rafmacalaba/data-use-mentions-extended huggingface dataset:
A second difference between the transition economies of the FSU and other developing economies is that the stigma of reporting that one is out of work is arguably greater, while the entitlement attitude that one deserves a pension or allowance is perhaps larger than in other country contexts. This is due to the legacy of the previous system, in which labor was perceived as the right and obligation of anyone who was able-bodied, but that those who had contributed previously to the labor market would be protected in oldage or during periods of temporary "disability" (e.g. pregnancy or illness). These attitudes are likely to evoke positive answers to survey questions such as "do you work?" or "do you have a job?" in conditions which might receive negative answers in other country contexts. Nonetheless, unemployment rates calculated from the HEIDE data are higher than both registered unemployment in the FSU and the rate of unemployment benefit receipts reported by HEIDE respondents, reflecting the extreme difficulty of qualifying for an unemployment benefit and its short duration, especially in Russia and Kyrgyz Republic.

A third particular aspect of the labor market in the transition economies of the FSU is that it is extremely in flux, as the private sector emerges, and the informal labor market offers as many opportunities at the top end of the scale as it does at the bottom. Given the historic legacy in Russia and the other FSU countries, where entrepreneurial behavior has always been regarded with extreme distrust, it is truly difficult to determine the extent of entrepreneurial business and earnings. As a result, it is quite possible that the number of respondents who report that they work in the private sector might be understated. It mnight be preferable for a person who has a state sector job "in name only" to maintain that legal affiliation while in essence running a full-time business on the side.

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that can conduct the computations on the fly, or a reliable internet connection for processing in the cloud, would be required. Enumerator training is another implementation aspect that would require adjustment as the targeting survey questions need to be ordered by their stepwise regression sequence rather than the standard of thematic grouping. A final IT-related issue is that the social registry information collected with a TrESt or early stopping approach would collect jagged optional question data, so that survey designers would need to include all variables required for analytical or monitoring purposes in the core questionnaire.

Although the simulations presented here suggest scope for reducing survey costs while potentially reducing exclusion errors, this paper is only deskbased proof of concept. Indonesia has already achieved good targeting outcomes through years of investment in improving targeting processes and implementation. Continued improvements in design could further strengthen targeting outcomes at no additional cost, but piloting would be advisable to verify that the method is practical, that the results hold in the field, and also to collect additional data. For Indonesia, a simple PMT trial that collects the survey metadata to estimate realistic survey times would provide key budgetary inputs. Similarly, information on travel times between households would be important to calibrate time savings, particularly in rural districts and island locations where shorter questionnaires may only yield negligible savings. Detailed cost estimation along the lines of Fujii and van der Weide [2020] on the cost-effectiveness of double sampling would be advisable if implementation were to be considered.

One way to improve accuracy may be to draw on alternative data sources, such as the exploration of internet and phone expenditure for Indonesia’s PMT by Pinxten [2021]. Such data can potentially support better classification, shorter questionnaires, or both. Similarly, alternative data preparation tailored to the early stopping approach may offer significant benefits in terms of accuracy or question numbers. On the other hand, the shortened questionnaire raises the risk of misreporting, as it concentrates both predictive power and enumerator attention on a few high-impact variables. A restriction to verifiable variables may mitigate this risk,
, DC: World Bank.

- Hallegatte, S., A. Vogt‐Schilb, M. Bangalore, and J. Rozenberg. 2017. Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development Series. Washington, DC: World Bank.

- Hoddinott, J. 2006. “Shocks and Their Consequences across and within Households in Rural Zimbabwe.” Journal of Development Studies 42: 301–21.

- Hertel, Thomas W., Marshall B. Burke, and David B. Lobell. 2010. “The Poverty Implications of Climate‐Induced Crop Yield Changes by 2030.” Global Environmental Change, 20th Anniversary Special Issue, 20 (4): 577–85. doi:10.1016/j.gloenvcha.2010.07.001.

