sup> exc;issive or the expert assessment for this age group and region<sup>is wrong. The 5-14 age</sup> group for Latin America also presents a problem: the rate for<sup>reporting countries is 6.9</sup> per 100,000, and the expert assessment of 50 could be reached<sup>(assuming the reports</sup> are accurate) only if nonreporting countries have a rate 14<sup>times that of reporting</sup> countries. In general, the pattern of expert assessments across<sup>age groups is so different</sup> from that of the regres'k'n rates that no attempt was<sup>made to reconcile them.</sup> Using the rates from the expert assessment, we calcula*td deaths<sup>within each</sup> age group and allocated these between the sexes (and, in the<sup>cc.-. of the youngest age</sup> gr'ups, between 0 and 1-4) in proportion to numbers of<sup>deai' s in the regression</sup> e.timates. For all ages combined, the regression estimates<sup>and expert assessment</sup> actually produce similar results: the developing-country<sup>mortality rate is reduced</sup> marginally following the recommendations from 62 to 60 per<sup>100,000, and developing-</sup> country deaths reduced from 3.0 to 2.9 million. However, deaths<sup>among those 0-4 are</sup> increased 40 percent and among those 5 and older decreased<sup>63 percent.</sup>

# _Polio_

Detailed altemative numbers of deaths were recommended based<sup>on lameness</sup> prevalence surveys reported in several publications, including<sup>a summary article by</sup> Bernier (1984). The surveys suggest that new cases worldwide<sup>are half a million, and</sup> a case-fatality rate of 15 percent (the literature suggests rates between<sup>10 and 20 percent)</sup> was applied to obtain the expert assessment.

For industrial market and nonmarket economies and for Latin<sup>America and the</sup> Caribbean, the recommended figures were uniformly zero. However,<sup>some deaths</sup> actually were reported to WHO (e.g., 9 deaths in Sweden<sup>in 1985, and 34 in the United</sup> Kingdom), so that the expert assessment was modified to incorporate<sup>the very low</sup> regression estimates in these cases.
<!-- Start of picture text --> 2030 2050 2080<br>60 60 60 = East Asia & Pac. .<br>Europe & Cent. Asia<br>= Latin America & Carib.<br><!-- End of picture text --> <!-- Start of picture text --> Mid, East & North Afr.<br>40 40 40 =. woeSouth Asia Africa;<br>&&<br><!-- End of picture text --> <!-- Start of picture text --> 55<br>320 & 20 . 8 20 °<br>aa<br>®2 . : .<br><!-- End of picture text --> <!-- Start of picture text --> = i : 4 : zt<br>0 0 0 hd<br><!-- End of picture text --> <!-- Start of picture text -->
-20 -20 -20<br>RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5<br><!-- End of picture text -->
- Shorrocks, A.F. (1980) ‘The class of additively decomposable inequality measures.’ _Econometrica_ 48(3), 613–626

- van Doorslaer, E., A. Wagstaff, H. Bleichrodt, S. Calonge, U. Gerdtham, M. Gerfin, J. Geurts, L. Gross, U. H ̈akkinen, R.E. Leu, O. O’Donnell, C. Propper, F. Puffer, M Rodr ́ıguez, G. Sundberg, and O. Winkelhake (1997) ‘Income-related inequalities in health: some international comparisons.’ _Journal of Health Economics_ 16(1), 93–112

- Victora, C. (1992) ‘The association between wasting and stunting: An international perspective.’ _Journal of Nutrition_ 122(5), 1105–1110

- Wagstaff, A., and E. van Doorslaer (2000) ‘Income inequality and health: What does the literature tell us?’ _Annual Review of Public Health_ 21, 543–567

- (2004) ‘Overall versus socioeconomic health inequality: a measurement framework and two empirical illustrations.’ _Health Economics_ 13, 297–301

- Wagstaff, A., E. van Doorslaer, and N. Watanabe (2003) ‘On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam.’ _Journal of Econometrics_ 112, 207–223

