rafmacalaba/datause-encoder-data huggingface dataset:
- IPCC 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: agricultural, forestry and other land use, National Greenhouse Gas Inventories Program, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). IGES, Japan.

- Macedo, I.C., et al. Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy 2008: 32(4).

- Menichetti E., and M. Otto. 2009. Energy balance and greenhouse gas emissions of biofuels from a life-cycle perspective. in R.W. Howarth and S. Bringezu (eds). Biofuels: Environmental Consequences and Interactions with Changing Land Use, Proceedings of the Scientific Committee on Problems of the Environment (SCOPE) International Biofuels Project Rapid Assessment, 22-25 September 2008, Gummersbach Germany, Pages 81-109.

- OECD. Biofuels Support Policies: An Economic Assessment. Organisation for Economic Cooperation and Development (OECD). Paris, 2008.

- Ogg, CW. Environmental challenges associated with corn ethanol production. Geopolitics of Energy 2008: 30(1):13-18.

- RFA. The Gallagher Review of the Indirect Effects of Biofuels Production. Renewable Fuels Agency (RFA). East Sussex, UK, 2008.

- Searchinger, R. Heimlich, R.A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, T. Yu. Use of US Croplands for Biofuels Increased Greenhouse Gases Through Land-use change, Science Express (Feb. 7, 2008)

- Timilsina, Govinda R. and Ashish Shrestha (2010). Biofuels: Markets, Targets and Impacts, World bank Policy Research Working Paper 5364, The World Bank, Washington, D.C.

- Timilsina, Govinda R., John C. Beghin, Dominique van der Mensbrugghe and Simon Mevel, 2010. The Impacts of Biofuel Targets on Land-Use Change and Food Supply: A Global CGE Assessment, Policy Research Working Paper 5513, The World Bank, Washington, DC.

- World Bank, 2009. Global Economic Prospects 2009: Commodities at the Crossroads, The World Bank, Washington, DC.

26
Corcos, G., D. Irac, G. Mion and T. Verdier (2013). “The Determinants of Intrafirm Trade: Evidence from French Firms.” The Review of Economics and Statistics 95 (3).

De Backer, K. and S. Miroudot (2013), “Mapping Global Value Chains”, OECD Trade Policy Papers, No. 159.

- Defever, F. and F. Toubal (2013). "Productivity, Relationship‐specific Inputs and the Sourcing Modes of Multinationals," Journal of Economic Behavior & Organization 94 (C).

- Dettmer, B. (2014), “International Service Transactions: Is Time a Trade Barrier in a Connected World?” International Economic Journal 28 (2).

- Hausman, R., J. Hwang and D. Rodrik (2007), “What You Export Matters”, Journal of Economic Growth, 12.

Hilberry, R. (2011), “Causes of International Production Fragmentation: Some Evidence”.

Hummels, D. and G. Schaur (2014), “Time as a Trade Barrier”, American Economic Review 103 (7).

IMF (2015), Regional Economic Outlook: Asia and Pacific.

Jabbour, L. (2012). "’Slicing the Value Chain’ Internationally: Empirical Evidence on the Offshoring Strategy by French Firms," The World Economy, 35 (11).

- Kowalski, P., J. Gonzalez, A. Ragoussis and C. Ugarte (2015), “Participation of Developing Countries in Global Value Chains: Implications for Trade and Trade‐related Policies,” OECD Trade Policy Paper, No. 179.

- Lederman, D., V. Pathikonda and L. Rojas (2012), “Do Eurasia’s Endowments Constrain Product and Market Diversification? Preliminary Findings”, mimeo.

- Nakazawa, E., N. Yamano, C. Webb (2014), “Determinants of Trade in Value‐Added: Market Size, Geography and Technological Gaps”, OECD, mimeo.

- OECD, WTO, and UNCTAD (2013), “Implications of Global Value Chains for Trade, Investment, Development and Jobs”. Prepared for G20 Leaders Summit.

- OECD, WTO and World Bank Group (2014), “Global Value Chains: Challenges, Opportunities and Implications for Policy”. Prepared for G20 Trade Ministers Meeting.

- OECD‐WTO (2015), “Aid for Trade at a Glance 2015: Reducing Trade Costs for Inclusive, Sustainable Growth”.

- Orefice, G. and N. Rocha (2014), “Deep Integration and Production Networks: An Empirical Analysis”, The World Economy 37 (1).

- Pathikonda, V. (2011), “New EU Member States’ Participation in Global Production Networks:  Recent Trends and Prospects for Future Growth”, mimeo.