- Ivanic, Maros, and Will Martin. 2008. “Implications of Higher Global Food Prices for Poverty in Low‐ Income countries1.” Agricultural Economics 39 (November): 405–16. doi:10.1111/j.1574‐ 0862.2008.00347.x.

- Ivanic, Maros, Will Martin, and Hassan Zaman. 2012. “Estimating the Short‐Run Poverty Impacts of the 2010–11 Surge in Food Prices.” World Development 40 (11): 2302–17. doi:10.1016/j.worlddev.2012.03.024.

Kousky, C. 2016. “Impacts of Natural Disasters on Children.” Future of Children 26(1): 73–92.

- Osorio‐Rodarte, Israel, Marcio Cruz, and Maurizio Bussolo. 2015. “GIDD Sectoral Wage Bill Database by Workers ́skill Level.”.

- Rose, E. 1999. “Consumption Smoothing and Excess Female Mortality in Rural India.” Review of Economics and statistics 81 (1): 41–49.

- Rozenberg, J. and Hallegatte, S., 2015. The impacts of climate change on poverty in
# **APPENDIX: VARIABLE DEFINITION AND DATA SOURCES**

This appendix provides a description of the variables and data sources. The political variables were constructed using the following sources: Keesing's Record of World Events (various issues), the Political Handbook of the World (1998), and Szajkowski (1994).

# **TIME VARYING VARIABLES:**

**_AID:_** foreign aid as a percentage of GNP. Source: World Bank, World Development Indicators Database, 1998.

**_COHESION:_** index of government cohesion assuming values from 0 to 2. It is constructed in the following vvay: 2 is assigned to single party governments in parliamentary systems or "not divided" governments in presidential systems; the value 1 is assigned to coalition govenments in parliamentary systems or "divided" governments in presidential systems; and 0 is assigned to minority or caretaker governments. Source: see above.

**_COHESIDEOL:_** interaction between **_COHESION_** and a dummy equal to zero for former Communist governments, and equal to zero otherwise.

**_EBRDINDEX:_** Index of economic liberalization constructed as the sum of four indices: large and small-scale privatization, enterprise restructuring, trade and foreign exchange system, and banking reform. Source: EBRD Transition Report, various issues.

**FREEDOM:** The sum of two seven-points indices for civil liberties and political rights published by Freedom House (previously, by R. Gastil). Since the two indices are highly correlated with each other (with a correlation coefficient is 0.95), they are here summed together to get **FREEDOM,** a variable which assumes values from 2 (no freedom) to 14 (complete freedom). Note that the values of the original Freedom House indices have been reversed. Source: Freedom House.

**_FREESTAB:_** interaction between **_FREEDOM_** and **_GOVCHANGE._** The variable is constructed
percent of Cambodia’s total exports in the early 1990s, but by 2003 it constituted 76.4 percent. Sri Lanka, already a seasoned apparel exporter in the mid-1990s, experienced a healthy but more moderate growth than Cambodia, with exports rising from $1.7 billion in 1995 to $3.0 billion in 2004 (figure 1). However, in the late 1990s export growth decelerated, and exports even declined in 2001 and 2002.<sup>7</sup> <!-- Start of picture text --> Figure 1 Global Apparel Exports from Cambodia and Sri Lanka, Volume and<br>Annual Growth<br>115 4,500<br>4,000<br>95<br>3,500<br>75 3,000<br>2,500<br>55<br>2,000<br>35 1,500<br>1,000<br>15<br>500<br>-5 0<br>1998 2001 2004 2005 2006 2007 2008<br>SL value Cam value SL growth Cam growth<br>growth, percent<br>total value, US$ millions<br><!-- End of picture text --> _Source:_ United Nations Commodity Trade Statistics Database (UN Comtrade). _Note:_ The growth rate reflects the change from the previous year.

