rafmacalaba/data-use-mentions-extended huggingface dataset:
have only recently repaid their first credit and may receive additional fuinds. This is a very critical issue for program viability and should be explored in more depth via a systematic survey of active and inactive groups. The program also has not yet explored why some groups rest. Thus, it is not clear to what extent groups disband versus are unable to obtain additional credits.

_Reasotns for EfvoItlion in lrogrctn_ **_Perftormclnce._** Program managers attribute P4K's recent increases in arrears and declinina, volume to the following issues It is possible that a reassignment of the program within the Ministry of Agriculture contributed to defaults because many borrower groups were accustomed to making repayments to specific a,,riculture extension workers. When these did not visit the groups for an extended period of time. some groups did not make other arrangements to deliver payments to a BRI outlet. A further hvpothesis is that arrears were exacerbated by BRI's policy of canceling the program in districts in which arrears ran higher than 10 percent. Under this policy. many groups with strong repayment records stopped paying when it became apparent that the program was to be discontinued in their area. BRI's policy of canceling credit provision in whole districts also accounts in large part for the contraction in real lending volume in 1995/96.

However, it is also possible that the program's basic structure has contributed to some of its problems. The program uses agriculture extension workers as credit agents. However, extension workers do not generally have a financial or banking background. nor are they under the control or supervision of the bank disbursing the loans. Furthermore, they have no direct incentive to investigate the creditworthiness of borrowing groups, or to encourage these groups to repay their loans in a timely manner. By acting as a go-between for banks and groups, extension agents reduce the transaction costs for both parties. However, they also act as a buffer between the two, inhibiting them from forming direct links. It is possible that few groups receive more than
Mogadishu, providing a baseline data set for monitoring poverty and contributing to other key statistical indicators. However, in addition to large inaccessible areas, the sample excluded nomadic population and households in insecure areas. Furthermore, the rural sampling frame had to be derived ad‐hoc with only limited representativeness. Wave 2 of the SHFS, implemented in December of 2017, significantly expanded coverage to urban and rural areas in central and southern Somalia and included the nomadic population for the first time, while a newly derived sampling frame enhanced overall representativeness.

The specific context of insecurity and lack of statistical infrastructure in Somalia posed a number of challenges for implementing a household survey and measuring poverty. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability‐based representative sample. Second, while some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. Third, poverty in completely inaccessible areas had to be estimated by other means. Finally, the non‐stationary nature of the nomadic population required special sampling strategies. This paper outlines how these challenges were overcome in wave 2 of the SHFS through methodological and technological adaptations in four areas: sampling strategy, survey design, fieldwork implementation, and poverty measurement. In line with the challenges outlined above, this paper contributes to several themes in the literature on poverty measurement and data collection in the context of conflict and fragility, involving hard‐to‐survey populations.

First, geospatial techniques and high‐resolution imagery were used in the SHFS to model the spatial population distribution, build a probability‐based population sampling frame, and generate enumeration areas in an effort to overcome the lack of a recent population census (section 2). The SHFS sampling strategy bears resemblance to the strategy proposed by Muñoz and Langeraar (2013), which relies on satellite imagery and grid
percent and 23.1 percent of the AROPE + population in Bulgaria and Romania, respectively, have simultaneously three or four deprivations, whereas only 10.4 percent in Croatia and 4.7 percent in Poland. Nearly 1 percent of the AROPE + individuals living in Bulgaria, Croatia, and Romania are deprived in all dimensions. <mark>The analysis of simultaneous deprivations is important because understanding the pattern of associations and overlaps across the core dimensions of multidimensional poverty is a key element for policymaking (Ferreira and Lugo, 2012).</mark>

**Table 8.** Overlapping of AROPE + dimensions by country <u>(2022)</u>

|**AROPE +**|**Bulgaria**|**Croatia**|**Poland**|**Romania**|
|---|---|---|---|---|
|Deprived in only 1 dimension|50.6|65.6|73.8|47.3|
|Deprived in only 2 dimensions|26.7|23.2|21.3|28.8|
|Deprived in only 3 dimensions|15.6|8.3|3.7|17.4|
|Deprived in only 4 dimensions|5.9|2.1|1.0|5.7|
|Deprived in only5 dimensions|1.2|0.8|0.1|0.9|

Note: Percentage of individuals that are AROPE + and are deprived in multiple dimensions (using survey weights). The different permutations are not presented due to the dimensionality issue. Source: Own estimates based on 2022 EU-SILC. We report the survey year, and not the income year.

