rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_B. Investigate the socio-economic channels and contextual factors driving climate-related (im)mobility_**

A complementary and parallel solution to the _Adaptation_ module suggested above is the development of an _Intention-to-migrate (ITM)_ module on potential migrants to investigate the issues of immobility and climate-induced reductions in migratory flows. Such an _ITM_ module would draw from the available evidence on climate risk perception (Helbling et al., 2021; Koubi, Stoll and Spilker, 2016; Zander, Richerzhagen and Garnett, 2019) and, in order to investigate the potential of climatic hazards to trap people in immobility, its framing should also be inspired by the theoretical insights of the sound literature on asset-based and geographic or shock-driven poverty traps (Barrett and Carter, 2013; Carter and Barrett, 2006; Carter et al., 2008; Jalan and Ravallion, 2002; Letta et al., 2018). With _ITM_ information at hand, it would also be possible to fully unleash the potential of one of the main advantages household surveys hold over other data sources on migration, the possibility of assessing the role of transmission mechanisms and contextual factors that affect the magnitude, or even the direction, of the climatemigration link. The LSMS-ISA collection, given its multi-topic nature, can be particularly informative on these issues, because the analyst can fully exploit the wealth of information available about household demographic, economic, and geographic characteristics to investigate a broader range of issues involving the causal links between poverty, agriculture, climate and the (in)ability to migrate.

# **_C. Other possible data improvements in longitudinal surveys_**

**Migration data.** The two _Adaptation_ and _ITM_ modules proposed above imply a change of perspective to investigate immobility and transmission mechanisms. But we also call for a series of other data improvements in longitudinal household surveys such as those of the LSMS-ISA. These surveys, in fact, exhibit a clear potential given their intertemporal nature (Carletto and Gourlay, 2019), which also allows them to track over time
2014 UNHCR survey finds that rents are among the largest expenses for refugees, representing 50 percent of monthly refugee household expenditure (UNHCR, 2014).

13 Apart from camp sites, respondents across all qualitative study sites in both Lebanon and Jordan complained about being exploited by landlords who charged high rental prices for ill-maintained accommodations. They also worried about being evicted and said that they paid a large share of their earnings towards rent, sometimes forgoing food or going into debt. Some reported renting low-quality housing so that they could afford meals. First-hand accounts of the situation included the following: “When the landlord knows [that] we are Syrian, they increase the rent. The landlord tells us every time that he is spoiling us because he is only taking JD 200 as rent, as if he is renting me a castle.” [Male, Amman, Jordan]. “Housing conditions are very bad for the price. The plumbing is old, walls leak, ceilings leak, there is high humidity. If we tell the owner, he says we must make the repairs at our own expense.” [Female, Beirut, Lebanon].

8
for the EU-15 from being a net exporter to being a net importer of rapeseed oil and a substantial expansion of EU-15 rapeseed production. All world vegetable oil prices increase considerably. The expansion of bio-ethanol induces a dramatic reduction of EU soft wheat exports. The analysis also accounts for set-a-side land coming back into agricultural production and for the impact on the livestock sector. Gohin’s land supply is similar to that of Keeney and Hertel (2009), with land being allocated in two steps, first between forest, pasture, and crops, and then among crops. Gohin does not explicitly mention the values of his CET parameters.

<!-- Start of picture text --> Lwheat Lcoarse grains Loilseeds<br>σCET 3<br>L L L<br>pasture other field crops grains & oilseeds & protein<br>σCET 2<br>Lmisc Lrice Lfield crops &pasture<br>σ3 ≥ σ2 ≥ σ1<br>σCET 1<br>L _<br><!-- End of picture text --> Figure 5. Land allocation in Huang et al. (2004) Bouët et al. use the MIRAGE model of the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) to analyze the same policy and use a land decomposition also based on AEZ data. They show that EU mandates would increase EU ethanol production by 373 percent and EU sugar beet production by more than 15 percent by 2025. EU biodiesel production would increase under a mandate by 34 percent in 2025. To reach EU consumption mandated targets, the full removal of tariffs on EU imports of ethanol and biodiesel would have to be complemented by a domestic consumption subsidy. Only the trade liberalization scenario stimulates foreign production of sugar cane with Brazilian production increasing by 7.6 percent in 2025. These EU biofuel policies imply a significant reallocation of land in favor of sugar cane/sugar beet and oilseeds for biodiesel sectors, in Europe and/or in South America. EU land allocated
, literature investigating the role of land as collateral in credit markets or studying farmer reactions to the efforts of large-scale investors to consolidate land has generally focused on documented ownership, deriving from the land registration and the owner’s possession of official documents (Domeher and Abdulai, 2012; Zhang et al., 2020; Joel and Bergaly, 2020). Work by Hasanbasri et al. (2022) uses data from Ethiopia, Malawi, and Tanzania to illustrate the importance of measuring multiple dimensions of land tenure, as gender gaps can vary significantly by type of right. The following are commonly used concepts for the analysis of land rights, which may be used jointly or in isolation, depending on the objective of the analysis and the features of the land tenure context in which the analysis is being conducted: 7
The second approach of employing statistical modelling techniques to impute missing data can offer a promising alternative when data are scarce. Within the imputation approach, there are also two directions: one is imputation at the micro level, using household (or individual) data from household surveys, and the other is imputation at the global level using country data. We refer interested readers to Dang, Jolliffe, and Carletto (2019) for a more detailed review of the literature on micro imputation methods. As for macro imputation methods, Bonjour et al. (2013) applied mixed modeling techniques to impute estimates for solid fuel use for household cooking for 155 countries over the period 1980-2010 and obtained encouraging results. Their approach has also been employed to impute estimates for missing electrification rates in a recent joint global study by the UN Statistics Division, the World Health Organization, and the World Bank (International Energy Agency et al., 2018).

