rafmacalaba/datause-encoder-data huggingface dataset:
**Based on the final synthetic dataset, we overlay monetary with energy poverty indicators and investigate energy poverty rates by income quintiles.** These types of analyses are not possible without data fusion methods, given that reliable and detailed expenditure and income data are only generated separately in Bulgaria, like in most countries in the EU. By employing statistical matching techniques, we can generate valuable information on who the energy poor are. We find that more than one-third of the energy-poor individuals (using the low-income high-cost measure) are not considered at risk of poverty. As a result, efforts directed at offering financial assistance to individuals in poverty might not adequately reach this particular subgroup. Importantly, these findings vary significantly by the underlying definition of energy poverty.

**This paper makes an important contribution to the increasing body of literature exploring statistical**

**matching techniques.** Researchers and Statistical Offices have become increasingly interested in alternatives to record linkage to combine different data sources, and the number of papers in this area is growing. To the best of our knowledge, we are the first ones exploring the potential of statistical matching techniques in Bulgaria. We are also the first to apply data fusion methods to the area of energy poverty. Lastly, we make a methodological contribution by exploring several different imputation models and comparing their performance to each other.

# **Future research can take advantage of the generated dataset to study energy poverty in Bulgaria in**

**more detail.** Our generated dataset allows us to describe the energy poor in more detail. Based on the new dataset, it is possible to analyze if the energy poor have access to social protection programs, for example, or what could drive their energy poverty status. In addition, having energy expenditure information in the EU-SILC allows for microsimulations that study energy price increases or potential mitigation measures.

**To improve the possibilities for statistical matching between EU-SILC and HBS, policymakers could push for greater harmonization between both surveys and include expenditure-based questions in a subsample of the EU-SILC.** In line with previous recommendations at the EU level (see Serafino and Tokin, 2017), we recommend better harmonization of surveys. In addition, including a roster of expenditure patterns for a subsample of the EU-SILC would increase the reliability of imputation and

32
part of a household survey such as those of the LSMS-ISA collection, which are meant to be, by their very nature, multi-topic and nationally-representative, and not exclusively targeted to the study of migration. In this respect, the relatively small sample size of most LSMS-ISA surveys often makes them unsuitable for the study of migration, as the standard LSMS-ISA multi-stage cluster design is unlikely to sample a sufficiently large number of households with migrants (De Brauw and Carletto, 2012).

**_Respondents’ issues._** More broadly related to data collection, Lucas (2021) notes that collecting migration data is essentially limited to two approaches: asking individuals about their migration experiences or asking remaining household members about those who left, and both present limitations. The former approach, in fact, provides little information about the household that the migrant left; the latter assumes that the respondent knows the current whereabouts and activities of the migrated household members, and memory about the list of those who departed may prove selective.

In addition, as noted by Kirchberger (2022) in her recent review, even when panel household surveys aim to track respondents, household surveys can still suffer from high levels of attrition. Other general shortcomings are: i) questions about migrants need to be answered by a proxy, generally a family member, which may introduce many imperfections and substantial bias (Carletto et al., 2014); ii) the double-counting of migrants, especially those who can be claimed as members in other households’ rosters; iii) the difficulty in classifying the type of migration: temporary (short-term) migration and (longterm) permanent migration are usually distinguished by an arbitrary threshold or time criterion set by the analyst. Return migration, seasonal migration, and circular migration can also be difficult to distinguish from one another (De Brauw and Carletto, 2012).<sup>12</sup> Finally, specifically concerning climate-related hazards, it is unlikely that migration caused by a fastonset climatic disaster would be captured in an LSMS-ISA survey, given the localized nature of these types of events and unless the survey takes place soon after the shock (De Brauw and Carletto, 2012).

> 12 _Seasonal_ migrants are those who leave for a specified period of time each year and should be identified through questions about repeated, short migration spells. _Return_ migrants migrated at some time in the past and have returned to the country or household somewhat permanently. _Circular_ migrants are those who have returned but plan to leave again for a significant period of time, or repeatedly migrate for long spells.