Despite pessimistic expectations for the apparel sectors post-MFA, both countries continued increasing apparel exports, though growth slowed somewhat. Sri Lanka expected that exports would decrease by half and that 40 percent of firms would close in 2005 (Kelegama and Epaarachchi 2002). However, Sri Lankan apparel exports grew 6 percent annually on average, and their value increased by approximately $1 billion over 2005–08. In Cambodia immediately after the MFA removal, total apparel exports increased to $2.7 billion in 2005 and to $4.0 billion in 2008, a rise of almost 14
I I I − ~~(~~ (C IppllCCAAI IIII I , CII I TTppLLCCrr C III<sup>ss</sup> )/1000 ) (15) ∆CC = TTppLLCCrr C II UUIIppi<sup>ss</sup> ∗CCppIIUUIIpprr I pps rrppIIUUCC∗<sup>rrIIWWppIrr</sup> Where MMC I CCIIQQrrII I is the total cost differential associated with overstaffing in utility TT<sup>II,WWppI</sup> CCIIQQrrII I TT<sup>II,WWppI</sup> _s_ ; are the reported labor costs of utility II _s_ ; are the reported number of ppllCCAAI II employees in utility ∆CC _s_ ; _o_ is the efficient ratio of employees per 1,000 II customers; TTppLLCCrr ccCCI IIC<sup>II</sup> I I is the reported number of water service consumers of UUIIpps utility _s_ in IBNET database; and ppIIUUIIpprr ICppsI<sup>rr</sup> rrppIIUUCC I is the reported number of wastewater service consumers of utility CCIIQQrrII I TT<sup>II,WWppI</sup> _s_ in IBNET database.<sup>IIWWppIrr</sup> CCIIQQrrII I TT<sup>II,WWppI</sup> Based on this equation we estimate the cost and tariff differential associated with inefficient levels of employment for each utility in IBNET:

## Where TT

## **3.7.3 Full Tariff**

Once we have computed the cost differential associated with water losses and staffing levels, we calculate the full tariff for each utility in IBNET, adding these two elements to the efficient tariff computed earlier for accounting for inefficiencies: I (17) Where is the full tariff. TTFFCCl = TTEEi iicciiI II + TTllCCI + TTEEIIppllCCAAI II FFCCl As previously discussed, we allocate the tariff differential due to excessive losses to the water tariff, while TT As previously discussed, we allocate the tariff differential due to excessive losses to the water tariff, while the tariff differential due to overstaffing is distributed across
<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Informal Competition (Country level cell<br>average)|Proportion of firms in the country excluding<br>the firm in question that report competing<br>against informal firms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Initial Employment (logs)|Log of total number of permanent full-time<br>workers at the firm in the initial year<br>(beginning of the growth period which is 2<br>fiscal years ago for most countries and 3 fiscal<br>years ago for other countries).<br>Source:<br>Enterprise<br>Surveys.<br>www.enterprisesurveys.org|
|GDP per capita (lagged, logs)|Log of GDP per capita lagged by 2 years from<br>the fiscal year covered by the Enterprise. GDP<br>per capita values are in constant 2011<br>International Dollars and PPP adjusted. In<br>cases where the Enterprise Survey spanned<br>more than one calendar year, the GDP per|

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,587|2.840|0.405|0.274|0.294|1.426|0.599|0.729|0.674|
|_Uganda_|2005|7,199|2.257|0.471|0.373|0.457|1.328|0.612|0.717|0.807|

_Source:_ World Bank, based on microdata from RIGA/RuLIS database. _Note:_ MLD = mean log deviation.

a. Effective number of observations after removal of missing and nonpositive values.

b. Dollar-a-day money amount in 2011 purchasing power parity.

