rafmacalaba/datause-encoder-data huggingface dataset:
|olds with<br>survey rounds)<br>Full Sample_b_<br>28.9<br>28.3<br>10.5<br>8.7<br>8.7<br>16.4<br>11.8|28.9<br>13.3<br>8.2<br>1.4<br>5.1<br>7.0<br>8.0<br>3.8<br>4.8<br>29.8<br>6.9|10,978||
|---|---|---|---|
|e of househ<br>ck (across <br>Uganda<br>43.2<br>42.3<br>8.5<br>6.4<br>3.7<br>3.3<br>3.4|26.0<br>15.8<br>5.8<br>0.2<br>0.8<br>8.0<br>2.6<br>1.6<br>5.2|3,487||
|Percentag<br>ion in sho<br>Nigeria<br>24.3<br>6.2<br>11.5<br>2.0<br>4.5<br>5.4<br>1.7|27.3<br>6.1<br>8.5<br>0.4<br>4.8<br>4.6<br>5.1<br>1.1<br>5.8<br>18.0<br>2.8|3,340||
|transit<br>Malawi<br>20.5<br>34.4<br>11.3<br>15.9<br>16.3<br>36.2<br>27.1|32.5<br>17.0<br>9.9<br>3.1<br>9.0<br>9.1<br>10.2<br>7.1<br>6.6<br>39.3<br>11.6|4,151|d waves.|
|ull Sample_b_<br>43.3<br>31.9<br>7.0<br>5.8<br>5.7<br>19.6<br>8.9|22.5<br>8.6<br>5.2<br>1.5<br>3.9<br>5.2<br>5.4<br>2.9<br>3.1<br>32.2<br>4.7|24,781|.<br> countries an|
|obs.<br>ne year)<br>F|||k, 2018)<br>a across|
|sehold-wave<br>ge during o<br>Uganda<br>40.6<br>35.9<br>4.8<br>3.2<br>1.7<br>1.7<br>1.6|15.7<br>8.1<br>3.1<br>0.1<br>0.3<br>4.1<br>1.1<br>0.8<br>2.5|8,686|World Ban<br> pooling dat|
|e of hou<br>n avera|||tabase (<br>ined by|
|Percentag<br>with shock (o<br>Nigeria<br>12.0<br>2.8<br>5.5<br>0.8<br>1.8<br>2.2<br>0.7|14.1<br>2.6<br>3.7<br>0.2<br>2.2<br>1.9<br>2.2<br>0.5<br>2.4<br>8.2<br>1.2|8,578|LSMS-ISA da<br> sample is obta|
|Malawi<br>82.1<br>60.6<br>11.4<br>14.4<br>14.8<br>60.3<br>26.8|40.1<br>16.1<br>9.4<br>4.5<br>10.0<br>8.9<br>10.5<br>7.6<br>6.6<br>59.7<br>11.4|7,517|data from the<br>ries. _b_The full|
|Description_a_<br>_Agricultural shock (AgShock)_<br>Drought/irregular rains<br>Floods/landslides<br>Crop pests or disease<br>Livestock disease<br>Increase in price of agricultural inputs<br>Fall in price of agricultural output|_Non-agricultural shock (NonAgShock)_<br>_Health_<br>Illness or accident of household member<br>Death or disability of household member<br>_Income_<br>Loss of employment<br>Reduction in non-farm income<br>End of assistance/remittances<br>_Crime_<br>Theft of money/valuables<br>Confict/violence<br>_Other_<br>Fire/earthquake/other damage<br>_Not classifed_<br>Increase in prices for food<br>Other (not specifed)|Observations|_Source:_ Authors’ calculation based on survey<br>_Notes: a_Exact wording difers between count|

18
**_Energy demand models for policy_** **_<u>formulation</u>_**

Most of the end-use models emerged after the first oil price shock in the 1970s but many have undergone major evolution to reach their present state. For example, the technological representation in MARKAL is supported by a detailed database. Similarly, the case examples and the modeler community have grown and sharing of experience is often possible through dedicated reference libraries, discussion forums and training. Further, most of the models now run on PC versions and can operate on a stand-alone basis.