**The incidence of our extended version of multidimensional poverty is substantially high among all types of vulnerable households, but some families are particularly more affected in some countries.*
and 11 provide an indication of the progress of land consolidation operations in Orissa since 1974 when the OCH & PFL Act was operationalised. These data suggest that land consolidation has so far been completed on only 17 percent of the total operated area in Orissa. Some 1 1 percent of this total was consolidated over the period 1995-98 at a cost of Rs. 626 million (Table 11), or around Rs. 6,000 per hectare. However, these official data must be interpreted with caution owing to wide variations in actual implementation across the state. Efforts to consolidate land holdings in western Orissa, in particular, appear to have unsuccessful (see section 4.2.4). While the process may be have been 'completed', in the sense that consolidation officers have processed the necessary paperwork and filed reports under the OCH & PFL Act, little change may actually have taken place on the ground as farmers frequently refuse to exchange plots or alter existing plot boundaries.

- 33 While some regard land consolidation to be a potentially important component of India's land reforms (e.g. Oldenburg 1990), others argue that land consolidation does not constitute land reform on the grounds that it usually attempts scrupulously to ensure that land distribution remains unchanged.

43
Disaggregated estimates of intangible capital (such as our industry-level estimates) are essential to such research. Here, we present evidence that our estimates of intangible investment are meaningful, in the sense that they are empirically related to ̳productivity‘ growth – first presenting the correlation with export growth with a larger set of sectors, and then presenting evidence with TFP but with a more restricted set of sectors.

Taking export growth as a proxy for traditional TFP growth, we show that intangible investment is significantly correlated with export growth in Brazilian manufacturing industries between 2000 and 2008. Specifically, our proxy for TFP growth is the industry level growth in the index of revealed comparative advantage (RCA). RCA of an industry i in year t is calculated as the share of industry i in Brazil‘s total exports in year t divided by the share of industry i in total world exports in year t. An increase in RCA of an industry during 2000-08 indicates Brazil‘s rising comparative advantage in that industry, which in turn suggests rising productivity in that industry. Figure 14 below shows that industry-level growth in RCA during 2000-08 is positively related to industry-level intangible investment during this period. This relationship is statistically significant.

# **4.2 Evidence from TFP patterns**

We rely on parametric estimates of traditional TFP from residuals of the regression of production values against labor and tangible capital based on a Cobb Douglas production function. The estimates are at a broad industry level, using 2-digit industrial data for 22 manufacturing sectors over the years 2000-08<sup>25</sup> . We present results for two different estimates of TFP, based on two different assumptions for calculating the values of the initial capital stock.<sup>26</sup> Table 2 reports results from regressing our intangible capital measure against both TFP estimates. Results are shown for models with and without industry fixed effects (which allow for unobserved sources of

> 25 We had to restrict the number of sectors to only 22 manufacturing sectors due to the available data
|---|---|---|---|---|
|RSE of mean nominal per capita expenditure|2.08%|2.08%|2.08%|2.08%|
|RSE of mean nominal total household expenditure|2.28%|2.28%|2.28%|2.28%|
|SE of mean poverty headcount ratio|0.83%|0.83%|0.83%|0.83%|

# **5. Robustness analysis**

All analysis above is based on the HIES 2010 data. As discussed earlier, the HIES 2010 sample was designed to provide reliable division-level poverty and welfare indicators for urban and rural areas, but not district-level indicators. Therefore, baseline parameters used to design the 29
fundamental explanation than shifting interests. This is that a great deal of the literature has used Foreign Direct Investment (FDI) data, enthusiasm for which has been dimmed by the increasing recognition of a point first made long ago: “FDI does not correspond directly to any measure of real investment [...] It is more accurately thought of as a measure of financial flows...”<sup>4</sup> Measured FDI includes, importantly, flows that for tax reasons (routing intra-group payments so as to take advantage of reduced withholding tax rates and other tax or treaty provisions), and perhaps for other reasons too, pass through some ‘conduit’ jurisdiction while generating real investment somewhere else. This means that the same underlying investment can be recorded multiple times as it passes through conduits, giving rise to a form of double counting. And such conduit flows cannot be unravelled from the FDI data, which typically report the location of affiliates’ immediate parents, not that of the ultimate parent company. The importance of these points jumps out from the data, in the existence of ‘investment hubs’ with inward investment clearly far in excess of levels that are plausible for domestic investment, given the size of the domestic economy.<sup>5</sup> Such investments, with no real links to the local economy, may account for around 40 percent of global FDI.<sup>6</sup> Relying on FDI data to infer tax effects on cross-border real investment is thus, at best, highly problematic.

