rafmacalaba/datause-encoder-data huggingface dataset:
RUB 8 to RUB 66 per cubic meter, with an average of RUB 33 (US$ 1.20), _i.e._ almost twice the average minimum stumpage fee in that region (Pochinkov 2000). Separate data from Irkutsk Oblast (southern Siberia) indicate that export-grade timber fetches RUB 1,000 per cubic meter but costs only RUB 350 to produce. The profit on such timber would equal RUB 650 or 65% of sales price (Shulyakovskaya 2000).

2.66 In this study, the simple net-back methodology was applied to 8 Russian regions (Arkhangelsk, Khabarovsk, Krasnoyarsk, Leningrad, Moscow, Novgorod, Pskov and Vologda) and the Russian Federation as a whole using two data sets.

2.67 The first data set was compiled from original data collected from the 8 regions. For each region, average costs and prices were obtained, covering from a couple of logging firms to all of them. The second data set was taken from NIPIElLesprom's database, which comprises cost information on 20,000 forest enterprises in the whole of Russia for the year 1998. Costs for 1998 were multiplied by the 1999 producer price index in the forestry sector (Goskomstat reports 1.572) to obtain 1999 costs.

2.68 There exist wide discrepancies between the two data sets. In particular, the costs reported directly by the regions come out much higher than the NIPIEILesprom equivalent. The higher costs from the regions are thus taken as the higher bound estimate and the NIPIElLesprom figures as the lower bound estimate.

2.69 Using the two data sets, notional profits, normal profits, notional tax takes, unit appropriated rent, and foregone revenues were calculated using two different techniques - the equity and the costs techniques. Though the data sets are independent, data from both must be combined to calculate some of the coefficients or variables in each technique, which means that the calculations themselves are not totally independent. For example, transport tariffs and handling costs are only available in the regional data set, while the normal profit rate is calculated with NIPEEILesprom data.

# _Equity Technique_

2.70 This technique calculates normal profit as a return on equity.<sup>22</sup> In the regional data set, production costs = production costs at lower landing minus stumpage fees. In the NIPIEILesprom set, production costs are taken as such. Then, in the regional data set, the cost of sales - production costs at lower landing + transport tariffs + handling costs; and, in the NIPIElLesprom set, the cost of sales = production costs + transport tariffs + handling costs. Next a notional gross profit can be estimated based upon the available revenues and costs. Notional profit is a reconstructed, not actual profit: notional gross profit = revenues minus cost of sales. 23 Using the regional data set, the notional tax take, which is the tax

> 22 This method corresponds to the "Current Rent Method I" of the U.S. Bureau of Economic Analysis (BEA 1994 and 2000).

> 23 Data include FOB (free on board) and CIF (cost insurance freight) prices for round wood. CIF prices are used here as the measure of revenues.

29
The full information maximum likelihood of a Vector Error Correction Model is as following:

> <sup>_y_</sup> _t_  Where, is a (n x 1) vector of the n variables in interest,<sup></sup> is a (n x 1) vector of constants,

> <sup></sup> _t_ represents a (n x (k-1)) matrix of short-run coefficients, denotes a (n x 1) vector of white noise residuals, and<sup></sup> is a (n x n) coefficient matrix. If the matrix<sup></sup> has reduced rank (0 < r < n), it can be split into a (n x r) matrix of adjustment coefficients<sup></sup> , and a (n x r) matrix of co-integrating vectors<sup></sup> . Testing for co-integration, using the Johansen’s reduced rank regression approach, centers on estimating the matrix<sup></sup> in an unrestricted form, and then testing whether the restriction implied by the reduced rank of<sup></sup> can be rejected. In particular, the number of the independent co-integrating vectors depends on the rank of<sup></sup> which, in turn, is determined by the number of its characteristic roots that are different from zero. The test for nonzero characteristic roots is conducted using Max and Trace tests statistics. It is important to note that as Johansen (2002) shows, since these tests tend to reject the null hypothesis of no co-integration in small samples; it is useful also to use the degree of freedom adjusted test statistics in the co-integration analysis.

A VECM is then determined following Johansen (1988) and Johansen and Juselius (1990). The last step would be identifying a conditional VECM, including endogenous variables that rejected the null hypothesis of being excluded from the co-integrating relation. For this deliberation, Oxmetrics 6.1 will be used as the key econometric package for related quantitative modeling.