As most of the end-use models do not rely on the neo-classical economic paradigm, it brings a very different perspective on energy system analysis. These models are capable of capturing rural-urban divide and can include informal activities. They can also capture the diversity of actual processes and technologies of energy conversion and use, and accordingly need not rely on stylistic, aggregate and a single vintage representation of technologies. As these models do not rely only on past history or evolution, they can capture structural changes and new technological developments. In fact, this is one of the major strengths of this category of models. Through the formulation of different scenarios these models try to capture different development trajectories and the influences of policies on economic development. However, accounting-type end-use models suffer from their inability to capture price-induced effects alongside non-price policies, thereby reducing their effectiveness for certain policy analyses. Moreover, the issue of overall consistency of the isolated assumptions used in the models looms large in models which do not include a macro-linkage.

# **3.2.2.3 Input-output models**

The input-output method has long been used for economic analysis. It provides a consistent framework of analysis and can capture the contribution of related activities through inter-industry linkages in the economy. Thus the input-output method is able to capture the direct energy demand as well as indirect energy demand through interindustry transactions. This feature makes this method an interesting analytical tool. Box 6 35
If there is sufficient expressive benefit from conforming to a trusting or trustworthy identity, the payoffs in the trust game inclusive of expressive benefit can result in a Pareto-efficient equilibrium, achieved through all 10 euros being transferred by the initiator. Because of the possibility that expressive utility can overwhelm material utility, external validity requires sums of money that are large enough for material utility to be potentially significant in affecting behavior. A role for material utility is required for external validity because, while students in experiments may be expressive, business and commercial decisions are generally made on the basis of material gain or reward.

In Montenegro in 2012, official per capita income was $7,160 (World Bank 2012). Unofficially, in all likelihood, as we have noted, per capita income was at least some 30 percent higher – because of unreported income in particular deriving from personal catering, tourism, and related services on the Adriatic coast, and retail trade. Taking Montenegrin per capita income inclusive of the informal sector, the initial endowment of 10 euros is comparatively equivalent to around 40 euros for Germany, France, or the U.K. or $70 dollars for the U.S. If all 10 euros were transferred, the recipient would have the equivalent with reference to western per capita income of 160 euros or $280 U.S. We view such sums as sufficient for considerations of material gain to influence behavior. Individuals who would play out an identity as trusting of others may then not transfer all their initial endowment, so revealing an actual not-expressively-dominated degree of lack of trust – or recipients who would expressively play out an identity of being trustworthy may be tempted under the conditions of anonymity to keep all or a large share of the money.

# **4. Particularized and generalized trust**

Because consequences of trust depend on who is trusted or who is not, it is important to distinguish between particularized and generalized trust (Freitag and Traunmüller 2009). Particularized trust is trust between people who know 11
81 African parliament in June 2015 passed the Eskom Special Appropriation Bill to address a shortfall in the fiscal year ending in March 2016 by injecting 23 billion rands in Eskom, to be raised from the sale of non‐ strategic state assets. In the same session, the parliament also passed the Eskom Subordinated Loan Special Appropriation Amendment Bill, which would strengthen Eskom’s balance sheet by converting the 60‐billion‐rand loan granted in 2008 to equity.

# **Sri Lanka**

The Government of Sri Lanka has been working to reduce and eventually eliminate energy subsidies for many years. Fuel and electricity tariffs are interlinked because of large fuel price subsidies historically given to electricity producers. The Ceylon Petroleum Corporation (CPC), which is state‐owned and the country’s sole refiner, suffered large losses in 2011 and 2012, and generated a small operating profit only in 2014 (Figure 21). More generally, fuel price and power tariff increases, combined with a doubling of hydropower generation, slashed the losses in the energy sector in 2013.