22
<!-- Start of picture text --> os 2007<br>:Eu<br><!-- End of picture text --> <!-- Start of picture text --> Bu<br><!-- End of picture text --> <!-- Start of picture text --> on Py .<br>e .<br>o<br>-2 -1 0 4 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text --> <!-- Start of picture text --> on ° s<br>a .<br>o<br>-2 =1 oO 1 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text --> <!-- Start of picture text --> " 2008<br>ww<br>Eo<br>a=]<br><!-- End of picture text --> <!-- Start of picture text --> 2009<br>wD"<br>Eo<br>Dm<br><!-- End of picture text --> <!-- Start of picture text --> z<br>* *,<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>=<br><!-- End of picture text --> <!-- Start of picture text --> . . .<br>o<br>-2 -1 it} 1 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text --> <!-- Start of picture text --> .<br>ee<br>o<br>-2 =1 0 1 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text --> <!-- Start of picture text --> 2010<br>oa<br>§ se<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>& ou<br><!-- End of picture text --> <!-- Start of picture text --> go<br>a<br>5im .<br><!-- End of picture text --> <!-- Start of picture text --> 2 °<br>5<br>8 = .<br><!-- End of picture text --> <!-- Start of picture text --> 3 " cas = . . .<br>= peat° eat<br>-2 =1 0 1 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text --> <!-- Start of picture text --> 8 a . 2 = s<br>pea<br>-2 -1 0 1 2<br>Normalized distance around 10km threshold (in km)<br><!-- End of picture text -->
double the size of the second largest country. But most countries in the region are relatively small, including the 14 CFA countries, whose population totals 97 million.<sup>4</sup> In terms of total income, South Africa is the largest. Its share of the region's GDP is equal to that of all West and Central African countries combined. Income distribution in the region diverges widely, ranging from $510 (PPP) per capita in Ethiopia to $7,380 in South Africa and $9,310 in Mauritius (see table 1).

GDP growth was relatively high in many African economies in 1997. Twenty-two of the 39 countries for which GDP growth is reported in table 1 had an average annual growth rate of 5 percent or higher. Growth has slowed considerably since then, however, and prospects are for continued slow growth, given the severe global recession brought on by weak demand, and large stockpiles. Further, prices for the region's key commodities (see next section) are the lowest in 30 years, and the outlook for long-term real prices is not favorable. Most prices are expected to stagnate at their current lows until 2010, with severe consequences for growth rates in Sub-Saharan Africa.<sup>5</sup>

# _2.2. Activity effects through EU-African trade_

Some 43 percent of merchandise imports to Sub-Saharan Africa originate from the European Union, an amount equivalent to 0.6 percent of world merchandise trade. EU countries account for 24 percent of all merchandise exports from Sub-Saharan Africa, again equivalent to 0.6 percent of world merchandise trade (World Bank 1999b). For some countries the share is much larger- 40 percent for the CFA countries, one-quarter of it going to France (table 4).

EU merchandise imports from Sub-Saharan Africa grew nearly 6 percent during 1986-96, while EU exports to the region grew nearly 5 percent. These numbers are well below the 10 percent growth in world merchandise trade over the same period (World Bank 1999b), a reflection of the diminishing role of Sub-Saharan Africa in world trade over the last 40

> 3All Sub-Saharan Africa countries with more than 1 mill. inhabitants are included in the analysis.

> 4 CFA franc zone members are: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Republic of Congo, Cote d'Ivoire, Equatorial Guinea, Gabon, Guinea-Bissau, Mali, Niger, Senegal, and Togo. _Source:_ "Global Commodity Markets" World Bank quarterly publication, May 1999.