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18

|Country,<br>L<br>econoy, or<br>territory|ast nubtis<br>P<br>Date|hed census<br>opulation<br>(1000) *|<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless irdicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|British<br>Virgin<br>Islands|May80|12 F|UNPVSR<br>1/88 (official<br>est.)|--||
|Brunei|Aug 81|193 F|UNPVSR<br>4/87(official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|aulgaria|Dec 85|8948 F|UNPVSR<br>7/86 (official<br>est.)|Based on INED 10/87|Based on UNPVSR<br>4/87|
|Burkina<br>Faso|Dec 85|<br>7747 F|Bank projection<br>from census|Bankest.|U.N. 1988 revision (prelim.|
|Burma|Mar 83|35308 F|U.N. 1988 revision<br>(pretim.)|BasedonU.N.1988 revision<br>(prelim.)|Based on U.N. 1984<br>assessment|
|Burundi|Aug 79|4114 F|Bank projection from census|Bank assessment of 1965,<br>1970-71, and 1979 censuses<br>and 1979 postcensaL|Bank est.|
|||||demographic<br>survey||
|Cameroon|Apr 76|7090 F|Bank projection from census,<br>
econometric examination based on multi-decade panel datasets of developed and developing countries using program evaluation techniques.

A small body of this research (Cubbin & Stern, 2006; Nagayama, 2009; Sen et al., 2016; Urpelainen et al., 2018), like this study, attempts to address endogeneity issues using the instrumental variables (IVs) approach. None of the studies using IV approaches has examined climate-related outcomes of power sector reform. However, Mallawaarachchi et al. (2021) discuss how studying the climate-related outcomes of power sector reforms can help coordinate policy agendas with differing economic and environmental objectives.

Our most closely related study is Doumbia (2021), which analyzes the relationship between the degree of a power market's competitiveness and power sector outcomes in developing countries, including renewable energy penetration. However, unlike our study, Doumbia (2021) does not address endogeneity issues, and their analysis is limited to conditional correlations.

Several well-known challenges confound the quantitative assessment of the power sector and renewable energy policy impacts (Bacon, 2018). These include endogeneity resulting from omitted variables related to the country and regional characteristics and simultaneity and reverse causality of policy enactment and outcomes. Estimates are also prone to measurement errors resulting from a lack of accounting for the depth of reform and collinearity of policies.

We design the study to engage with each of these challenges systematically. We include time and country fixed effects to account for the country and temporal omitted variables and estimate instrumental variable (IV) regressions across six regional panels.

Our identification strategy for selecting appropriate IVs assumes that developing countries are more likely to implement renewable energy policies when they exhibit "closeness" to major donors that champion today's best practices in energy markets. The IVs 4
). A two-step procedure is adopted. Based on discrepancies in trade statistics (mirror statistics), the paper presents products or sectors in which customs fraud is deemed to be significant. A quantification exercise of customs losses is provided. Then, through the use of highly disaggregated customs data, high-risk operators (importers and brokers) are identified.

Despite the fact the methodology is straightforward, to our knowledge there is hardly any paper using this approach (at the importer/broker level) due to the fact that it is usually difficult to get access to such information. The paper demonstrates how useful such detailed customs data can be and should convince the Head of Customs and/or Ministers of Finance to give access to them to researchers, since they can be used for an operational use. For researchers, it enables to identify some fraud techniques and collusive practices.

The use of export data provided by exporting countries allows us to compute for each sector/product the mirror gap. The mirror gap is, for each product/sector, defined as the difference between export _X_ to Madagascar reported by the exporting country and import _M_ from the exporting country as reported by Madagascar customs. Export data are downloaded

> 2 This means that the same nomenclature is used between the exporting and the importing country, both declarations are made in the same currency, and the same incoterm is used.

2
Household survey data available for Brazil is a notable exception to this. Large scale annual household surveys contain detailed information on individual household member economic status (the Pesquisa Nacional por Amostra de Domicílios or PNAD). They permit some comparison of alternative informality definitions. Firstly individual informality can be examined at the level of employer compliance with state labor market regulation through the possession of a registered labor contract (signed labor card). Secondly informality can be examined at the level of individual social protection coverage, through membership of a social security institute. Finally informality can be examined, as it is in many studies for which information on contract registration and social protection coverage is non-existent, by addressing the scale of micro-entrepreneurship activity. In practice this means an examination of individual employment status (type of employment or self-employment) combined with information, if employed or an employer, on the sector and scale of the business activity.

