rafmacalaba/datause-encoder-data huggingface dataset:
easily provoke a backlash and because a considerable accumulation of evidence is needed to yield truly valuable new knowledge.

# **References**

Abbring, J. and J. Heckman (2007). ―Econometric Evaluation of Social Programs Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation,‖ in Heckman, J. and E. Leamer (eds.) _Handbook of Econometrics_ , vol. 6B, pp. 5146-5303.

Ashenfelter, O., Harmon, C., and H. Oosterbeek (1999). ―A Review of Estimates of the Schooling/Earnings Relationship,‖ _Labour Economics_ 6, 453-470.

Atkin, D. and A. Khandelwal (2011). ―The Use of Experimental Designs in the Evaluation of Trade‐Facilitation Programs,‖ in Cadot, O., Fernandes, A., Gourdon, J., and A. Mattoo (eds.) _Where to Spend the Next Million? Applying Impact Evaluation to Trade Assistance_ , pp. 107-122. The World Bank and CEPR.

Balat, J., Brambilla, I., and G. Porto (2009). ―Realizing the Gains from Trade: Export Crops, Marketing Costs, and Poverty,‖ _Journal of International Economics_ 78, 21-31.

38
|Dependent variable: Forecast revi|sion of real consumption|
|---|---|
|Constant|-1.967 **|
||(0.98)|
|Stringency index|-0.207 ***<br>(0.02)|
|COVID-19 infections|-0.042 ***<br>(0.02)|
|Adjusted R-squared|0.574|
|No. of observation|63|
|No. of countries|21|
|Sample periods|2020Q1-Q3|

Note: Results obtained from pooled panel regression. Forecast revision of real consumption is the deviation between pre-pandemic forecast (January 2020) and the latest forecast (November 2020) from Oxford Economics; stringency index is Oxford Covid-19 government response stringency index; and covid19 is new “true” covid-19 cases per capita estimated by Imperial College London and is assumed to proxy voluntary social distancing. Standard errors in parentheses. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

Table 1 shows that both the stringency index and COVID-19 cases economic activities can have a significant adverse impact.<sup>9</sup> The historical shocks to consumption obtained from the regression are broadly consistent with results from the behavioral SIR model literature (Bethune and Korinek 2020; Eichenbaum et al. 2020).

# Quantifying global growth impacts

Using parameters from equation (3) and the SIR model’s epidemiological projections, future consumption shocks are extrapolated to consider the impact of both voluntary and mandatory social distancing on private consumption. The expected evolution of the stringency of pandemic-control measures is based on projected COVID-19 cases per capita obtained from the SVIR model combined with economist judgement (Figure 2.C). Consumption shocks are projected for the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and EM7 excluding China (India, Brazil, Mexico, the Russia Federation, Indonesia, and Turkey).

The projected consumption shocks are then mapped into the Oxford Global Economic Model (Oxford Economics 2019). The model permits the quantification of the domestic and global economic implications of these shocks for the outlook.<sup>10</sup> Thus, increased

> 9 The impact of lockdown and voluntary social distancing depends on the type of economic activity. Maloney and Taskin (2020) find that the mobility provided by Google is more driven by “voluntary” individual response to increased COVID-19 case incidence, proxying for awareness or fear or social responsibility, rather than lockdowns. In contrast, Coibion (2020) and IMF (2020) show consumer expenditure is more affected by lockdown, rather than the voluntary measure. Also, the impact could be changed over time. 10 In the model simulations, monetary policy is assumed to respond endogenously to developments in activity and inflation, cushioning the epidemiological shock’s economic consequences. Fiscal policy is assumed to be exogenous beyond existing automatic stabilizer mechanisms except for the United States, where fiscal transfers are increased in the downside scenario.

9
occupations which are intensive in manual tasks towards those more intensive in cognitive tasks demands a greater level of qualification from the labor force.