- Waterlow, J.C., R. Buzina, W. Keller, J.M. Lane, M.Z. Nichaman, and J.M. Tanner (1977) ‘The presentation and use of height and weight data for comparing the nutritional status of groups of children under the age of 10 years.’ _Bulletin of the World Health Organization_ 55(4), 489–498

- WFP (2001) _Identifying Poor Areas in Cambodia: Combining Census and Socio-Economic Survey Data to Trace the Spatial Dimensions of Poverty_ (Phnom Penh: World Food Programme)

- WHO Working Group (1986) ‘Use and interpretation of anthropometric indicators of nutritional status.’ _Bulletin of the World Health Organization_ 64(6), 929–941

- (1995) ‘An evaluation of infant growth: the use and interpretation of anthropometry in infants.’ _Bulletin of the World Health Organization_ 73(2), 165–174

- Wilkinson, R.G. (1996) _Unhealthy Societies: The Afflictions of Inequality_ (Routledge)

- World Health Organization (2002) _World Health Report 2002: Reducing risks and promoting healthy life_ (Geneva: World Health Organization)

- Yitzhaki, S., and J. Slemrod (1991) ‘Welfare dominance: An application to commodity taxation.’ _American Economic Review_ 81(3), 480–496

- Zeini, L.O., and J.B. Casterline (2002) ‘Clustering of malnutrition among Egyptian children.’ mimeo, Cairo University and Population Council

42
<!-- Start of picture text --> Val<br>13000<br>12000<br>11000<br>10000<br>4000<br>8000<br>7O00<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>-1000<br>-2000<br>-3000<br>13 14 «15 16 #17 «618 «6190620 lO) 22 23 24 25 «760 620 628 UPD lO UCI O82<br>MEAN TEMPERATURE<br>PLOT ~——— VAL_TMEAN_SML ———~ VAL_TMEAN_BIG<br><!-- End of picture text -->
Donald F statistics. As suggested in the literature (Steiger and Stock, 1997), as a rule of thumb we use a value of 10 of the F statistic to conclude with some confidence that the chosen instruments are indeed valid.<sup>7</sup>

# **_b) Assessing the impact of migration_**

In Tables 2-4, we report the estimated coefficients and the relevant diagnostic statistics for all outcomes of choice. Starting in Table 2 with the impact of migration on agricultural labor, all models consistently support the view that migration of a household member has a negative effect on the household labor effort in agriculture, both in total and per capita terms. This is also true across gender lines, although we find a more sizable drop for male labor. Although the larger drop in total male labor, _vis a vis_ female labor, could be expected in view of a still predominantly male migration, the larger drop in per capita terms is puzzling. One possible reason is that male household members, more than women, take advantage of the improved income and relaxed credit constraint to get involved in non-agricultural types of activities, for instance because returns to rural off-farm labor are greater for men. An alternative explanation is that the income from migration is used to substitute agricultural work for leisure, with men taking most advantage of the opportunities granted by higher income levels.

Age of the head of the households is positively associated with labor effort in agriculture, at a decreasing rate: older household members are more likely to work longer hours in agriculture, regardless of gender. Another interesting, although not surprising, result relates to the education variable: more educated households tend to work less in agriculture. Presumably, more educated individuals are more likely to find better off-farm opportunities. Also, not surprisingly, individuals in large households tend to work less hours, again with a higher impact on male labor.

As expected, households with more land and livestock resources, allocate more time to agriculture, both in total and per capita terms. Similar patterns are observed across gender lines, with the exception of the livestock variable, for which a stronger positive relation seems to exist between livestock ownership and male labor _vis a vis_ female work. As per hypothesis, the crime level in the community appears to deter work in agriculture. Finally,

> 7 As we end up using the same IV specification for all models, in Table 5 we only report one full first stage regression, where the first three variables are the excluded regressors (Table 6).