A comparative descriptive investigation of the sets of individuals defined as informal according to these different definitions reveals some substantial similarities, such as in demographic and educational status. However, a multivariate (probit) analysis reveals that some characteristics are more strongly associated with one measure of informality than another. For example, educational attainment is far more strongly associated with social security institute membership, and with informality defined according to employment status and activity than it is with the measure of informality based on compliance with labor regulation. On the other hand social security institute membership appears to be less strongly associated with the demographic and informal status of others within the household.

5
designed an intervention which cleaned vacant lots, planted new grass and trees, and installed fencing. 541 vacant lots were randomly chosen from poor neighborhoods in the city, and then randomly assigned to treatment and control groups. After a 38-month period, they found that areas around the treated lots saw a statistically significant 36% drop in perceptions of crime, and a 76% increase in the use of these vacant lots for social activities. Overall crime dropped by 13% and gun violence by 29%. In this study, ethnography was used less to track the process of change, and more to understand community needs to design an effective intervention to address urban blight and associated gun violence and crime.

Blattman et al’s (2021) recent paper also studies drug-ridden neighbourhoods in the city of Medellin in Colombia which has long had a reputation of being at the centre of Colombia’s drug economy. The research team, which combined economists and ethnographers, spent four years conducting open-ended interviews with dozens of gang leaders, managers, and foot soldiers in thirty gangs asking questions about their 30 gangs on their organization, operations, and rule. They also interviewed several experts, police, prosecutors, and community leaders. Drawing on their field work they designed a quantitative survey on the services provided by drug gangs in neighborhoods, tax collection, extortion, and the extent to which these were perceived as legitimate by the community. They used a natural experiment where the city was reorganized into 16 wards called _communas_ in 1987 _._ Streets on either side of a _communa_ border were very similar, but for the three decades since the reorganization were located at different distances from the police and administrative headquarters of the neighboring _communas._ Quantitative data were collected from 7,000 respondents and 223 low- and middle-income neighborhoods. The qualitative and quantitative data provide revealing insights into the workings of the drug economy. Gangs did not provide public goods, the government does, but gangs provide excludable goods such as security services, informal contract enforcement,
This considers refugee numbers based on the UN Refugee Agency (UNHCR) populations of concern data up to March 2017 and builds in refugee returns over 2020‐2035.<sup>25</sup> There is a possibility though that the above numbers might still understate fatalities and migrants. The cumulative number of fatalities from the war is hard to ascertain. A source currently relied upon, the Syrian Observatory for Human Rights (SOHR) puts the death toll at 560,000 (civilians and combatants) over March 2011 – 10 December 2018 (with 367,965 documented victims). Netting off foreign combatants of 65,108, we would get a total of 494,892 Syrian fatalities.<sup>26</sup> This is higher than what the UN population statistics suggest in terms of conflict‐related deaths – 303,451, based on our calculations when we take the difference of average deaths between 2011‐2020 and 2001‐2010 (multiplied by eight years).

The UNHCR populations of concern data show that compared to 2016 total registered Syrian refugees and asylum‐seekers increased in 2017 by 746,811 (Graph 8(a)). While the equivalent 2018 data are not available, from the UNHCR Regional Refugee and Resilience Plan database specifically on refugees in selected neighboring countries ‐ Egypt, Iraq, Jordan, Lebanon, Turkey and North Africa,<sup>27</sup> we note that when compared to 2017, refugees had increased by 184,398 to 5,663,675 in 2018 (patterned bars in Graph 8(a), with the age and gender profile of these refugees shown in Graph 8(b)). Based on this, the UN population statistics may be understating refugee movement by about 900,000. But if we discount the 1 million refugees born in displacement/exile supposedly included in the UNCHR data, then the under‐representation of population “loss” in the UN population statistics due to the use of outdated UNHCR data