In this sense, this distribution risk is that the labor market remains represented by two large groups of workers. On one side, those who are highly-qualified, who perform tasks requiring an intensive use of non-routine cognitive tasks, of high productivity and high incomes. On the other side, a group of poorly-qualified workers, relegated to occupy those positions requiring intensive use of non-routine manual tasks, and therefore of low-productivity and low incomes. This will occur while workers with medium-level qualifications and incomes, face the risk of a reduced labor demand in the performance of routine tasks. This polarization may manifest itself through two complementary paths: employability (with increases in the extremities of the productivity distribution and falls in the center) and salary levels.

In order to provide an approximation of the effect that the changes in intensity of the tasks performed by the labor force have on the well-being of the workers, we have estimated here the significance of the relative importance of each task on the explanation of two key variables of workers’ well-being: probability of becoming unemployed and level of income.

The identification of the determinants of the probability of becoming unemployed is done by the estimation of the following probit model: Where �� takes the value of one if the individual finds themselves unemployed and zero if they are employed,12 ��,� represents the intensity of the task j which the individual i performs and Θ� is a vector of individual control variables such as age, a dummy variable which takes the value of one if the person is a man and the number of years of study.

The hypothesis states that those workers who perform routine tasks more intensively have a greater probability of finding themselves unemployed as a result of facing a greater risk of substitution, while those who perform more cognitive tasks have a lesser risk of finding themselves in that situation. The results of the estimation are presented in Table 2.

> 12 The household surveys conducted both in Argentina and in Uruguay include a question relating to the characteristics of the previous employment of the individual in the case that he is currently unemployed.

- 25 -
||on energy consumption<br>or<br>having<br>absolute<br>energy<br>expenditures<br>below<br>a<br>threshold<br>(hidden energy poverty)||the literature on how<br>exactly to measure this; no<br>incorporation of minimum<br>adequate levels of energy<br>(energybasket)|
|---|---|---|---|
|Consensual<br>approaches|Self-reported perception<br>of energy deprivation|Direct and personal;<br>incorporates different<br>energy demands and<br>necessities.|Subjective and personal;<br>lower comparability and<br>higher<br>measurement<br>errors|
|Outcome-based<br>approaches|Measures<br>outcomes<br>based<br>on<br>energy<br>poverty, such as cold-<br>related deaths or arrears<br>on utilitybills|Direct and observable|This might be confounded<br>by other factors, such as<br>low savings, low public<br>infrastructure, and low<br>awareness.|
|Multidimensional<br>measures|A<br>combination<br>of<br>multiple indicators that<br>reflect different aspects<br>of energy deprivation|Nuanced and holistic,<br>it<br>captures<br>the<br>complexity<br>and<br>multidimensionality of<br>energy poverty.|Subjective choices on the<br>inclusion of indicators and<br>weighting require more<br>data.|
|Approaches that<br>use big data|Measures that use big<br>data, such as satellite,<br>mobile phones, social<br>media, etc.|Innovative, real-time,<br>and granular analysis|Abstracts from quality,<br>affordability,<br>and<br>reliability;<br>require<br>advanced<br>data<br>science<br>skills and tools; may raise<br>ethical and data privacy<br>concerns|

_Source: Own compilation based on literature (2023)._

# 3. Energy Poverty Measures in Bulgaria

**We next explore several energy poverty measures in Bulgaria.** We start by looking at purely expenditure-based measures. We then explore consensual and outcome-based approaches and the potential of big data sources. We mainly rely on data from the household budget survey (2021) and the EU-SILC (2020), the most recent data for this study.

## 3.1. Expenditure-based measures

**We start by exploring expenditure-based measures of energy poverty using survey data from 2021.** We explore the three expenditure-based approaches detailed previously and also analyze the sensitivity of these measures to varying some of the underlying parameters.