We run a co-integrated VAR then analyze the standardized eigenvectors. These are defined as the matrix containing the parameters of the co-integrating vectors. The rows of the β matrix—as defined in the earlier methodology section—represent the standardized coefficients of the variables entering into the respective co-integrating vector. These coefficients are then normalized to unity along the principal diagonal of this matrix.

23
based on quantities delivered. As a result, if they choose to access international commodity markets, they need to resort to more expensive types of price hedging instruments such as options. As the international coffee market becomes more competitive, more quality conscious, with more price fluctuations and lower prices, the combination of Dominican Republic's production instability and low quality are a major problem for competitiveness in international markets. This problem is also relevant for the cocoa sector.

A significant challenge to the coffee sector in recent years has been recovery from Hurricane Georges. Much of the country’s productive capacity in coffee suffered damage. In the survey undertaken by the CRMG, about 83% of respondents claimed damages from Georges. The magnitude of the effects stated by respondents increased with farm size. The majority of producers faced between 20% and 40% damage to their trees from the Hurricane while less then 20% of producers were affected seriously. Despite low coffee prices and storm damage, 50% of those surveyed chose to replant trees after the hurricane.

# **D. Structure of production**

The majority of coffee producers in the Dominican Republic operate on small land holdings, but more than half the volume of coffee production is by medium and larger scale producers. The share of smallest landholding sizes among total number of coffee producers and total production has declined over the past 20 years.

Very small producers dominate the sector in terms of the number of producers (table III.7). About 50% of producers have less than 1.25 hectares, but they account for only about 12% of coffee area. Approximately 30% all of coffee producers have less than 0.625 ha. Alternatively, coffee producers with more than 6.25 ha account for about 6% of the farms and about 36% of land areas devoted to coffee. About 27% of coffee producers are located below 500m, but this includes a relatively high percentage of small sized producers (table III.8). Medium and larger sized producers tend to be located at higher elevations, where higher-quality coffee is produced.

**Table III .7  Coffee Farm Size and Numbers of Producers**

|LAND SIZE<br>AREA|LAND SIZE<br>by HECTARE<br>(approximate)|<br>NUMBER OF<br>FARMS|% OF<br>FARMS|CULTIVATED AREA<br>Tareas<br> (Hectares)|% OF<br>AREA|
|---|---|---|---|---|---|
|1 –10|0 – 0.62|13,248|30.4|83,705<br>(5,264)|4.5|
|11 –20|0.63 – 1.25|8,130|18.6|137,926<br>(8,675)|7.3|
|21 – 50|1.26 – 3.14|12,747|29.2|467,061<br>(29,375)|24.8|
|51 – 100|3.15 – 6.25|6,777|15.5|510,418<br>(32,102)|27.1|
|101 +|6.26 +|2,711|6.2|684,345<br>(43,041)|36.3%|
|TOTAL||43,613|100%|1,883,455<br>(118,456)|100%|

Source: SEA, 1999 National Registry of Producers. Preliminary statistics.

36
Table 6: Relationship between energy management and energy intensity in value terms.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|Energy|-0.392<sup>_∗∗∗_</sup>|-0.379<sup>_∗∗∗_</sup>|-0.362<sup>_∗∗_</sup>|0.106|-0.284<sup>_∗∗_</sup>|0.235|0.330<sup>_∗∗∗_</sup>|0.107|0.286|
||(0.089)|(0.110)|(0.159)|(0.089)|(0.128)|(0.156)|(0.120)|(0.155)|(0.245)|
|General|||||||-0.164|-0.049|-0.370|
||||||||(0.193)|(0.265)|(0.355)|
|No. Employees||||1.299<sup>_∗∗∗_</sup>|0.883<sup>_∗∗∗_</sup>|1.465<sup>_∗∗∗_</sup>|1.095<sup>_∗∗∗_</sup>|0.914<sup>_∗∗∗_</sup>|0.973<sup>_∗∗∗_</sup>|
|||||(0.077)|(0.111)|(0.126)|(0.129)|(0.173)|(0.219)|
|Capital Stock|||||0.513<sup>_∗∗∗_</sup>|||0.409<sup>_∗∗∗_</sup>||
||||||(0.059)|||(0.074)||
|Log(Sales)||||-1.793<sup>_∗∗∗_</sup>|-1.802<sup>_∗∗∗_</sup>|-1.952<sup>_∗∗∗_</sup>|-1.591<sup>_∗∗∗_</sup>|-1.659<sup>_∗∗∗_</sup>|-1.689<sup>_∗∗∗_</sup>|
|||||(0.062)|(0.091)|(0.101)|(0.098)|(0.130)|(0.171)|
|3-digit sector|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations|13472|7378|5734|13442|5567|5720|6094|2978|2047|
|Adjusted _R_<sup>2</sup>|0.157|0.096|0.207|0.241|0.205|0.285|0.281|0.225|0.347|
|df<br>~~m~~|123|84|66|125|87|68|121|86|66|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**25**
**_Table 12a: Effect of school size, difference in dependent variable, relative to median school size_**