There are of course analyses that do not use aggregate FDI data, with much attention coming to be focused on firm-level data (as for example in Liu (2020) and Millot et al. (2020)). While this has evident importance and appeal, results from aggregate FDI-based studies continue to shape views on tax-responsiveness; and untangling what FDI data do or do not say about tax effects has its own importance from the wider macroeconomic perspective of understanding
Brenton, Paul and Miriam Manchin. 2003. “Making EU Trade Agreements Work: The Role of Rules of Origin,” _The World Economy_ , 26, 755-769.

- Bouet, A., Y. Decreux, L. Fontagne, S. Jean and D. Laborde, 2004, “A consistent, advalorem equivalent measure of applied protection across the world: The MAcMapHS6 database,” CEPII discussion paper No 2004–22, December.

- Brown, Drusilla K. 1987, “General Equilibrium Effects of the US Generalized System of Preferences _,” Southern Economic Journal_ 54: 27-47.

- Candau, Fabien Lionel Fontagne and Sébastien Jean. 2004, “The utilization rate of preferences in the EU,” CEPII, mimeo.

- Carrière, C. and de Melo, J., 2004, 'Are Different Rules of Origin Equally Costly? Estimates from NAFTA, CEPR Discussion Paper No. 4437.

- Francois, J. and D. Roland-Holst, 1997, _Scale Economic and Imperfect Competition_ in Francois, J. and K. Reinert, eds., _Applied Methods for Trade Policy Analysis: A Handbook_ , Cambridge University Press: Cambridge.

- Francois, J. 2001, _THE NEXT WTO  ROUND: North-South stakes in new  market access negotiations_ , CIES: Adelaide and Tinbergen Institute:Rotterdam.

- Francois, J. and K. Reinert, 1997, _Applied Methods for Trade Policy Analysis: A Handbook_ , Cambridge University Press: Cambridge.

- Francois, J., D. Spinanger, and J. Woertz,  2005, “The impact of ATC quota elimination on LDC exports,” mimeo.

- Francois, J.F. and I. Wooton, 2001, “Trade and Competition in Shipping Services and the GATS,” _Review of International Economics_ , 9(2).

- Francois, J.F. and I. Wooton 2005, “Market Structure in Services and Market Access in Goods,” CEPR discussion paper.

- Hansen, B. E. 2000, ‘
for official poverty statistics while the NPS helps monitor welfare dynamics in the country – and they are always implemented separately and in different years. The HBS is implemented every 5 years, while the NPS is implemented more frequently. Historically, the HBS used a 30-day diary until 2017/18 when the institutions switched to a 14-day diary. The nature of a diary implementation means that the respondent is not restricted by an item list in the HBS, while in the NPS the respondent is limited to a list of both food and non-food items (though variations of smaller and larger lists have been utilized in the NPS). Both surveys are collected over a 12-month period, to capture seasonality.

# **3. Data and study design**

# **3.1. Data**

This paper uses data from the Tanzania Household Consumption Methodological study conducted in May-June 2022 by the National Bureau of Statistics (NBS) and Office of the Chief Government Statistician (OCGS) Zanzibar, with funding and technical assistance from the World Bank. The methodological study adopted a multi-stage sampling approach designed to provide estimates at the national level, as well as rural and urban stratification. The first stage involved the selection of 143 enumeration areas (EAs) spread across all regions of the United Republic of Tanzania (both Mainland and Zanzibar) using a probability proportional to size (PPS) approach. The selection ensured the inclusion of both urban and rural EAs in each region. Once the EAs were selected, a fresh household listing was conducted to get the total household composition in each of the EAs. Following the listing exercise, 15 households were selected using a systematic random sampling procedure for inclusion in the study, with 5 households assigned to each of the three arms of the study. In the end, a total of 2,119 households were included in the analysis. The final sample was 6
# constraints.<sup>13</sup>

For the annual dataset used in this regression, the event years are defined as the years of the onset of the oil price collapse from its pre-collapse peak (1985, 1990, 1997, 2000, 2008, and 2014). The results are robust to defining them based on the year in which oil prices bottomed out. For the baseline results, only the oil price collapses of 1985 and 2014 are considered supply-driven, the other results are considered demand-driven. The robustness of the results to this assumption is tested in the robustness section.

The regression sample includes 153 EMDEs for 1970-2018, of which 34 EMDEs are considered energy exporting (oil, gas, or coal), defined as in World Bank (2020b). Annual data on real GDP are available from the World Bank’ World Development Indicators.

# 3.3 Baseline results

The model estimates the response of EMDE output to the six oil price plunges before 2020 over the following five years. It distinguishes between demand-driven (1990-91, 1997-98, 2000-01, 2008-09) and supply-driven oil price plunges (1985-86, 2014-15). Table 3 presents the results.