25
|<br>* 4K.s~*1<br>.......|J!M"<br>ta ,R~or<br>.........|
|---|---|
|~~~~~~~~~~<br> <br>-----------|~~~~~~~~~~~~~~~~~m<br> <br>------------|
|**|_e~si~<br>tg U|n$e4<br>=n~t~tutkt<br>d<br>+<br>b<br>s<br>B.<br>.....|
||_ANbWN_<br>_.<br>PeRo|

Source: Intemational Finance Corporation, Leasing in -mriaMres Finance Corporatson, Washington, DC, 1996.

The World Bank and Intemational 29
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**Table 3:** Determinants of the World Bank’s Helpfulness at the Implementation Stage of the Policymaking Process

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|lnaid<br>~~d~~pl<br>~~d~~isb<br>~~p~~c<br>~~0~~<br>~~m~~<br>~~1~~p|-0.01<br>(0.04)|||||-0.00 <br>(0.02)|-0.06***<br>(0.02)|-0.04<br>(0.04)|||
|lnaid<br>~~i~~nv<br>~~d~~isb<br>~~p~~c<br>~~0~~<br>~~m~~<br>~~1~~p||-0.01||||0.02|0.01|-0.01|-0.01|-0.01|
|||(0.03)||||(0.01)|(0.04)|(0.04)|(0.03)|(0.03)|
|n<br>~~e~~sw<br>~~m~~<br>~~1~~p|||0.24**|||0.02|0.02|0.21**|0.22**|0.22**|
||||(0.10)|||(0.04)|(0.05)|(0.09)|(0.10)|(0.10)|
|dpl<br>~~p~~roj<br>~~n~~<br>~~m~~<br>~~1~~p||||0.10<br>(0.19)|||||0.04<br>(0.19)||
|pa<br>~~m~~<br>~~1~~p|||||0.01|||||0.01|
||||||(0.03)|||||(0.04)|
|lngdp<br>~~p~~c<br>~~1~~st|-9.01|-10.44*|-9.31|-8.32|-8.61|-0.07|-8.09**|-12.76**|-11.36*|-11.55*|
||(5.75)|(6.08)|(5.75)|(5.71)|(5.66)|(0.76)|(3.77)|(6.26)|(6.25)|(6.21)|
|lngdp<br>~~p~~c<br>~~1~~st<br>~~2~~|0.58|0.70*|0.59|0.53|0.55|-0.01|0.61**|0.84**|0.76*|0.77*|
||(0.37)|(0.40)|(0.37)|(0.37)|(0.37)|(0.05)|(0.25)|(0.41)|(0.40)|(0.40)|
|lnpop<br>~~1~~st|3.55**|2.81|4.43**|3.50*|3.61**|-0.08*|-1.28|3.79**|3.64*|3.70*|
||(1.80)|(1.87)|(1.89)|(1.87)|(1.84)|(0.04)|(1.08)|(1.89)|(1.96)|(1.92)|
|aid<br>~~g~~ni<br>~~1~~st|0.01|0.00|0.01|0.01|0.01|-0.00|-0.00|0.00|0.01|0.01|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|polity2<br>~~1~~st|0.06|0.07|0.05|0.07|0.07|-0.00|0.04|0.05|0.06|0.05|
||(0.05)|(0.06)|(0.05)|(0.05)|(0.05)|(0.01)|(0.03)|(0.06)|(0.06)|(0.06)|
|left<br>~~1~~st|-0.57|-0.58|-0.63*|-0.54|-0.54|-0.03|-0.25|-0.65*|-0.63|-0.61|
||(0.37)|(0.37)|(0.36)|(0.36)|(0.36)|(0.12)|(0.21)|(0.38)|(0.39)|(0.39)|
|Respondent-level controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Policy Cluster Pair FEs|Yes|Yes|Yes|Yes|Yes|No|No|Yes|Yes|Yes|
|Country FEs|No|No|No|No|No|No|Yes|No|No|No|
|Policy Cluster FEs|No|No|No|No|No|Yes|No|No|No|No|
|Observations|634|635|657|657|657|621|732|621|635|635|
|BIC|2944|2901|3011|3078|3046|2319|3036|2833|2900|2913|
|AIC|1916|1886|1957|1983|1973|2057|2406|1840|1881|1884|