|Dependent Variable|type|FirstQuartile|Median|ThirdQuartile|At Max|Max at|
|---|---|---|---|---|---|---|
|Participation Ratio(Ukrainian)|urban|-3.8|0|3.5|6.3|1060|
|Participation Ratio(Ukrainian)|rural|-1.5|0|2|5.6|470|
|Average Ukrainian Score|urban|-2|0|2|6.4|1600|
|Average Ukrainian Score|rural|-0.4|0|0.5|1.3|427|
|Average Mathematics Score|urban|-2|0|2.1|6.8|1641|
|Average Mathematics Score|rural|-0.4|0|1.3|3.1|443|
|Average Ukrainian HistoryScore|urban|-1|0|1.2|8.5|2159|
|Average Ukrainian HistoryScore|rural|-0.3|0|0.5|1.1|422|
|Average Ukrainian Score - Female Students|urban|-1.8|0|1.9|5.9|1584|
|Average Ukrainian Score - Female Students|rural|-0.6|0|0.8|2|446|
|Average Ukrainian Score - Male Students|urban|-2|0|2.1|7.5|1762|
|Average Ukrainian Score - Male Students|rural|-0.6|0|0.8|2|446|
|% students having> 173 on the Ukrainian Exam|urban|-2|0|2.3|15.6|3065|
|% students having> 173 on the Ukrainian Exam|rural|-0.7|0|1|3.1|538|
|% students having< 135.5 on the Ukrainian Exam|urban|3.2|0|-3.2|-7.5|1291|
|% students having< 135.5 on the Ukrainian Exam|rural|0.36|0|-0.4|-0.7|316|
|Participation Ratio - weighted average variables|urban|-2.9|0|2.7|4.9|1105|
|Participation Ratio - weighted average variables|rural|-0.9|0|1.1|2.5|430|
|Average Ukrainian Score- weighted average variables|urban|-1.8|0|1.8|4.8|1408|
|Average Ukrainian Score- weighted average variables|rural|-0.3|0|0.3|0.5|344|

Differences are percentage points for the participation ratios and points for the test scores For the 2010 regressions, school quartiles are at about 100, 140 and 210 for rural areas and 305,465 and 666 for urban areas. For the weighted average regressions, quartiles are at 110,154 and 225 for rural areas and 322, 484 and 685 for urban areas 57
# **Figure 6 Projected Increase in Share of Age Cohort Working as Migrants after a One Percent Increase in Village Migrant Share**

<!-- Start of picture text --> 5<br>4.5<br>4<br>Age<br>3.5 Cohort<br>3 16-20<br>21-25<br>2.5<br>26-30<br>2<br>31-35<br>1.5 36-40<br>41-45<br>1<br>46-50<br>0.5 51-55<br>56-60<br>0<br>1995 1997 1999 2001 2003<br>Year<br>%<br><!-- End of picture text --> Note: This graph calculates the percent increase in migration by age cohort corresponding to a one percent increase in the share of the village workforce working as migrants. We do so by computing how many additional migrants would be implied by a one percent increase in each year _t_ , and multiply by the share of migrants in each age cohort in the sample to understand how the one percent increase affects each cohort. Dividing by the size of the age cohort in year _t_ , we calculate the relative size of the increase in migrant share for each age cohort. See Appendix Figure A.7 for the evolution of the share of each cohort working as migrants over time (panel A) and the age distribution of the village workforce (panel B).