15
<!-- Start of picture text -->
-<br>Figure 4b Cross Sectoral Flows (I F) (Trends and Cycle): Mexico an Brazil<br>Mexico  Brazil<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>I-F F-I I-F F-I<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>I-F (HP) F-I (HP) I-F (HP) F-I (HP)<br>.5<br>.25<br>.4 .2<br>.3 .15<br>.1<br>.2<br>.05<br>.1<br>.1<br>.05<br>.05<br>0<br>-.05 0<br>-.05<br>-.1<br>-.1<br>-.15<br><!-- End of picture text -->

Notes: The graph shows the transition rates between informal self-employed (S=Informal Self-Employed) and the informal salaried (I=Informal Salaried). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

36
App. Table 1c: Estimations of location decisions of new plants by industry labor intensity

||Log ne<br>by in|w establishme<br>dustry labor i|nt counts<br>ntensity|Log ne<br>by in|w employme<br>dustry labor i|nt levels<br>ntensity|Log<br>by in|new output<br>dustry labor|levels<br>intensity|
|---|---|---|---|---|---|---|---|---|---|
||0-25th|25th-75th|75th|0-25th|25th-75th|75th|0-25th|25th-75th|75th|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||A. Base spa|tial horizon|measuring eff|ects relative|to districts 5|0 km from th|e GQ netwo|rk|
|Post GQ upgrades *|0.782|0.375|0.152|0.814|0.289|0.219|0.394|0.015|0.404|
|Nodal district|(0.499)|(0.347)|(0.626)|(0.503)|(0.270)|(0.538)|(0.535)|(0.369)|(0.651)|
|Post GQ upgrades *|0.401|0.077|-0.384|0.411|0.073|-0.418|0.134|0.085|-0.559|
|District 0-10 km from GQ|(0.372)|(0.225)|(0.386)|(0.379)|(0.225)|(0.370)|(0.413)|(0.263)|(0.483)|
|Post GQ upgrades *|-0.197|-0.232|-0.532|-0.246|-0.169|-0.549|-0.416|0.088|-0.583|
|District 10-50 km from GQ|(0.538)|(0.334)|(0.591)|(0.543)|(0.324)|(0.565)|(0.561)|(0.386)|(0.690)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1089|1089|1089|1089|1089|1089|1089|1089|1089|
||B|. Extended s|patial horizon|measuring e|ffects relativ|e to districts|200 km from|the GQ net|work|
|Post GQ upgrades *|1.114|0.628|0.316|1.120|0.536|0.362|0.878|0.283|0.720|
|Nodal district|(0.545)|(0.372)|(0.649)|(0.547)|(0.300)|(0.563)|(0.582)|(0.400)|(0.692)|
|Post GQ upgrades *|0.733|0.330|-0.220|0.717|0.321|-0.274|0.618|0.354|-0.242|
|District 0-10 km from GQ|(0.428)|(0.263)|(0.423)|(0.434)|(0.262)|(0.406)|(0.469)|(0.307)|(0.537)|
|Post GQ upgrades *|0.133|0.020|-0.369|0.058|0.077|-0.406|0.065|0.355|-0.269|
|District 10-50 km from GQ|(0.580)|(0.358)|(0.616)|(0.584)|(0.349)|(0.590)|(0.606)|(0.416)|(0.730)|
|Post GQ upgrades *|0.590|0.408|0.167|0.430|0.341|0.108|0.504|0.198|0.337|
|District 50-125 km from GQ|(0.440)|(0.290)|(0.463)|(0.425)|(0.297)|(0.456)|(0.445)|(0.378)|(0.570)|
|Post GQ upgrades *|0.681|0.564|0.466|0.749|0.610|0.450|1.367|0.844|0.887|
|District 125-200 km from GQ|(0.446)|(0.305)|(0.491)|(0.436)|(0.306)|(0.485)|(0.490)|(0.377)|(0.618)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1089|1089|1089|1089|1089|1089|1089|1089|1089|

Notes: See App. Table 1a. Industries are divided into groups based upon labor intensity in 2000 at the national level. These three bins include those with low labor intensity (the bottom quartile of intensity), medium intensity (the middle two quartiles), and high intensity (the top quartile).

36
**40** -

-

speoLflcatlons for the fuel **share quatlons** to plck up **some** of the partlal adjustment.