Notes: All regressions use inverse-probability weights to account for variation in unit non-response rate. * _p <_ 0 _._ 1; ** _p <_ 0 _._ 05; *** _p <_ 0 _._ 01.

policy _implementation_ . Here again, the _n_ _~~e~~ sw_ _~~m 1~~ p_ results mirror those reported in Tables 1 and 2, which suggests that ESW products are not only influential at upstream stages of the policymaking process, but also helpful during downstream policy implementation.

# **Robustness Checks**

In this section, we undertake a battery of robustness checks and summarize the results reported in the Appendix. First, in Tables A2-A4, we alter the measurement of our treatment and (country-level) control variables by taking their mean values over the duration of a 23
# **Table A2: Alternative specifications of long-difference model**

_Panel A: Poverty_

|Dependent variable:<br>Poverty rate at $1.90|4-year<br>average|5-year<br>average|Adding<br>time-<br>invariant<br>covariates|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|∆Temperature|0.510***|0.601***|0.708***|0.266***|
||(0.037)|(0.040)|(0.054)|(0.077)|
|Temperature||||-0.045<br>(0.127)|
|Temperature squared||||0.011**<br>(0.005)|
|Temperature*∆Tempearture||||0.155<br>(0.115)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Subnational FE|No|No|No|No|
|Year FE|No|No|No|No|
|Number of countries|95|95|95|95|
|Observations|1,109|1,109|1,109|1,109|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Poverty rate is measured by the Subnational Poverty Headcount Ratio at $1.90 a day. Control variables in Column (3) are taken from Kalkuhl and Wenz (2020) which include cumulative oil gas, distance to coast, distance to river, and altitude. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 48
respondents. We did similar placebo tests using as treatment the first two days of the survey from Round 4 (Appendix S5). This reveals that in Round 4 there was no discontinuity in respondents’ evaluations of living conditions after two days of surveying.

# _Conditioning Effects of Information and Cognitive Sophistication_

So far we have documented a strong effect of the shock devaluation on subjective well-being. However, as mentioned earlier, there may be reason to expect that people with higher levels of information and cognitive sophistication display stronger and more immediate responses to the news of the devaluation. Specifically, individuals with more informed and sophisticated mental models of the economy may make more accurate predictions of the consequences of the devaluation and update their expectations about the future more rapidly. In Table 3 we examine whether the association between subjective well-being and the macroeconomic shock depends on respondents’ level of information and cognitive sophistication.

To operationalize information we construct a dummy variable where we treat informed respondents as those who report getting daily news from the radio, television, or newspapers (coded 1). News consumption must be on a daily basis to moderate the observed drop in subjective wellbeing already on the day following the devaluation. If respondents do not follow the news on a daily basis, we treat them as uninformed (coded 0). As a proxy for cognitive sophistication, we use respondents’ level of education (see Appendix S4 for details).

24
This work is organized as follows. Section 2 reviews the most recent empirical literature on the causal relationship between climate change and migration. This section also clarifies important conceptual distinctions, summarizes the key open research questions, and takes a closer look at Sub-Saharan Africa, the area where the LSMS-ISA survey program currently operates. Section 3 presents the empirical and methodological challenges and highlights the related data gaps. Section 4 takes a closer look at the LSMS-ISA data collection and discusses its main limitations and possible use for empirical work. Section 5 suggests potential improvements and add-on modules to be integrated into future LSMS-ISA surveys. Section 6 sets up an agenda for future work and concludes.