**For the relative measure, we start by defining energy spending shares as follows:** ES = EE II

, where E is the total energy spending category reported in the household budget survey (electricity, natural gas, town gas, liquified energy sources, liquid fuels, coal, other solid fuels, and heat energy). We divide it by total household expenditure and alternatively disposable household income and then compare both measures. Importantly, we rely on net income, the total income from all sources, 21
<!-- Start of picture text --> First two children girls<br>(a) 1972-1984 (N=312) (b) 1985–1994 (N=942) (c) 1995–2006 (N=842)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>First two children one boy and one girl<br>(d) 1972-1984 (N=666) (e) 1985–1994 (N=1,909) (f) 1995–2006 (N=1,763)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>85 85 85<br>80 80 80<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>30 30 30<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>85 85 85<br>80 80 80<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>30 30 30<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 10: Predicted probability of having a boy and probability of no birth yet from nine months after second birth for rural women with 8 or more years of education by quarter. Predictions based on age 24 at second birth. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

Relatively few women with eight or more years of education have three children and even fewer have four. This means that caution should be used in interpreting the results and in the 36
Both these alternative methods confirm that the program performs relatively well in avoiding leakage to ineligible households. They also confirm that there is considerable undercoverage of those in need — despite the programs’ stated aim of covering all eligible households — although the extent of this varies greatly according to our method of assessing who is eligible.

# _5.3 Targeting and poverty impacts across cities_

Anti-poverty programs such as DB rely on decentralized financing and implementation. Heterogeneity in outcomes across geographic areas is to be expected; there will, of course, be differences in local resources and administrative capabilities, but there will also be (less obvious) differences in the local political economy. Here we aim only to describe the differences in DB performance across municipalities,<sup>22</sup> and to use these differences to assess how well prevailing targeting measures perform in predicting impacts on poverty. Of course, if we know the impacts on poverty — which we agree to be the objective — then we don’t need the targeting measures. However, since these measures are widely used in assessing anti-poverty programs and in comparative work, it is of interest to test their value as indicators.

As can be seen from Table 5, there is considerable variation across municipalities in targeting performance. _SHARE_ varies from 31% to 98%; _CI_ varies from 0.64 to 0.93, _CGH_ varies from 2.8 to 18.8,<sup>23</sup> while _TD_ varies from 0.06 to 0.53. However, some cities do much better by some measures than others; as one would expect, _SHARE_ is positively correlated with _CI_ (r=0.50). _CI_ is also positively correlated with _CGH_ (r=0.34). However, _SHARE_ is negatively correlated with _CGH_ and _TD_ , though the correlations are not significant. (As expected, _TR_ and _SHARE_ are highly correlated (r=0.80) as are _TD_ and _TD_<sup>*</sup> (r=0.88).) _TD_ is highly correlated with

> 22 The Appendix gives more detail on the results by city.

> 23 All except one city (Kunming) have a _CGH_ higher than the best performing program, _Trabajar_ , surveyed by Coady et al. (2004).

22
fertility in HIV+ women (Kim Powers, personal communication). Family planning promotion in HIV+ women may reduce the numbers of them attending antenatal clinics. Second, ANC survey data represent only the geographical regions covered by participating antenatal sentinel sites. It is possible that those sites that were added in later years to the sentinel system are at an earlier HIV epidemic stage than those included early or represent lower level epidemics, if more highly affected areas were chosen first for surveillance. Finally, in sites having trouble reaching necessary sample size, or wanting better estimates for the incidence proxy, oversampling among 15-24 year olds has been recommended (WHO, UNAIDS, and CDC 2003). Any modifications in sampling/oversampling will bias the longitudinal HIV prevalence trend. The direction in which it would do so is difficult to predict and is largely dependent on compositional changes happening within ANC population sites, with regard to age, geographic residency, treatment coverage, fertility behaviors, etc.

# **2.1.1.2 Demographic and Health Surveys/AIDS Indicator Surveys**

Data from national population-based surveys, including DHS/AIS, have also been used to estimate HIV incidence and supplement prevalence data from antenatal clinics. These surveys are typically done by sampling and testing for HIV within households for which the residents have given their individual consent. Although they have the advantage of including both male and female HIV data, population-based surveys are more expensive and logistically difficult to carry out, and therefore, are performed less frequently. Additionally, these data can be biased by non-response (either by individual refusal to participate or household absence at time of survey). As voluntary counseling and testing services become increasingly available, more non-response bias from individuals already knowing their HIV status, including those previously testing positive for HIV, may be introduced into DHS/AIS surveys (Kim Powers, personal communication), bringing in considerable uncertainty around the estimates when non-response is high. However, preliminary work suggests that DHS response rates are not declining, despite potential increases in testing, and therefore it is unlikely that any potential bias in survey data is increasing (Mark McGovern, personal communication).