Some general conclusions on the suitablilty of econometric models for measuring the degree of fuel substitutability can be drawn. EconometrLi models **are** designed to use published aggregate data to glve an estimate of the degree of substitutLon betwen fueli that has taken placo Ln response to changes Ln relatlve prlces, wlthout detAiled field work or a case by case approach they provLde a **masure** of such substltutablilty. Thlo is best seen as a fLrat approxLwaa<sup>4</sup> -on fe-- the purposes of pollcy - an LadLcatLon of where substitution has taken placo and hence of the potentLal for further change. The extrapolatLon of results from one study to other **cases appears** at thli stage of knowledge to be limited to suggestLng whlih LndustrLes or clrcumstances are worth more detailed LnvestlgatLon. **As** the study by Mountain and HsaLo indicates there is not yet strong agre ment from dLfferent researchers on prlce elasticLties even when they are all analysLng the sam data (e.g. speciflc industry values ln Canada). Different methodologies yield very dLfferent elastLcitLes and would result ln very dlfferent strengths of pollcy rccommended to achLeve the same degree of fuel switching. Comparing **results** between countrles is even le satLsfactory. **Here** there is substantlal dissatisfaction over model choice, with some models on occasLon producLng counter-intuLtlve results (the wrong sign for own prLce elasticitLes etc.). The extrapolatlon of estimates from such models to countrles wlth completely dlfferent lndustrLal structures and technologLes is not likely to be **rellable.** The **0naiaetiLM Anoroach**

**Thes** nglneerlng approach to fuel substitution has generally been at a plant speciflc level, although **some** recent **studles here** attempted to draw more general **lessons** on **the** preferred technology **(see** Moore and Crouasllat 119911 and Pinto and Besant-Jonew 119891).

The key difference ln englneerlng studles **ls** that a speclflc sltuatlon li analyzed and this Lnvolves **an analysis** of **all** the costs of alternatlve proposals. Such costs Lvolve capltal and lnfrastructure costs as well as fuel
# **Figure 2 Educational Attainment by Age Cohort for China’s Urban Population** Evidence from the 2000 Census

<!-- Start of picture text -->
1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>1940 1945 1950 1955 1960 1965 1970 1975<br>Birth Year<br>At Least Primary School At Least Middle School<br>At Least High School College of More<br>Cohort Share<br><!-- End of picture text -->

28
<!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1st 2nd 3rd 4th 5th<br>Central Eastern Northern Western<br><!-- End of picture text --> Source: Staff calculations using UNPS 2005/06‐2013/14

# 3. Returns to input use

In this section we examine the profitability from using improved inputs, taking into account the variability of the output prices and the quality of available inputs to farmers. We focus on the use of inorganic fertilizer and improved seeds for several of the main crops in Uganda described in Table 2.2: maize, beans, coffee and matooke.<sup>6</sup> We first estimate the inputs’ profitability under optimal conditions using the best available information: agronomic evidence on the yield return to inputs from experimental fields, as well as output price data from local markets. We also briefly touch on the effects of sub‐optimal weather conditions on these financial returns. We then study the potential returns under optimal conditions. Together with yield responsiveness and the historical distribution of agricultural output prices, we estimate whether the expected return to using modern inputs is, on average, positive for farmers in Uganda. Lastly, we consider the issue of the quality of inputs (for maize) and adjust returns closer to what an average farmer might actually obtain. More than 90 percent of households engaged in agriculture report buying their inputs from markets, shops and local vendors, according to the 2015 National Service Delivery Survey,<sup>7</sup> whose inputs rarely retain the same quality as those used in agronomic field trials.