# **2. Review and current state of the literature**

## _2.1 A synthesis of recent empirical findings_

<mark>In this subsection, we carry out an updated review of the literature by focusing on the period 2010-2022.</mark><sup>3</sup> <mark>Overall, even the most recent literature still provides mixed and inconclusive evidence about not just the magnitude, but even the sign of the climate-migration relationship. Heterogeneity, depending on several context-specific features including</mark> _<mark>, inter alia</mark>_ <u><mark>,</mark></u> <mark>the type and frequency of climatic shocks, peoples’ resources and adaptation strategies, seems to be an irreducible aspect of this delicate matter, that rules out blanket generalizations on the nexus between climate change and (im)mobility. Such complexity calls for caution in issuing predictions that climate change will force tens of millions of people to move within and/or out of their own country (Rigaud et al., 2018; Clement et al., 2021) and to pay more attention to the potential of climate change in preventing voluntary migration and trapping more vulnerable and poorer populations in immobility.</mark> <mark>By analyzing this flourishing strand of the literature, we flesh out some thematic issues that we see as pivotal for the subsequent analysis of the empirical issues and data gaps. S</mark> pecifically, we look at recent insights into five key facets of the climate–migration relationship as they have emerged in the literature: slow- vs fast-onset events (United Nations Framework Convention on Climate Change, 2012; BohraMishra, Oppenheimer, and Hsiang, 2014); direct and indirect links (Bardsley and Hugo, 2010); internal

> 3 Our survey is a data-oriented synthesis of selected recent findings relevant for the study of the potential of household survey data. For general and more comprehensive reviews and meta-analyses of the literature on the climate-migration relationship, we recommend readers to consult Cattaneo et al. (2019), Kaczan and <mark>Orgill-Meyer (2020), and Hoffmann et al. (2020).</mark>

4
55 Table 18: Crime and Inequality between Racial Groups using OLS

||Propert|yCrimes|Violent|Crimes|
|---|---|---|---|---|
||Residential<br>Burglary|VehicleTheft|Aggravated<br>Assault|Rape|
|_Within-Racial_GroupInequalityinPolice<br>Station|0.76<br>(0.23)**|1.87<br>(0.34)**|1.19<br>(0.18)**|1.48<br>(0.22)**|
|Between-RacialGroupInequalityinPolice|0.14|-0.01|0.09|0.05|
|<br>Station|(0.01)**|(0.02)|(0.01)**|(0.01)**|
|MeanExpenditureinPoliceStation|(079)**|(01165)**|(0.06)|(0.072|
|Unemployment in CriminalCatchmentArea|(0.09)|(0.13)|(0.07)|-0.04|
|RichestJurisdictionin CriminalCatchment|0.25|0.19|0.10|-0.05|
|Area|(0.11)*|(0.16)|(0.09)|(0.10)|
|ProvinceDummies|YES|YES|YES|YES|
|Constant|-9.62<br>(0.64)**|-16.31<br>(0.92)**|-2.63<br>(0.50)**|-5.33<br>(0.59)**|
|Adjusted-R2|0.48|0.52|0.22|0.14|
|Observations|1064|1064|1064|1064|

Standard errors in parentheses. * significant at 5%; ****** significant at 1%. Dependent variable is the log per capita crime rate. All explanatory variables are in logs. Crime rates equal to zero were replaced with the smallest value for that crime.
However, despite improved access to infrastructure financing in the late 2010s, bolstered by increased private sector participation, substantial infrastructure financing gaps remain (ICA 2018). The pandemic has widened these gaps further, while rising global fiscal pressures have seen multilateral and bilateral lending to SSA decline. Lending from China has also weakened substantially on growing concerns about mounting public debt and increasing credit risks in SSA.

Across the region, investments are needed to raise the quality of education and skills, improve the health of populations, and expand access to basic public services, notably sanitation. Despite recent progress, SSA is behind other regions in human capital accumulation, partly because of insufficient investment spending on education and health (Figure 18.C.D).