Oil price collapses were associated with significant EMDE output losses up to five years after the collapse ( Table 3, first column ).<sup>14</sup> The output response in the first year was insignificant, reflecting the fact that all oil price collapses straddled the turn of a year. However, from the following year, when oil prices reached their trough, the effect becomes statistically significantly negative. Five years after the oil price collapse, EMDE output was still 2.7 percent below baseline.

These output losses associated with oil price collapses were broad-based, affecting both energy-exporting and other EMDEs to broadly similar degrees. To test for differential impacts on energy-exporting and other EMDEs, a dummy for energy exporter status and an interaction term between this dummy and the oil price collapse dummy are added ( Table 3 , last three columns ). The
(and in some cases, data on hate crimes) to explore how migrants’ presence affects attitudes and behaviors toward migrants and migration policies. Since little public opinion and granular election data is available for developing countries, and given that policy makers in democracies are generally more attuned to their constituents’ preferences, such studies have focused almost exclusively on high- and upper-middle-income countries.

With few exceptions (e.g., Vertier and Viskanic, 2020), these studies have found that migrants’ presence—usually measured as the share of migrants (or refugees) in an area—substantially increases support for anti-immigration policy positions in Germany (Schaub, Gereke and Baldassarri, 2021; Otto and Steinhardt, 2014), Italy (Barone et al., 2016), Switzerland (Brunner and Kuhn, 2018), France (Edo et al., 2019), Denmark (Dustmann, Vasiljeva and Piil Damm, 2019), Austria (Halla, Wagner and Zweimüller, 2017; Steinmayr, 2021), Greece (Hangartner et al., 2019), and Colombia (Rozo and Vargas, 2021). As mentioned above, some of the factors that drive these negative effects may be less relevant in low-income countries. We contribute to this literature by exploring the effect of refugees’ proximity on the migration policy preferences of host community populations in a low-income country.<sup>5</sup> Focused almost exclusively on the Global South, the second strand of research explores whether the presence of refugees is associated with a greater risk of conflict (Jacobsen, 2002). Earlier studies highlighted possible tensions with local citizens that are exacerbated by resource competition or ethnic rivalry (Salehyan and Gleditsch, 2006; Rüegger, 2017). Much of this scholarship recognizes that refugees are often victims of conflict (Onoma, 2013; Fisk, 2018; Böhmelt, Bove and Gleditsch, 2019; Savun and Gineste, 2019). Recent research suggests that conflict between host communities and refugees may be avoided if refugees’ presence attracts
# **Appendix B – The TIMES Model**

## **B1. Introduction**

The TIMES model is an energy sector optimization model built on the Reference Energy System (RES). The model is designed to minimize the total cost of the entire energy system to meet the projected end-use energy demand satisfying technical, resource, environmental and any other constraints. It selects an optimal combination of energy production, transformation, transportation and utilization technologies accounting for their investment, operation and maintenance and any other associated costs. It is capable to simulate a range of scenarios related to energy and environmental policies. The TIMES modeling system has a series of modules, such as demand module, resource module, production module, transformation module, utilization module. The demand module projects end-use energy demand using demand drivers, such as GDP and population growth rates, urbanization rate, and evolutions in energy utilization technologies (light bulbs, roof/wall/window insulations, cooking stoves, refrigerators, heating and cooling devices, etc.). The demand module is simplistic. The relationship between energy demands and corresponding drivers are established through regression analysis using historical data. The resource module provides information on energy extraction costs, such as costs of coal mining, natural gas drilling and production, crude oil drilling and production considering resource potential, costs of technologies used. It also includes production of electricity from primary sources (hydro, nuclear, wind, solar, geothermal). Power generation costs from various primary sources are derived from investment costs, economic lives of generation technologies, resource availability profiles (availability factors). The energy conversion module does the same when primary energy resources (e.g., coal, natural gas) are converted to another form of energy, such as electricity from coal and gas; refinery petroleum products from crude oil, processed gas from natural gas, charcoal from fuel wood, production of ethanol and biodiesel. It also includes many physical conversions of energy commodities such as raw coal washing and dressing. The
<br>_EFNX_is the external finance need of exports;_Log(Trade/GDP)_is log of exports and imports as a share of GDP;<br>_Log(Income)_is log of PPP-adjusted real per capita income from Penn World Tables;_French, German, Scandinavian, and_<br>_Socialist Legal Origin_dummies are as defined originally by La Porta et al. (1998)._Predicted EFNX_is the predicted external<br>finance need of exports constructed by first estimating sector-level gravity regressions, and then generating predicted export<br>shares for each country and sector. All of the variables are average values over 1970-99. Variable definitions and sources<br>described in detail in the text.|
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