rafmacalaba/data-use-mentions-extended huggingface dataset:
may pose financial hardships for the poor if they are applied strictly on monthly consumption. To address this concern, in Cape Town, South Africa, free electricity is based on monthly consumption averaged over the past 12 months. Using moving averages and issuing warning messages in advance to customers as they approach the limit are two ways of alleviating such hardships.

- High appliance efficiency further helps reduce monthly bills. Recent advances in energy efficiency improvement are enabling households to meet basic electricity needs for as little as 15 kWh a month. For example, using four light‐emitting diode light bulbs of 800 lumens each for four hours a day, charging a cell phone, running a large fan for five hours a day, and watching one small efficient television for three hours a day consume about 15 kWh over a month. That said, light‐emitting diode light bulbs and other efficient appliances are relatively expensive, making it difficult for the poor to pay the upfront costs—one of the main reasons energy is more expensive for the poor than for the rich, exacerbating energy poverty. Financial assistance may be needed.

- For gender equality, helping the poor is likely to go a long way in helping female‐headed households. Once both per capita and total household expenditures are accounted for, there is little evidence that female‐headed households are disadvantaged with respect to electricity use, except in urban Ghana and Togo. That said, these are preliminary findings, and the gender dimension should be investigated further to see how robust this finding is.

Utilities can take steps to provide more targeted assistance to low‐income households without increasing their deficits markedly:

- Rolling out prepaid meters helps utilities. Utilities are assured of prompt payment for every kWh consumed.
can be useful, especially when considering disaggregated statistics, but should – at least for a subset of the data – be validate <mark>d by traditional data sources.</mark>

**To operationalize the measurement of energy poverty, Bulgaria could follow the model previously explored in Belgium.** In Belgium, the King Baudouin Foundation has developed an Energy Poverty Barometer, considering the multidimensional nature of energy poverty (Meyer et al., 2018).<sup>46</sup> The barometer considers several complementary measures of energy poverty and considers that different subsets of the population might suffer from different aspects of the overall concept of energy poverty (ibid). The Energy Poverty Barometer considers the following dimensions of energy poverty: measured energy poverty, hidden energy poverty, and perceived energy poverty measured, respectively, by the share of households reporting excessive energy bills compared to available income, restriction in energy consumption below basic needs, and self-reported difficulties to heat the housing adequately.

**In addition, Bulgaria could learn from success stories and challenges identified in other countries, such as France, when operationalizing energy poverty.** The EU Energy Poverty Observatory (EPOV) 47(2019) published a case study outlining France's successfully implemented energy poverty measures. This initiative relied on two complementarity measures: the energy stress indicator, based on household energy expenditure and income ratio, and the indicator on the ability to keep the home adequately warm. The main challenges identified in the case study were little awareness of energy poverty, lack of data, insufficient evaluations of existing energy poverty policies to identify what works and does not work, and lack of coordination between the involved stakeholders.

**Moreover, the chosen indicator tracked to measure progress in the fight against energy poverty should reflect the aspect of energy poverty that the government aims to address.** Suppose the government aims to implement long-term approaches to address energy poverty via renovation programs, for example. In that case, observational indicators about the square
to Keep Older People in the Labor Force. Economic Studies at Brookings, January 2019.

- NCST. 2011. Seniority Community Service Employment Program: A resource for SCSEP providers. National center on senior transportation: Washington, D.C.

- Neumark, D., I. Burn and P. Button. 2019. Is It Harder for Older Workers to Find Jobs? New and Improved Evidence from a Field Experiment. Journal of Political Economy, 127 (2).

- Ng, T.W. and C. Feldman. 2008. The relationship of age to ten dimensions of job performance. J Appl Psychol. 93(2):392-423.

- OECD. 2008. Improving Financial Education and Awareness of Insurance and Private Pensions. OECD Publishing: Paris.

OECD. 2013. OECD Employment Outlook 2013. OECD Publishing: Paris.

OECD. 2017: Pensions at a glance. OECD Publishing: Paris.

OECD. 2018. Working Better with Age. Belgium Country Note. OECD Publishing: Paris.

OECD. 2019a. Pensions at a glance. Country profiles: China. OECD Publishing: Paris.

OECD. 2019b. Working Better with Age. OECD Publishing: Paris.