For a brief illustration, we adopt a modified version of this statistical model with some further refinements, and provide estimates of the electrification rate for several African countries in Figure 7.<sup>28</sup> The selected countries represent different levels of electrification rate and include Uganda (low electrification), Nigeria (medium electrification), and Tunisia (high electrification). While the imputed rates (solid line) do not perfectly coincide with the actual data points, they closely track the latter in all three cases.<sup>29</sup> Estimates for all countries in the African region as a whole are also encouragingly close to those based on the actual data.

Estimations results using both micro and macro imputation methods thus appear promising and could be applied to provide estimates for other indicators where data are unavailable. There

> 28 We offer more discussion on the technical details in Appendix 1.

> 29 The actual data points themselves are the best available estimates of electrification that come from different data sources including household surveys and population censuses, so some inconsistency may be expected; see
the least proportion of individuals classified as extreme poor identified as international extreme poor, followed by Bhutan, Maldives, Pakistan, Nepal, Bangladesh, and India. However, when we use the standardized consumption aggregates to do the same exercise, Bangladesh and India have virtually the same poverty rate, whereas the poverty rate is three percentage points higher in India when using the national aggregates. The international poverty rate for South Asia as a region also falls when using the standardized consumption aggregates, from 19.6 percent to 18.3 percent – a fall of 1.3 percentage points.

Figure 1 shows how standardization affects the distribution of per capita consumption in each of the South Asian countries. For India, standardization has a significant impact on the distribution of the consumption aggregates, with the distribution shifting to the right and away from the $1.90 poverty line. On the other hand, it has more minor impacts on the distribution of consumption aggregates in Bangladesh and Pakistan, shifting them very slightly to the left. This reinforces the observed decline in India’s poverty rates when using the standardized aggregates, and the small increases in poverty rates for Bangladesh and Pakistan.

Using the standardized national consumption aggregates significantly reduces the number of poor in India and the region. Table 12 presents the estimates of the number of international extreme poor in each country using the national consumption aggregates and the standardized consumption aggregates.

**Table 11. International extreme poverty rate in South Asia** **<u>using the standardized and national consumption aggregates</u>**

||**Year**|**Standardized**<br>**consumption**<br>**aggregates**|**National**<br>**consumption**<br>**aggregates**|**Difference**<br>**(1)- (2)**|
|---|---|---|
many in the years following 2015 and its effects on different proxies of social cohesion such as trust, perceived fairness, and attitudes towards immigrants. Similarly, they find no evidence of any effect, neither negative nor positive. In contrast, they identify an increased incidence of anti-immigrant violence in the short-term which was larger in areas with higher unemployment and greater support for right-wing parties. Such null effects of refugee migration on native attitudes in host communities are also identified by Zhou et al. (2021) for the case of Sudanese refugee immigration to Uganda.

# **4 Research Design and Data**

## **4.1 Data**

Our analysis relies on data from the following sources: _Encuesta Dirigida a la Población Venezolana que Reside en El País (ENPOVE)_ is a specialized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immigrant’s origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant’s experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent’s current location is identified down to the _centro poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.