3
Table 5: Decomposed price shock and investment decisions

||(1)|(2)|
|---|---|---|
|VARIABLES|d_lninv|d_lninv|
|d_price1|8.713***|8.612**|
||(3.267)|(3.351)|
|d_price2|5.702|5.100|
||(14.63)|(14.11)|
|d_price1*d_price2|-14.19**|-13.30**|
||(6.296)|(6.372)|
|d_price1^2|-0.106|-0.117|
||(0.492)|(0.502)|
|d_price2^2|-14.80|-15.03|
||(12.39)|(12.39)|
|d_price1*Ln(asset)|-0.532***|-0.528***|
||(0.178)|(0.183)|
|d_price2*Ln(asset)|0.0302|0.0458|
||(0.799)|(0.768)|
|Ln(asset)|0.297***|0.291***|
||(0.0950)|(0.0956)|
|d_price1*Ln(land)|0.406*|0.443*|
||(0.238)|(0.246)|
|d_price2*Ln(land)|-0.368|-0.279|
||(0.556)|(0.510)|
|Ln(land)|0.151*|0.272***|
||(0.0852)|(0.0988)|
|Ln(land)^2||0.0542***|
|||(0.0167)|
|Average years of schooling|0.0147|0.0152|
||(0.0263)|(0.0262)|
|Household size|0.0765**|0.0686**|
||(0.0339)|(0.0343)|
|Average age|-0.00367|-0.00366|
||(0.0103)|(0.0103)|
|Constant|7.265***|7.360***|
||(1.554)|(1.554)|
|Observations|762|762|
|Sample|All|All|
|Provincial dummies included but|||
|not reported|YES|YES|
|R-squared|0.247|0.253|
|*** p<0.01, ** p<0.05, * p<0.1|||

Standard errors are heteroscedasticity robust and clustered at the village level, and are reported in the parentheses.

32
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# **2.3 Setting**

The 12 middle-income countries included in this study were selected based on three key criteria: the share of GDP that is spent on energy subsidies, the number of adults with internet access, and the availability of recent fiscal incidence analysis based on detailed household survey data. The starting point for the selection process was the complete set of low- and middle-countries that spent at least 1 percent of GDP on energy subsidies for consumers in the most recently available country-level data. Given that this is an online study with a large sample size, it was also necessary to restrict the set of countries to those with at least 5 million people aged 18 years or older with internet access (this threshold is based on previous experience collecting similar sample sizes online, such as Hoy [2022]). Finally, in order to include an information treatment about the degree of regressivity of energy subsidies, it was necessary to focus on countries where fiscal incidence analysis has been conducted using recent household survey data that broadly reflect the current distribution of energy subsidies in that country (this led to the elimination of several large countries, including India and Iran where such analysis was not available).<sup>7</sup> Collectively, the final 12 countries’ spending on fossil fuel subsidies was almost US$200 billion—around a quarter of all fossil fuel subsidies in middle-income countries in 2022.

The 12 countries fall into two groups based on the main energy source that is subsidized (see Table 1). In seven of the countries (Angola, Argentina, Bolivia, Ecuador, Indonesia, Kazakhstan, and Nigeria), fuel is the main energy source that is subsidized (IMF 2023; IEA 2023). In the remaining countries (Bangladesh, Egypt, Ghana, Pakistan, and Vietnam), electricity is the main energy source that is subsidized (IMF 2023; IEA 2023). While this distinction between groups of countries is an important one, there is notable variation within these groups across multiple dimensions, such as whether they are a net exporter or importer

> 7The study thus provides respondents with the best available insight into the distribution of energy subsidies in their own country, as opposed to providing generic information that energy subsidies tend to be regressive across countries. Information tailored to specific country circumstances was expected to be more likely to influence respondents’ preferences, and it was deemed important for ethical reasons to provide accurate information.

13
Perhaps unsurprisingly, in the past year, perceptions of improvements of public health services are not related to the 2011 levels of the use of government schools. More surprisingly, however, our measure as to whether citizens perceive that government health facilities have improved over the past year is also not related to the reported usage of these facilities in 2011. This discrepancy between self-reported usage and perceptions of quality can be interpreted in at least three ways. First, it is possible that because fiscal authority over health spending was one of the first functions devolved to local authorities, councilors might have devoted funds to improving access to local health facilities early in their tenure, so that by 2011 respondents would not report improvements during the previous year. Second, it is possible that whether or not citizens use government health facilities is quite separate from the quality of services they receive at these facilities. As current media attention to the Ebola outbreak has highlighted, access to government health facilities does not mean that these facilities will be adequately staffed or well stocked. Finally, it is possible that the low level of correlation between our measures of perceptions and usage suggests that one of these measures may be a more valid and reliable indicator of local public health provision than the other.

Each of these possible interpretations indicates to us the importance of measuring health facility usage and quality of services separately, allowing us to robustly test the council-level effects on different dimensions of service delivery. Further, we argue that the relatively low levels of correlation between our dependent variables strengthens our ability to test for the effects councilor/ chief interactions on different dimensions of service delivery. That is, the results we present below demonstrate how inter-elite relationships shape fairly independent measures of service delivery, rather than displaying similar effects across highly correlated dependent variables.

||**Use of government**<br>**health facilities (2011)**|**Perceptions of**<br>**government health**<br>**services (2011)***|**Use of government**<br>**schools (2011)**|
|---|---|---|---|
|**Use of government**<br>**health facilities (2011)**|1.00|||
|**Perceptions of**<br>**government health**<br>**services (2011)***|-0.041|1.00||
|**Use of government**<br>**schools (2011)**|0.1254|0.0316|1.00|

* Higher values indicate quality has gotten better over the previous year.