## _3.1. Yield gains from the use of authentic inputs_

It is often noted that experimental yields from controlled agronomic studies are high but rarely observed on an average farmer’s plot. This can be explained by differences in soil and environmental conditions across farmers (Suri 2011, Harou et al. 2017), variation in crop management practices (Esilaba et al. 2005, Ngome et al. 2011, and Sileshi et al. 2010), and variation in access to high quality inputs (Tjernstrom et al. 2017, Fairbarn et al. 2017, Ashour et al. 2017, Bold et al. 2017). In this section we first consider yield returns observed in experimental studies to be able to provide an upper‐bound estimate on the financial

> 6 We did not find experimental field trials for pesticide use in Uganda or similar countries with detailed data on application rates and costs. Similarly, for coffee and matooke there was no evidence on improved seeds. 7 The NSDS is collected by UBOS and contains information on the differences in services available for the Ugandan population, including agricultural services.

9
**Table 2: County to county-level-city upgrade**

Number of county-year observations by upgrading status and number of requirements satisfied (1994-1997)

|Number of requirements satisfied|Total|0|1|2|3|
|---|---|---|---|---|---|
|Non-upgrading cases|6401|4583|1317|465|36|
|Upgradingcases|99|24|30|39|6|

Graduation into city status came with a number of benefits, relating to four main categories: (1) tax and fees, (2) land-related policies, (3) administrative powers, and (4) local government size, rank, salary and reputation - see Table 3 (taken from Fan, Li, & Zhang, 2012).

**Table 3: Benefits of being a city: An incomplete list**

|Category|Benefits|
|---|---|
|Tax and fees|- Cities enjoyed a higher urban construction tax (7%<br>compared to 5% for counties).<br>- Could collect the surcharges levied on the issuing of<br>motorcycle registration.<br>- In Liaoning province, cities could get 1-2 million additional<br>subsidies each year after upgrading.|
|Land-related policies|- Cities generally convert more land to construction use and<br>retain a larger share of revenue from land sale.|
|Administrative powers|- Cities have more authority on foreign trade and exchange<br>management.<br>- Could establish branches of custom and large state-owned<br>banks.<br>- Could approve projects with a higher investment cap.<br>- Gain authority over police recruitment and vehicle<br>administration.<br>- After achieving the status of “line item under province”<br>(Shengji Jihua Dalie), cities could report directly to the<br>provincial administration to ask for investment projects.|
|Government size; Rank and<br>salary; Reputation|- Cities could establish more branches of government and<br>have a larger number of government employees.<br>- Sometimes the bureaucratic rank and salary of officials are<br>raised after upgrading.<br>- Cities generally carry greater prestige and are more<br>attractive to investors from outside.|

In this paper, we investigate if newly-promoted county-level cities in China utilize these new powers to attract new firms and help firms grow. We study macro-level city outcomes and micro3
**Table 7 - Reduction in Medical Costs (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|1.13|0.71|0.50|0.41|0.34|0.34|0.23|0.18|0.14|0.05|
|Medium elasticity|1.60|1.05|0.76|0.65|0.55|0.56|0.40|0.33|0.24|0.10|
|Upper-bound elasticity|2.07|1.39|1.02|0.89|0.76|0.78|0.56|0.47|0.35|0.15|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey 2016. _Note:_ The table reports the share of total consumption for each decile.

**Figure 3 – Income Gains: Medical Costs of Tobacco Taxes (Reduction in Medical Expenditures), by Decile**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **Income gains deriving from an increase in working life years**

We estimate the cost of the working life lost because of tobacco consumption, assuming that the impact of lower tobacco use on health and work-generated income is direct. The 6.0 million years of life lost attributed to tobacco consumption are distributed using the occurrence of mortality profile. For each death, working years lost are divided across deciles proportionately to the number of households that consume tobacco in each income group. The results show that the reduction in tobacco consumption and the expected reduction in work years lost have a positive impact on welfare that represents a transversal income bump among all decile groups (table 8 and figure 4).

**Table 8 – Years of Working Life Lost (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.35|0.36|0.32|0.32|0.31|0.38|0.32|0.31|0.31|0.27|
|Medium elasticity|0.49|0.53|0.49|0.51|0.51|0.63|0.54|0.55|0.55|0.53|
|Upper-bound elasticity|0.64|0.70|0.66|0.70|0.71|0.88|0.76|0.79|0.79|0.79|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total consumption for each decile. Years of life lost have been estimated using all deaths from tobacco-related diseases.

11