Finally, the COVID-19 pandemic has illustrated the importance of social safety nets as an effective tool to respond to crises. Investments in social protection could improve economic resilience, reduce poverty, and decrease income inequality across the region. Many SSA governments have achieved some progress in building more responsive, efficient, and inclusive social safety nets. However, population coverage remains low, partly because of the high prevalence of informality, leaving many vulnerable populations exposed to income and consumptions shocks, such as, for example, during the recent surge in food and fuel prices.

# 10.4 Regional policy priorities

The COVID-19 pandemic and recent deterioration in the growth outlook for many SSA economies have created formidable challenges to the aim of strengthening the growth of investment, and particularly to the financing of infrastructure investment, in the region. In 2020, many countries diverted already limited public resources from infrastructure projects to emergency spending on health and support for demand. Lockdowns, travel restrictions, supply chain disruptions, and higher input costs resulted in delays in project preparation and implementation. Since 2021, tightening global financing conditions and investment rating downgrades have raised borrowing costs and complicated access to international financial markets. As a result, funding commitments for infrastructure investment in SSA, after exceeding $100 billion in 2018 for the first time, have declined, leaving many untapped opportunities, including in renewable energy, climate resilience, digitalization, and agriculture, as well as other areas that can support private sector development.

On a positive note, innovative financing solutions for infrastructure investment that mitigate key risk factors have been spreading rapidly in SSA. Tools such as blended finance, co-financing between private investors and development finance institutions, public-private partnerships, and climate finance instruments are being deployed in countries across the region (AfDB 2022).

Nevertheless, financing investment projects remains challenging. Private investment has become more significant in a broad range of countries, albeit mainly in ICT. Despite the
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**Table 6 – Albania: Composition of debt**

As of end December 2016, millions EUR

||Amount|Percent|
|---|---|---|
|Domestic Debt|3,884|51.0|
|External Debt|3,731|49.0|
|Total|7,615|100.0|

Its foreign exchange reserves cover approximately 80 percent of external debt (FX debt-toreserves ratio of 1.23, see Table 7). While the amount of USD-denominated debt is roughly half that of USD reserves (ratio 0.53), Albania’s EUR-denominated debt is 1.15 times greater than its EUR reserves.

**<mark>Table 7 – Albania: Composition of Foreign Currency Debt and Reserves</mark>** <mark>as of end December 2016, millions EUR</mark>

||USD|EUR|JPY|GBP|SDR|Other|Total|
|---|---|---|---|---|---|---|---|
|<br>EUR mil. eq.|662|1,848|-|83|-|451|3,045|
|Reserves<br>Percent of total|21.7|60.7|-|2.7|-|14.8|100.0|
|Percent of GDP|5.9|16.4|-|0.7|-|4.0|26.9|
|EUR mil. eq.|348|2,124|74|-|998|188|3,731|
|External Debt<br>Percent of total|9.3|56.9|2.0|-|26.<br>7|5.0|100.0|
|Percent of GDP|3.1|18.8|0.7|-|8.8|1.7|33.0|
|Concentration<br>Ratio FX debt/<br>reserves|0.53|1.15|n/a|n/a|n/a|0.42|1.23|

Source: General Public Debt Directorate and Bank of Albania _Applied FX Rate: end December 2016 135ALL/EUR_ In terms of composition, shares of EUR and USD assets and liabilities are quite closely matched, particularly after breaking out SDR<sup>8</sup> into its component currencies (post break out USD is 22 percent of reserves and 21 percent of external debt; EUR is 60 percent of reserves and 65 percent of external debt, see Figure 6).

> 8 The value of the SDR is based on a basket of five major currencies – the US dollar, the euro, the Chinese renminbi, the Japanese yen, and the British pound sterling. The current weights are 41.7 percent for USD, 30.9 percent for EUR, 10.9 percent for RMB, 8.3 percent for JPY, and 8.1 percent for GBP.

17