**Figure 21: Annual losses of Ceylon Petroleum Corporation**

<!-- Start of picture text --> 100,000 $1,000<br>80,000 $800<br>60,000 $600<br>40,000 $400<br>20,000 $200<br>0 $0<br>2009 2010 2011 2012 2013 2014<br>‐20,000 ‐$200<br>SL Rs million US$ million<br><!-- End of picture text --> _Sources:_ Annual reports of the CPC and the Department of Fiscal Policy, Ministry of Finance and Planning. _Note_ : The losses do not include additional expenses associated with the hedging transactions in 2007–2008.

Although annual losses were reduced substantially in 2013, the CPC’s outstanding borrowings from state banks increased slightly from SL Rs 402.5 billion (US$3.1 billion) in 2012 to SL Rs 419 billion (US$3.2 billion) in 2013, and then declined to SL Rs 373 billion (US$2.9 billion) by the end of 2014 (MFP 2014, 2015). These debts not only increased financing costs, but the high levels of borrowing by the CPC and its counterpart in the power sector to cover their losses have significantly affected the banking sector. These loans have hindered efficient allocation of resources to more productive investments and impeded capital formation, while having adverse effects on interest rates, employment, exchange rate, fiscal management, and inflation (CBSL 2014).

A prolonged drought in 2012 reduced hydropower generation capacity and increased demand for fuel oil, when international oil prices were high. Despite tariff increases in February 2012 and price subsidies for petroleum products provided by the CPC, the losses of the Ceylon Electricity Board (CEB) rose in 2012 sharply to SL Rs 61 billion (US$490 million) from SL Rs 19 billion (US$175 million) in 2011. Greater reliance on power generation using petroleum products increased the cost of supply to SL Rs 22.13 (US$0.17) per kWh, while the average tariff was SL 15.65 per kWh (US$0.12) (CBSL 2013). In that year, heavy fuel oil cost the CPC about SL Rs 100–110 (US$0.78–0.86) per liter to supply, but the power sector
# **2.2.1 Increases in the Cost of Borrowing**

In a situation of monetary tightening and/or credit crunch, the external finance premium _(the difference in cost between funds raised externally and funds generated internally to the firm)_ is likely to increase, thus increasing the cost of borrowing. Typically, this increase in the cost of borrowing is the effect of two channels: the **balance sheet channel** and the **bank lending channel.**<sup>**3**</sup>

## **Box.2 TYPES OF CREDIT RATIONING**

Credit rationing can take the following three forns: _(i) Pure rationing_ occurs when some borrowers are denied credit while otherwise identical borrowers receive credit. In this case, the lender has set an interest rate at which the demand for funds exceed the supply; _(ii) Divergent views rationing_ occurs when borrowers would like to borrow at prevailing rates and feel their loans do not present a serious credit risk, but the lenders disagree and refuse to lend. In this situation, borrowers who cannot obtain credit at the prevailing interest rate may conclude that the lenders are rationing credit. Even when borrowers and lenders agree on the appropriate criteria, they may have different forecasts for future asset prices and cash flows; (iii) _Sectoral rationing_ refers to the application of credit standards that effectively shut off the flow of credit to entire sector, such as certain classes of borrowers or types of borrowing. For some borrowing sectors, lenders may find it difficult to distinguish between good and bad credits and therefore choose to make no loans at all. Sector rationing appears in combination with divergent views rationing when borrowers have private information or different views about their own creditworthiness.

Source: _Federal Reserve Bank ofNew York Quarterly Review, Spring 1993._ On the one hand, the **balance sheet channel** emphasizes the potential depressing impact of the monetary squeeze on borrowers' assets and profits, including variables such as borrowers' net worth, cash flow and liquid assets, which increases the risk premium. The increase in the level of interest rates triggered by the monetary squeeze raises corporate risks because it reduces both business profits and the value of assets firms have posted as collateral. This will generally increase the wedge between the interest rates at which corporates can borrow and the yields on risk-free assets (See also Box 3).