_Encuesta Nacional de Hogares (ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational background, labor market conditions, crime victimization, and a module on respondent’s perceptions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of
Tax Evasion: Evidence from a Revealing Experimental Representative Survey of Indonesian Firms<sup>∗</sup> Christopher Hoy<sup>†</sup> Filip Jolevski<sup>‡</sup> Anthony Obeyesekere<sup>§</sup> Originally published in the Policy Research Working Paper Series on _July 2024._ This version is updated on _August 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Tax Evasion; Experiment; Firms; Public Finance, Compliance

**JEL Codes:** H25, H26, E26, D22, O17, O53, C83, C93

> ∗The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors are incredibly grateful for the support received throughout this study from Jorge Luis Rodriguez Meza, Habib Rab, Alexandre Laure, Rong Qian and Stephen Davenport. We also want to thank Revocatus Paul for his outstanding support with data analysis. The authors are also very appreciative of comments received from Lars Christian Moller, Emilia Skrok, Anne Brockmeyer, Jonathan David Timmis, Mahvish Shaukat, Khudadad Chattha, David C. Francis, Norman Loayza, Joshua Wimpey, Davide Mare, Attique Ur Rehman, and Nazım Tamko ̧c. In addition, we are very thankful for comments and suggestions provided by staff at the Indonesia Directorate General of Taxation, participants in the World Bank DECIG Seminar. The work was made possible by funding support from European Union, SECO and Canada under Indonesia PFM MDTF Phase III Program, together with funding from the World Bank Global Tax Program and the Gates Foundation.

> †Christopher Hoy (corresponding author, choy@worldbank.org) is an Economist with the Poverty and Equity Global Practice at the World Bank.

‡Filip Jolevski (fjolevski@worldbank.
0|3847.4|5581.0|
|**EAP**|0.5|1.3|8.6|95.5|281.8|
|**ECA**|5.2|16.3|11.3|7.2|10.0|
|**MENA**|0.5|0.5|0.6|1.0|1.9|
|**SHA**|1.2|1.7|2.0|2.4|2.5|
|**SA**|1.2|1.8|1.3|5.1|28.4|
|**LAC**|76.7|91.0|86.0|161.7|258.4|

_Source: Author’s calculations based on USPTO statistics._

**Note:** LAC: Latin America and the Caribbean; Scand.: Scandinavia, EAP: East Asia and the Pacific; ECA: Europe and Central Asia ; MENA: Middle East and Noth Africa (excluding Israel); SA: South Asia; SHA: Sub-Saharian Africa . All previous groups exclude high income countries..

Table 5: Product Innovation Rates by Region

|Region|Quality-Innovations|Variety-Innovations|
|---|---|---|
|Sub-Saharan Africa|41%|52%|
|East Asia and Pacifc|60%|39%|
|Europe & Central Asia|59%|43%|
|Latin America & Caribbean|33%|56%|
|Middle East and North Africa|36%|16%|

Note: This table shows the composition of innovations per region across two dimensions: quality and variety innovations 26
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ 36% of Vietnam’s electronics exports<sup>10</sup> in 2009 were comprised of local value added.<sup>11</sup> Vietnam ranks low relative to its ASEAN counterparts in this regard: next to last among the eight ASEAN countries for which TiVA data are available (See Figure 3). Thus, while Vietnam’s ICT hardware export growth in recent years has been impressive; a closer look suggests that it has depended heavily on imported components. In fact, the foreign content of Vietnam’s hardware exports has grown relative to domestic content since 1995 (See Figure 4). While TiVA data are only available since 2009, our field research suggests that local content remains a small portion of Vietnam’s electronics hardware exports today, and that TiVA estimates exaggerate local value added. The consensus among the respondents interviewed in Vietnam was that FDI has grown markedly since liberalization, but that linkages between foreign investors and local Vietnamese firms remain very weak.

**Figure 3. Electronics Domestic Value Added as a Percentage of Exports, 2009**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Percent<br><!-- End of picture text --> _Source:_ OECD-WTO TiVA Dataset _Note:_ Myanmar and Lao are not included in the TiVA dataset

# _Global Lead Firms in Vietnam_

A number of global lead firms are directly active in Vietnam, most prominently Samsung, LG, Panasonic, and Nokia (now Microsoft). Samsung is one of the largest foreign investors in Vietnam with $9 billion invested to date, and an additional $3 billion smartphone factory under development. Its existing investments include a smartphone production facility in the northern Bac Ninh province, a smartphone and tablet display
relationship between the WTP to avoid power outages and GDP per capita, but the WTP is consistently much larger than electricity tariffs (Figure 4).

In addition to the small number of studies, the wide range of results might come from fundamental differences in experience design and different frequency and duration of considered outages. Most of the papers – listed in Appendix (Table 4) – are applied to advanced economies where dependency on electricity is high and power outages are infrequent. In poorer countries, the cost of electricity outages will fall on those with access and relatively high dependence on electricity – for example those whose livelihood depends on it (Pasha & Saleem, 2013).