OECD. 2019c. Adapting to Demographic Change. Paper prepared for the first meeting of the G20

- Employment Working Group under the Japanese G20 Presidency, 25-27 February 2019, Tokyo. Retrieved at: https://www.oecd.org/g20/summits/osaka/OECD-Ageing-and-Demographic-changeG20-JPN.pdf

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Policy Research Working Paper 7555

# **Abstract**

This paper looks at how individual preferences for the allocation of government spending change along the life cycle. Using the Life in Transition Survey II for 34 countries in Europe and Central Asia, the study finds that older individuals are less likely to support a rise in government outlays on education and more likely to support increases in spending on pensions. These results are very similar across countries, and they do not change when using alternative model specifications, estimation methods, and data sources. Using repeated cross-sections, the analysis controls for cohort effects and confirms the main results. The findings are consistent with a body of literature arguing that conflict across generations over the allocation of public expenditures may intensify in ageing economies.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hwinkler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
industry (but are not direct competitors). All startups were deemed by Vilcap to be of high quality and at similar stages. Baseline studies suggest that all startups accepted into the Vilcap program have similar observable characteristics, which does not differ by founder gender (Burns et al. 2019). Vilcap also provided access to a curated set of investors that had expressed an interest in startups at an early-stage and in a specific industry, which limits the sample to investors that have the potential to be interested in this stage of startup. Any differences in results should not be driven by differences in startup quality, nor investor interest.

Vilcap’s evaluation process is typical of the average investment organization in several ways. The average investment organization employs a collective evaluation process to decide whether to invest their organization’s funds into startups (Tyebjee and Bruno 1984, Fried and Hisrich 1994), which typically takes about 90 days (Gompers et al. 2020). Investment organizations assess startups using organization-level criteria which typically include assessments of the founding team, market size, product and business model (Cohen et al. 2019b, Gompers et al. 2020). As shown in a simplistic model in Figure 1, when evaluating, investors typically receive information such as a pitch deck or overview, meet founders, and then make a decision on whether to continue to diligence (or inquire about) the startup. This process repeats, as investors progress through deeper stages of diligence before they invest in a startup.

In Vilcap, this process unfolds with two types of investors. Professional investors,<sup>1</sup> embedded in a range of investment organizations, meet startups once. They receive a venture overview, written by Vilcap, meet approximately three founders for thirty minutes, and evaluate startups using a Vilcap survey where they decide if they wish to conduct due diligence, and ask for additional information

> 1 Professional investors were invited by Vilcap and included other accelerator managers, investors from angel groups, and early-stage venture capital
13 and it greatly increases the prospects for survival and development of her living children.

# IV. COST-EFFECTIVENESS OF FAMILY PLANNING SERVICES IN PREVENTING MATERNAL MORTALITY AND MORBIDITY

Family planning services appear to compare favorably from the standpoint of cost-effectiveness, with other means of preventing mortality and ill health in women in their childbearing years. To our knowledge, the cost-effectiveness of family planning services in imoroving maternal health has not been measured directly from actual country program data. In the absence of such empirical data, estimates depend on assumptions regarding fertility, contraceptive use, and the cost of delivering health and planning services in a given setting. A recent study attempted such estimates.

Herz and Measham (1987) estimated the cost of averting maternal deaths by taking two hypothetical populations drawing data from Asia and subSaharan Africa and calculated the cost of maternal health and family planning programs from estimates of their constituent parts (personnel, equipment, supplies, etcetera), again using composite data from several countries. The results are summarized in tables 3-6. Table 3 sets forth the demographic, economic and health infrastructure characteristics of two hypothetical districts in which 'Safe Motherhood' programs are to be mounted. Tables 4 and 5 give the costs of the two illustrative programs, one a 'moderate" program effort, the other a more limited effort. And table 6 provides the estimated cost and impact of the two illustrative programs.

The Herz/Measham estAme-tes are for the incremental capital and recurrent costs of a maternal health and family planning program in the hypothesized settings. The cost per maternal death averted is US$4,800 and US$6,250, respectively, for the illustrative programs. The more limited effort appears more cost-effective mainly because it is usually easier and less costly to reduce mortality from a very high level to a slightly lower level, than to achieve equal reductions at lower levels of mortality. We assume, conservatively, that the cost of averting one maternal death through family planning services is no more (or less) than the cost of averting
# **Antidumping, Safeguards, and Countervailing Duty Policies**

Antidumping, safeguards, and countervailing duty policies are other types of trade policy barriers.<sup>8</sup> After the global financial crisis of 2008/9, many developed and developing countries increased the use of TTBs to protect domestic industries, thereby constraining trade. Between 2007 and 2009, seven major emerging economy G20 members collectively increased by roughly 40% the share of non-oil import product lines affected by this type of restriction (Bown, 2012). Table 12 summarizes newly constructed indicators of TTBs, which have been assembled by Bown (2012). Column 2 of Table 12 reports the share of import product lines, at the 6-digit Harmonized System level, over which the policy-imposing economy had at least one import-restricting TTB in effect in 2011. This measure captures the stock of products covered by the accumulation of TTBs over time and were in effect in 2011. Columns 4 through 8 present other indicators constructed with trade data.