DHS/AIS prevalence data obtained across two household sero-surveys can be used to mathematically estimate incidence, also incorporating cohort mortality or survival after infection data into the equation (Hallett et al. 2010; Hallett et al. 2008). These methods for inferring incidence are limited by the number of sero-surveys conducted, as incidence can only be estimated for the inter-survey period. Assessing temporal incidence trends will be only possible when countries have completed at least three of these surveys. In the meantime, as we have done here, focusing on DHS/AIS prevalence trends within females 15-24 is another way of descriptively inferring incidence, as most infections within this group are likely to be recent.

13
**Table 1.1  International Use of Antidumping and the Global Antidumping Database**

||**Number of Antidumping**|**Number of Antidumping**|
|---|---|---|
|**Country**|**Investigations, 1995-2004**|**Measures Imposed, 1995-2004**|
|**User-Countries in the Global Ant**<br>|**idumping Database**<br>||
|Argentina<br>|192|139<br>|
|Australia|172|54|
|Brazil<br>|116<br>|62<br>|
|Canada|133|80|
|Colombia|23|11|
|European Union<br>|303|193|
|India|400|302|
|Mexico|79|69|
|New Zealand|47|14|
|Peru|55|34|
|South Africa|173|113|
|South Korea<br>|77<br>|43<br>|
|Turkey|89|77|
|United States|354|219|
|Venezuela|31|25|
|Subtotal|2244|1435|
|(share of total)|(84.8%)|(86.7%)|
|**User-Countries not yet in the Glo**|**bal Antidumping Database**||
|China (since 2001)|99|52|
|Egypt|38|30|
|Indonesia|60|23|
|Israel|27|15|
|Malaysia|31|18|
|Taiwan (since 2000)|8|2|
|Thailand|34|23|
|Other WTO Members|105|58|
|Subtotal|402|221|
|(share of total)|(15.2%)|(13.3%)|
|Total|2646|1656|

_Source_ : Data for the initiations and measures used in this table is taken from WTO (2005a,b).

Before turning to a discussion of the actual data, it is important to highlight a number of data collection hurdles affecting the structure of our approach as well as the end product. First, there is a 4
targeting. After distributing funds on the village level though, the localities must implement their own intra-village distribution; making this a hybrid approach of geographic targeting and possibly the same village-level lists of poor that are extant now. The reason is that with this methodology, estimates on a household level are probably too noisy to yield useful results.

A related note of concern must be raised on the issue of _elite capture_ . The presented gains from targeting are obtained assuming that whichever spatial unit is targeted, resources distributed within the unit are distributed uniformly. In reality, the power of the local elites is known to affect the way decentralized benefits reach the poor. For instance, Galasso and Ravallion (2005) show that the results of the Food for Education program in Bangladesh is better in communities with more land equality and they argue that this reflects greater elite capture when the poor are less powerful. Araujo et al., (2008) find that localities with more inequality (as measured by the expenditure share of the top 1%, 3%, 5%, 10% and 20% of the population) reduces the probability that latrine projects that are especially pro-poor are implemented in Ecuador, holding the level of poverty in the community constant. These findings may be especially worrying as we show in this paper that poverty is correlated with inequality in Vietnam. Also numerous studies show that that even at the lowest administrative levels the scope for political elite capture should not be assumed away<sup>9</sup> .

On the one hand, one may say that elite capture will challenge our findings as presented in Tables 2-3, but on the other hand it enforces the need to establish an objective framework for poverty targeting. Plus, as this phenomenon occurs at all levels, distributing social assistance from the commune- or village level down to poor households would still be an improvement from the current provincial level.

# **8. Conclusions**

This paper combines the 2009 Vietnam Population Census and the 2010 Vietnam Household Living Standard Survey to estimate poverty and inequality indexes for all the

> 9 See for example the World Bank Policy Research Report, “Localizing Development: Does Participation Work?” (World Bank, 2012).

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