For the US, several studies provide convincing empirical evidence on the importance of the above mentioned interest rate spread. More precisely, Kashyap, Stein, _3_ See again Bemanke and Gertler (1995) and Hubbard (1994) for a detailed analysis.

8
# **Online Appendix**

## **A1. Data**

Household surveys are retrieved from the SEDLAC database (CEDLAS and the World Bank, 2019). The data repository covers the period from 1974 to 2015. However, for most countries, the surveys are only available since the 1990s.

To add power to the analysis, I pool data from the adjacent year surveys to the reference years whenever possible. Table A1 lists the mapping of survey years to model year for each country.

**Table A1: Surveys constituting model years**

|Model Year||Survey years included||
|---|---|---|---|
||Costa Rica|El Salvador|Panama|
|1995|1995, 1996|1995, 1996|1995, 1997|
|2005|2004, 2005, 2006|2004, 2005, 2006|2004, 2005, 2006|
|2015|2013, 2014, 2015|2013, 2014, 2015|2013, 2014, 2015|

The sample selection of workers is discussed in detail in the main paper. For the sample, I extract the following variables from the survey – sex, age, years of schooling, occupation, hours worked and labor income.

**Generalities:** The occupational taxonomy assigns workers into 10 professions which correspond to the major groups of the International Standard Classification of Occupations, 1988 (ISCO-88) (ILO, 2012). Following Hsieh _et al._ (2019), the workers engaged in armed forces are dropped. Respondents not in the labor force are assumed to be employed in the home sector. Hence, together with the home sector, a worker can choose from a set of 10 occupations. For Costa Rica and Panama, some recoding is needed to map workers to the same occupational taxonomy.

Besides the number of workers in each _cohort-gender-occupation_ category, I also need the years of schooling, labor income, and hours worked associated with them. Hours worked in the main occupation are reported weekly in each survey, while income is reported on a monthly basis. Years of schooling and labor income are coded by the SEDLAC. The labor income is deflated using the consumer price index (2011=100). Beyond this general transformation, each country presents its own idiosyncrasies, which are discussed below.

**Costa Rica:** There are two different surveys available for the country in SEDLAC. For 1990-2009, the repository contains the surveys from _Encuesta de Hogares de Propósitos Múltiples_ (NISC, 2009) and from 2010 onward the surveys correspond to _Encuesta Nacional de Hogares_ (NISC, 2020). The sampling methodology varies a bit across the two surveys which raises the concern of consistency. However, in practice there are no unusual jumps in the time series of variables around 2010. The yearly change from 2009 to 2010 in the series appears reasonable given the past variations.

Beginning in 2001, the household surveys in Costa Rica adopted the ISCO-88 occupational taxonomy. Before 2001, a national classification was employed. The system was based on the second 36
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# **Table 9: Interaction of Migration with Mother's Schooling**

||Males<br>12 to 15|Males<br>12 to 15|Males<br>16 to 18|Males<br>16 to 18|Females<br>12 to 15|Females<br>12 to 15|Females<br>16 to 18|Females<br>16 to 18|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|
|Child is in a Migrant Household|-0.201|-0.538|-1.244|-1.741|0.138|-0.520|-0.817|-2.211|
||(0.52)|(1.48)|(1.51)|(2.16)*|(0.31)|(1.44)|(0.94)|(2.53)*|
|in a Migrant Household *Mother's Years Schooling|-0.072||-0.042||-0.098||-0.187||
||(1.43)||(0.34)||(1.34)||(1.34)||
|in a Migrant Household * Low Mother's education||0.355||0.796||0.489||1.090|
|||(1.03)||(1.43)||(1.60)||(1.82)|
|Observations|6451|6451|4093|4093|6107|6107|3431|3431|

Robust z statistics in parentheses * significant at 5%; ** significant at 1% Regressions also include all controls in Tables 3-6.