Table 12. TTB Polic ~~Product line share of~~ <u>y-Imposing Economies and Affected Imports in 2011</u>

|Country|All TTBS in<br>2011<br> <br>impo|All TTBs in<br>2001<br> <br>rts|All TTBS|AD only<br>2011 Trade-|from China<br>Weighted shar|from other<br>emerging<br>economies<br>e of imports|from high-<br>income<br>economies|
|---|---|---|---|---|---|---|---|
|China|1.4|0.3|3.2|3.2|.|1.1|3.9|
|Indonesia|1.8|0.9|1.2|0.8|2.4
we aim to quantify both the distributional and efficiency implications of political connections.

Indonesia provides a suitable environment for analyzing the effect of democratization on competition. Suharto’s autocratic regime was marked by state capture. His family had extensive and highly diversified business interests and is estimated to have amassed $15-$35 billion during his 31 years in office from 1967-1998 (Koerner, 2004, Transparency International, 2004). Cronyism was rampant, and it was well known that ingratiating oneself with the president’s family was an important enabler of business success (McLeod, 2000; Basri, 2001; Mobarak and Purbasari, 2006a, 2006b). Suharto’s fall, in the wake of the financial crisis, was largely unexpected. Indonesia furthermore has high-quality plantlevel manufacturing census data spanning the Suharto era, the crisis and its aftermath in which 246 firms with political connections to the Suharto regime were identified by Mobarak and Purbasari (2006a). Among these, 86 firms are owned by Suharto family members, which is our preferred proxy for being 2
- 83 -

- requested, as compared with 354 for Brazil and 571 for the USA (Kravis, Heston and Summers, 1982, p. 45).

- 2) Although our "price" information is implicit, the unit values we derive refer to actual transactions, and they cover all such transactions throughout the year and for all parts of the country. Specification prices, by contrast, are quotes, shelf, list or monitored market prices for one point in the year in a limited number of locations. For example, for Mexico, in order "to obtain national average prices it was necessary to obtain an average of the various urban prices and to take account of rural prices. The adjustment for rural prices was done roughly on the basis of a sample survey of forty common items in rural areas linked to several of the major provincial cities. From these rural and urban prices, adjustment factors were obtained to move from urban to national average prices" (Kravis, Heston and Summers, 1982, p. 43). Quite clearly, the ICP pricing technique involves an elaborate process of collection, adjustment, and data merge, and what comes out of in the wash is not always as clean as was specified.

# Conclusion

The industry of origin and the expenditure approaches are complementary techniques. Each approach had its weaknesses and its strengths. A detailed reconciliation is not feasible by comparison of unit values and specification prices because the one approach deals with producer prices and the other with final expenditure. The nature of the reconciliation problem also depends on whether the basic comparisons are of a binary kind, such as we have attempted here and which was also the case in the early expenditure comparisons of OEEC; or multilateral, as was the case in ICP III, and in the recent studies of EUROSTAT and OECD. In multilateral studies where "international" prices are used, the problem of representativity becomes much more complex, as items have to be selected which are "representative" across a very wide range of
jj=1 _Zi_ is a _J_ -vector of covariates used to explain technical efficiency, which includes plot manager age, an indicator for the manager’s attainment of primary education, an indicator for whether the plot manager received agricultural extension services, the dependency ratio of the household, and the number of agricultural assets owned by the household. Uncertainty around climatic factors, which may influence farmer behavior with respect to farming practices, is proxied by the coefficient of variation of flowering season rainfall (over the period 1999 – 2014), which is also included in _Zi_ . Additional controls were initially included, such as gender of the plot manager and seasonal rainfall shocks, but due to correlation with other covariates and lack of explanatory power in linear regressions, these were ultimately excluded. Indicators of access to credit and public infrastructure could theoretically be included in _Z_ , although data on these covariates are not available for this

> 13 The onset of the season is defined following the Water Requirement Satisfaction Index (WRSI), such that the season begins when at least 25mm of rain falls in one dekad, and a total of at least 20mm of rain falls in the subsequent two dekads (documentation available here: https://goo.gl/sgmTK8).

> 14 Pure stand plots are those on which only maize is grown. Intercropped plots are those on which maize and at least one other crop is grown. The “seeding rate” included here is a ratio of the quantity of maize seed used on the plot to the quantity of seed that would have been used had the farmer planted only maize. The seeding rate is, therefore, bounded (0,1] and equals 1 for all pure stand plots. The seeding rate is included in addition to the dummy variable for cultivation pattern because it is believed that some combinations of crops could improve potential maize yields.