35
**Table 1: Results of the gravity equation estimates on panel data**

|||**M**|**ijt**||
|---|---|---|---|---|
|**Variables**|**1. GLS**|**2. HT****~~a)~~**|**3. HT****~~b)~~**|**4. HT****~~a)~~**|
|**Ln Yit**<br>**Ln Yjt**|0.852***<br>[141.01]<br>1.069***|1.066***<br>[35.055]<br>1.077***|0.973***<br>[28.655]<br>0.954***|1.078***<br>[29.963]<br>1.168***|
|**Ln Rit**|[174.82]<br>0.463***|[39.071]<br>0.579***|[28.327]<br>0.409*|[35.408]<br>0.503**|
|**Ln Rjt**|[7.21]<br>1.263***|[2.648]<br>0.919***|[1.753]<br>0.722***|[2.206]<br>1.049***|
|**ln Dij**|[19.78]<br>-1.437***|[4.220]<br>-1.507***|[3.100]<br>-1.494***|[4.705]<br>-1.549***|
|**Bij**|[73.51]<br>1.267***|[31.846]<br>0.984***|[29.335]<br>0.844***|[31.946]<br>0.901***|
|**Li**|[12.48]<br>-0.246***|[4.364]<br>-0.245***|[3.443]<br>-0.348***|[3.877]<br>-0,105|
|**Lj**|[6.98]<br>-0.504***|[2.658]<br>-0.408***|[3.493]<br>-0.527***|[1.046]<br>-0.287***|
|**ln (1+Kit) **|[14.31]<br>0.290***|[4.424]<br>0.269***|[5.194]<br>0.287***|[2.836]<br>0.226***|
|**ln(1+Kjt)**|[13.13]<br>0.507***|[6.519]<br>0.204***|[6.850]<br>0.226***|[4.694]<br>0.234***|
|**ln Nit**<br>**ln Njt**<br>**ln RERijt**|[22.87]|[5.043]|[5.517]<br>0.169**<br>[2.569]<br>0.235***<br>[3.720]|[4.991]<br>-0.043***<br>[6.445]|
|Number of obs (NT)<br>Number of bilateral (N)<br>R2|185,967<br>23,470<br>0.659|185,967<br>23,470|185,967<br>23,470|154,871<br>22,159|
|Hausman test W vs. GLS**c)**<br>_Chi-2(Kw)_|875.72***<br>_chi-2 (18)_|-|||
|Hausman test HT vs. GLS**d)**<br>_chi-2(K)_|-|143.88***<br>_chi-2 (22)_|||

***, ** and * significant at the 1%, 5% and 10% level respectively (Absolute values of t-student are presented under the correspondent coefficients).

The time dummy variables and the constant are not reported due to space constraints.

> **a)** Hausman –Taylor method with endogenous variables = lnYit, lnYjt, ln(1+Kit) and (1+lnKjt **)**

> **b)** Hausman –Taylor method with endogenous variables = lnYit, lnYjt, lnNit, , lnNjt,  ln(1+Kit) and (1+lnKjt **)**

> **c)** This test is applied to the differences between the within and GLS estimators.

> **d)** Hausman test applied to the differences between GLS and HT estimators.

18
in out-of-pocket tobacco-related medical expenses could represent 0.5 percent of household income for the poorest 10 percent of the population.<sup>18</sup> Figure 4. Effect of reducing medical expenses Panel A. 25% Price increase Panel B. 50% Price increase <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Medium-bound Elasticity Lower-bound Elasticity Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>.3 .6<br>.2 .4<br>.1 .2<br>Inconme Gains (%) Inconme Gains (%)<br>0 0<br>1.5<br>1<br>.5<br>Inconme Gains (%)<br>0<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016, HIES (2017), Goodchild et. al (2018) and WDI (2017). _Notes:_ Only _outof-pocket_ tobacco-related medical expenses are considered. Deciles are created based on per capita household consumption.