15
# **7. Conclusions**

We formulate and test hypotheses about the links between bank ownership type and relationships using data from an important developing nation. Specifically, we directly address hypotheses about 1) the kinds of firms that have their main banking relationship with foreign, state-owned, and private domestic banks, 2) the effects of these bank ownership types on whether firms have multiple banking relationships and the number of these relationships, and 3) whether firms diversify across these ownership types. Our analysis uses data from India, one of the largest and fastest growing developing nations, which also has a mix of all the major different bank ownership types. The Indian economy is representative as well of financial market imperfections that are prevalent in many developing countries.

Our data set is particularly informative on these issues because it has information on a large number of nonfinancial firms; their banking relationships and markets; and the banks themselves. The data set also distinguishes between two types of state-owned banks – SBI and nationalized banks – with different governance structures and history. The data set is for 2001, allowing a period of about 10 years for most of the effects of the liberalization of India’s banking sector to have taken effect.

We test the hypothesis that foreign banks may be most likely to serve as the main relationship bank for transparent firms (e.g., large, listed, foreign firms) and state-owned banks tend to provide relationships for firms they are mandated to serve (e.g., small firms with limited credit access, stateowned firms, rural firms). We also test hypotheses that firms with main relationships at foreign banks may be more likely to maintain multiple relationships, more relationships, and to diversify across ownership types by having at least one other banking relationship with another ownership type. We test the converse for firms with state-owned banks for their main relationship – that they are least likely to maintain multiple relationships, have many relationships, or diversify across bank ownership types.

These hypotheses are based primarily on extensions of current theories about banking relationships and multiple relationships. For instance, we build on the theory that firms may have multiple relationships to insure themselves against a premature withdrawal of services due to the financial fragility of their main bank. We broaden this concept to describe the fragility of the banking relationship itself. This is linked to bank ownership type based on arguments that relationships with foreign banks may be particularly fragile (most likely to leave the country) and relationships with state-owned banks 23
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OECD (2007). Investment in ICT. _OECD Factbook, SourceOECD database_ .

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- Van Ark, B., O’Mahony, M., and Timmer, M. P. (2008). The productivity gap between europe and the united states: trends and causes. _The Journal of Economic Perspectives_ , pages 25–44.

10
|**Variable Name**|**Description**|**Source**|
|---|---|---|
|Bank capital / assets|Bank capital to assets ratio %|World Development<br>Indicator (World Bank)|
|Bank Concentration|Assets of three largest banks as a share of assets of all commercial<br>banks.|Financial Structure<br>Database (World Bank)|
|Bank Deposit / GDP|Demand, time and saving deposits in deposit money banks as a share of<br>GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et +<br>Ft-1/P_et-1]}/[GDPt/P_at] where F is demand and time and saving<br>deposits, P_e is end-of period CPI, and P_a is average annual CPI|Financial Structure<br>Database (World Bank)|
|Bank liquid reserves / assets|Bank liquid reserves to bank assets ratio %|World Development<br>Indicator (World Bank)|
|Banking supervision|Measure of prudential regulations and supervision of the banking sector|Abiad, Detragiache and<br>Tressel 2009 (IMF)|
|Chinn-Ito Index of Financial<br>Openness|A measure of the degree of financial openness of a country where higher<br>value indicates greater de jure financial openness.|Chinn & Ito (September<br>2008)|
|Crisis Dummy|Dummy set to one if a country is experiencing a banking crisis|Laeven Banking Crisis<br>Database|
|Financial Reform Index|Measure of financial reform. Normalized from 0 to 1. (1 stands for fully<br>liberalized)|Abiad, Detragiache and<br>Tressel 2009 (IMF)|
|GDP per capita growth|GDP per capita growth annual %|World Development<br>Indicator (World Bank)|
|International capital liberalization|Measure of restrictions and regulations on international financial<br>transactions|Abiad, Detragiache and<br>Tressel 2009 (IMF)|
|Liquid Liabilities / GDP|Ratio of liquid liabilities to GDP, calculated using the following deflation<br>method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is liquid<br>liabilities, P_e is end-of period CPI, and P_a is average annual CPI|Financial Structure<br>Database (World Bank)|

34