13
In the section below, we match these national, regional and sectoral mandated prices with plant-level information to construct plant-specific energy prices for Mexico.

# **3 Data**

Both Indonesia and Mexico maintain some of the most detailed manufacturing plant-level datasets available in developing countries. These include information on expenditure of energy consumed by energy source, investment by type of asset and other key information on production that can be used to estimate productivity. The plants are tracked over time with very high response rates, which makes the panel representative in every year. That is unusual especially for developing economies, for which the scarcity of granular data has constrained the evidence on the impact of environmental policies on firms’ performance.

## **3.1 Indonesian Data**

Plant level data are taken from the Indonesian survey of manufacturing plants with at least 20 employees (Statistik Industri) administered by the Indonesian statistical office (BPS). The coverage of the survey is extensive; in fact it becomes an actual census in 1996 and 2006 and it is very close to a census in the remaining years, hence ensuring high representativeness even at the provincial level. Plants are grouped into 5 digits sectors following the definition Klasifikasi Baku Lapangan Usaha Indonesia (KBLI), a classification mostly compatible with ISIC Rev.3. The KBLI classification has been adjusted to be consistent over the whole sample, ranging from 1998 to 2015. The data also include information on products defined at a more refined level (9-digit Klasifikasi Komoditi Indonesia), which we use in the computation of product level TFPQ as explained below. The plant level data provide information on several variables such as output, capital stock, employment, materials and energy usage (price and quantities) by type of energy.<sup>20</sup>

> 20To maximize the reliability of the data, we dropped an observation from the raw sample when at least one of the variables used in the analysis had an implausible value. For instance, we dropped an observation when a plant reported
Jan Jan Jan Jan Jan<br>2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027<br><!-- End of picture text --> Figure 1. Predictions made with final model, aggregated across all IDA, Blend and IBRD countries (144).

_Note:_ The total food insecure population is based on the predicted prevalence rates, combined with the country population totals taken from the WDI database, and the population growth rates projected by the WEO. The black solid line corresponds to the period with observed data (2015-2019), the historical part of the black dashed line are predictions generated based on historical covariate values from the World Bank WDI database and the future values are generated using the IMF’s WEO outlook of April 2022. The red dash line is a downside projection that considers the slowed growth rates and higher inflation rates identified in the WEO’s downside analysis. _Source:_ Figure prepared by the author for this paper.

plistic scenario in which the GDP rate reductions are applied equally to all countries as point percentage reductions and the food price inflation rates are applied as relative multipliers (times 2 in 2022, times 1.5 in 2023) to the default WEO CPI inflation rates.
> Other channels of transmission of the shock can also contribute to its uneven incidence. For example, rents in oil producing and closely related sectors (such as refineries) will be directly hit. Given the large capital stocks of the oil sector, capital returns may also suffer larger losses vis‐à‐vis labor returns. This shift

> 13 Note that if rich consumers purchase a larger share of imported goods, they may be impacted more severely than poorer ones by the increase in import prices. Some authors [Broda and Romalis 2008] have studied this issue for the case of the US. They found that lower import prices of goods originating in China have actually helped the poorer consumers in the US. However, the current household data for Russia do not allow to investigate this channel.

15
Where: MLV: estimate net monthly living wage CBpc: estimate minimum monthly consumption basket per capita (food +non-food component)<sup>14</sup> HH Size: Estimate average household size FT: Estimate the average number of full-time workers in the household

**This approximation needs to be extended to consider non-labor income sources and part-time vs fulltime work.** When extending this approach to include non-labor income sources and accounting for work intensity (the average amount of full-time vs. part-time employed household members), the approximation becomes:

MLVa= estimate net monthly living wage (adjusted) α = estimate labor income (as a share of overall household income), and FTE = number of full-time equivalent workers. An FTE of 1.0 is equivalent to a full-time worker, while an FTE of 0.5 signals half of a full-time worker (or a part-time worker).

**A target group must be chosen since the monthly living wage estimate varies significantly for households with different household compositions and across the income distribution.** The reference household used for our estimates is a “relative” poor household, defined as a household with disposable income lower than 60% of median household incomes. This assumes that the policy objective is to provide a minimum income so the “relative poor household” can afford a minimum consumption basket.

**Since there is no official measurement of the representative food basket as a relative poverty line is used in the EU, we use a Romanian food basket recently estimated by the EC under the European Reference budget network.** In 2015, the Romanian basket was estimated under the European Reference budget network.<sup>15</sup> The Romanian food basket indicates the monthly budget needed for an adequate food intake

> 14 This is equal to the poverty line estimate in countries measuring poverty using consumption, rather than income.

> 15 Reference budgets have been developed across the EU by _the Herman Deleeck Centre for Social Policy, University of Antwerp and DG