**Figure 4: Distribution of willingness to pay for avoiding power outages**

<!-- Start of picture text --> 1200<br>US, 1988<br>1000<br>800<br>600<br>Canada, 1989<br>400 Italy, 2005<br>Spain, 2013 Austria, 2013<br>200 Pakistan, 2004<br>US, 2004<br>Mexico, 1999<br>Turkey, 2016 Sweden, 2008<br>0<br>0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000<br>GDP per capita, ppp (USD)<br>Choice Experiment Macroeconomics Contingent valuation<br>(% of hourly GDP per capita)<br>Willingness to pay per hour of electricity unserved<br><!-- End of picture text --> _Source: authors based on World Bank data and estimates from the literature. A country-year is matched to the closest nonmissing value of GDP per capita._ The global cost of power outages for households cannot be accurately estimated. Assessments fall between 0.002 and 0.15% of GDP (in purchasing power parity) per year, which corresponds to a range of 2.3 to 190 billion in 2018 USD<sup>4</sup> .
, 1998), which is suited for dynamic panel data and accounts for some endogeneity in the explanatory variables through instrumentation. Our set of control variables comprises information on the level of trade openness, human capital (as proxied by the human capital indicator of Penn World Table), institutional quality (as proxied by the indicator of government effectiveness of the Worldwide Governance Indicators), lack of price stability, and government burden. More specifically, our dynamic panel regression equation is expressed by: where y denotes the real GDP per capita (in logs), X is a set of control and stock of infrastructure measures. When estimating the above equation, we face the potential problem of endogenous regressors. This affects, in principle, both the standard determinants of matrix X given that we can argue that these variables may be jointly determined. Indeed, this may be subject to reverse causality from labor productivity.

> 28 We restrict our sample to 31 countries due to data availability limitations on human capital index from Penn World Table.

> 29 On these concerns, see Blanchard and Giavazzi (2003) for the case of the European Union, and Easterly and Servén (2003) for that of Latin America.

> 30 Calderon (2009) uses 5 years averages as the main focus of that paper is to estimate long-run growth. This paper instead focuses on the medium term, namely the period 2019-2023 and requires estimates of annual elasticity of output which are obtained using annual data.

16
behavior (social capital) in loan recoveries involving group lending as argued by Manski (1993).<sup>3</sup> Those with the same loan-default preference are likely to join a particular group, in which case group liability essentially reflects individual liability, suggesting that a high repayment rate (e.g., the case of the Grameen Bank) is driven by unobserved individual and joint default-preference incentives. Estimating the dynamics of group liability in group lending requires controlling for this self-selection bias at both individual and group levels.

The Grameen Bank in Bangladesh, the world’s pioneer in promoting group-based lending, is an ideal case for studying the role of group, versus individual, default incentives in a group-based lending scheme. To address this central issue, this paper uses a unique data set of the weekly borrowing and repayment history of all borrowers in some 297 Grameen Bank groups over a 7-year period (since their inceptions). The data was recorded and kept by Grameen’s branch-level office but never released.<sup>4</sup> Group liability is defined as the cross-member effect of loan default rates, while individual liability is measured by an individual’s past record of loan default. Thus, when an individual loan amount or the amount repaid depends on the behavior of group members as well as the individual, the unobserved attributes of both individual and group-members are likely to influence the performance of group-based lending.

An individual fixed effects (FE) method that sweeps out group FE, as well the influence of area unobserved characteristics influencing credit demand and loan repayment, can correct for the endogeneity of certain individuals joining a group and certain groups

> 3 Karlan (2005) shows how experimental economics can be used to measure social capital and its role in loan repayment behavior. However, such an ideal experimental design is hardly possible in practice and certainly not possible within the context of Grameen Bank lending.

> 4 However, because of branch-level computerization program, detailed group level data are not anymore recorded
events (i.e., political events), and the continuous control takes place to smooth out changes in the sol that inevitably result from adverse changes in economic fundamentals. As in Mundaca and Oksendal (1998), the range of variation of the exchange rate is determined endogenously from the optimal stochastic intervention control problem.

We test our model of stochastic control empirically using intra-daily data. Even when the period within which the BCRP would intervene has been made public (between 11:00 and 13:30 everyday), forward-looking market participants still remain uncertain as to if or when interventions will take place. This uncertainty occurs because on a daily basis, the market does not know how much appreciation/depreciation on one side and volatility on the other side the BCRP is ready to tolerate. The market needs to assess the probability of intervention by the BCRP. Therefore, it should be not only interesting but also useful to analyze the effect of such uncertainty regarding the probability of intervening, and to compare it with the effect that past and public information on interventions may have on both the level and the volatility of the exchange rate itself. To carry out these tasks, our empirical model considers the unannounced future interventions as dichotomous, endogenous variables that both depend and affect the exchange rate. Modeling future interventions as endogenous is realistic because market participants make decisions on the exchange rate, taking into account their beliefs about possible central bank interventions in the foreign exchange markets. Finally, this study shows that the disturbances of the exchange rate mean equation are characterized not only by an autoregressive conditional heteroscedastic (ARCH) process but also by another source of heteroscedasticity, namely their correlation with the intervention variables. Accordingly, we allow expectations of central bank’s interventions enter as explanatory variables in the conditional mean and variance of the exchange rate.

5