41
of poverty. In fact, there was no change over the decade, with the headcount index staying at 26.5%. Distributional shifts working against the poor exactly offset the gains from growth. What does the empirical evidence suggest about the relationship between initial inequality and the growth elasticity of poverty reduction? Support for the intuition that higher inequality countries tend to have lower (absolute) elasticities was first presented in Ravallion (1997) and subsequently verified by Ravallion (2001) and Kraay (2005). These papers have used regression-based methods (in which rates of change in poverty are regressed on rates of growth both on its own and interacted with initial inequality). We will return to this approach shortly, but first it is instructive to look at the empirical relationship seen in a more flexible way.

A simpler non-parametric method is to calculate the elasticity as the log difference in the headcount index divided by the log difference in the mean, all based on successive household surveys. There is clearly a lot of noise in such a measure. To help reduce the noise, I smoothed the period-specific elasticities by taking the simple average of two-period elasticities (across three surveys). I also trimmed 15 extreme elasticities (below –20 or above 20). Figure 3 gives the results for the “$1 a day” poverty rate. The elasticity is negative in 80% of cases. We see a rather weak tendency for the elasticity to rise (become less negative) as inequality rises, from an average of about –4 at the lowest Gini index to roughly zero at the highest. The correlation coefficient is 0.26, which is significant at the 1% level. The two high positive elasticities in Figure 3 are almost certainly measured with large errors, and this is exaggerating the slope of the line of best fit. Dropping these two observations, the correlation is still significant at the 1% level, and the line of best fit passes through an elasticity of zero at Gini index of about 60%.

In modeling the relationship between poverty reduction and growth, Ravallion (1997) postulated that the rate of poverty reduction (measured as the difference in the log of the measure 9
elasticity was –0.345. Additionally, recent decreases in tobacco consumption have occurred in lockstep with increased prices of cigarettes from the pass-through of excise and ad valorum taxes.<sup>7</sup>

## **3. Model**

In this paper, the impact of raising tobacco taxes in Russia is estimated using an extended cost-benefit analysis as in other studies (Pichón-Riviere et al. 2014; Verguet et al. 2015). The paper analyzes three factors to estimate how tobacco taxes affect household incomes. First, assuming tobacco consumption does not change, tobacco taxes directly affect household income because the share of household budgets allocated to tobacco purchases increases as the tax increases. Second, household medical expenses decrease as a result of a reduction or widespread cessation in tobacco consumption. Third, households experience a positive income change because of additional years of labor recovered through the extension of life expectancy. The aggregate effect of a tax policy is estimated as follows: Income effect = change in tobacco expenditure (A) + lower medical expenses (B) + rise in income (C) (1) For further details on the methodology used, see annex A; refer also to Fuchs and Meneses (2017a).

# **4. Data and descriptive statistics**

## **Data**

For the current analysis, we mainly used the data provided by the RLMS-HSE. This is a rich set of data on individuals and households and covering incomes, expenditures, and health status as well as attitudes toward smoking. The RLMS-HSE is an annual survey that allows researchers to construct panels to follow the same individuals over a few years at a time. The latest available wave of the survey when the analysis was conducted was the 2016 round.

The information on attitudes toward smoking is available on households and individuals. In the family questionnaire are questions about how much households spend on cigarettes and how many packs were purchased during a recent period. This allows the unit values (individual prices) of cigarettes to be calculated for all the households that were involved in smoking. There are also questions on how much the respondent smokes and how much the respondent spends on cigarettes.

Another source of information is cigarette prices in communities. This information is also collected through the survey. Prices for four types of cigarettes are collected in this section of the questionnaire: brands that are domestic, imported, cheaper, or more expensive. However, the variation in prices among households is relatively low. This is why the information is not helpful in the analysis.<sup>8</sup> The analysis was run and is presented below by deciles that were defined using per capita expenditures (unless specified otherwise).

> 7 Sakharova, Antonov, and Salagay (2017) observe a 25 percent decrease in consumption in 2005–14 that correlates with government action on the tobacco epidemic.

> 8 As a robustness check, we tried a few modules in which the prices in communities were used as instruments. However, the results appeared to be statistically unstable.

4