# (C) Change in productive Years of Life Lost (YLL)

Panels A to C of <u>Figure 5</u> show the additional share of income that households could gain as labor incomes, after the tobacco tax increase results in lower years of life lost. Compared to other country applications of the ECBA, these results seem more progressive. This distributional pattern is mainly driven by the high variance in price elasticities across deciles, and the price-responsiveness among lower-income households

> 18 Following standard tax incidence analysis, Figures 2 to 6 plot the average income change for each decile. This average, however, considers both smoker and non-smoker households in the decile, e.g. those who spend on tobacco and suffer the direct negative consequences of smoking, and those with zero expenditures on tobacco and no direct income losses from smoking (assuming no externalities or spill-over effects, and no form of fiscal cross-subsidy to smokers). The impact on households with smokers (relative to their own income level) would be higher than this average effect.

19
# _Few benefits in wage employment_

Digging deeper into the status of wage workers, it becomes apparent that wage employment does not often afford the benefits that would be expected from this type of employment. The great majority of wage workers are employed on fixed-term contracts or without a formal contract (Table 17). Women are slightly better off than men, a third of employed women report having a permanent contract compared to a quarter of employed men. About 10 percent of women and 12 percent of men were working without having any type of contract or agreement in 2009. Although the share of workers with no contract decreased between 2007 and 2009, there was a small decrease in the share of workers under permanent contracts.

**Table 17: Contract types for wage workers (2009, %)**

|||2007<br>|||2009<br>||
|---|---|---|---|---|---|---|
||Men|Women|Total|Men|Women|Total|
|Permanent|28.2|38.3|32.25|26.1|34.2|29.4|
|Fixed term (3 years or less)|22.4|27.5|24.5|22.8|30.0|25.8|
|Verbal agreement|34.3|22.3|29.45|38.9|25.9|33.7|
|No contract|14.7|11.4|13.4|12.1|9.5|11.0|
|Others|0.5|0.5|0.5|0.1|0.04|0.06|
|Total|100|100|100|100|100|100|

_Source: (LFS, 2007 and 2009)_

# _Informal employment_

Vietnam is not the only country where such results are found. In fact, the recognition that formal sector firms may employ workers informally, or provide limited or none of the benefits prescribed by labor laws has prompted statisticians across the world to define formally the concept of informal employment. This has the advantage of providing a measure that is comparable across countries. Moreover, it has the further advantage of being relevant for any country, including developed countries, which have seen an increase in “precarious” employment (i.e. employment that does not offer the usual benefits associated with permanent wage employment contracts). For measurement purposes, informal employment has been defined as unpaid family workers and wage and 19
limited funds from family and friends, the other nonformal financing sources consist of suppliers' credits in the form of trade credit, buyers' advances (either in cash or in raw materials and supplies), moneylenders, various "club" systems pooling membership savings for loans, credit cooperatives and NGOs. The costs of financing facilities in the nonformal sector are typically twice or more than the prevailing interest rates charged by institutions in the formal sector. Moreover, a number of developing-country governments control interest rates and influence the allocation of credit, usually in favor of large-scale modem industry and exporters. This either drives up interest rates to MSBs or, if lending rates are controlled, enables even fewer small firms to obtain credit. The smaller the enterprise, the more likely it is to be denied credit from formal sector institutions.

28. Data available from the experience of various countries indicates that the MSB sector is an important, large and growing subsector of many countries in terms of value added generated and number of the labor force absorbed.<sup>10</sup> Equally important are the subcontracting linkages between MSBs and the larger industrial firms in urban areas as well as between them and the agricultural sector in rural areas. It can then be argued that it is possible to achieve relatively high impact on productivity and output of MSBs by means of additions to equipment used in production and application of new technology to operations. Lease financing is especially suited for this role of providing the financial resource for acquisition and use of equipment and new technology by MSBs, leaving the borrower's nonformal sources available for working capital needs. Box 3 below summarizes financing options available to small businesses and microenterprises with and without a leasing industry.

15elei~s tt 10 MSBs employ a substantial portion of the econonmically active population in the informal sector, particularly in developing countries. In the Statistical Yearbook of the International Labour Organisation (ILO), the pertinent classification would be employerlself-employed for own account and the noncompensated household members employed in family enterprises categories. ILO statistics show that ahnost 60% of the economically active population is employed in business estabishments in the informal sector. In Sri Lanka, it is reported that 80% of industries are composed of MSBs. Similar observations have been made in other countries like Bangladesh, the Philippines and Indonesia in a number of studies and reports on microenterprise development.