rafmacalaba/datause-encoder-data huggingface dataset:
The main aim of the SPI exercise is a cross-classification of expenditures in which, for each infrastructure function or subsector (e.g. power generation), expenditures can be disentangled by their economic use (e.g. operations or investment). Standardized infrastructure expenditure data that are comparable across countries permit tracking over time while benchmarking trends and levels of infrastructure government outlays.

Template 10 consolidates the core of the budgetary data of the SPI exercise and follows definitions and classification of expenditures as presented in sections 2 and 3, largely based on the GFSM 2001. Reporting of spending is done using a cash-based approach.<sup>9</sup> Filling template 10 is a labor-intensive process that involves three steps (i) code mapping the budget, (ii) extraction of budget lines from budget documents, and (iii) actual consolidation of data in template 10 .

# **_Code mapping the budget_**

Reporting infrastructure expenditures using standardized functional and economic classifications involves a remapping of the country-specific budget classification to the GFSM 2001 format.

Code mapping has two components: functional code mapping and economic code mapping. In no other phase of the infrastructure data collection process do the data collector and the relevant technical personnel need to work so closely together as during the code mapping. The Ministry of Finance is usually the most relevant partner for the code-mapping exercise.

_Functional code mapping_ . The prevalent functional coding system needs to be identified as a starting point. Provided the country uses the GFSM 2001, the government expenditure data can be used directly; however, a functional code mapping exercise should be undertaken when the country is not utilizing GFSM 2001 (for example, when the country utilizes GFS86, United Nations 93, or any other system) or for reported years that precede the GFMS 2001 reporting. By way of example, table 3.5 presents the functional code mapping for Kenya, in which ―votes‖ of the Kenyan budget system were mapped to the GFSM 2001‘s COFOG.

> 9 In the GFSM 2001 framework, transactions should be recorded on an accrual basis (flows are recorded at the time economic value is created, transformed, exchanged, transferred, or extinguished) in contrast to a cash basis (flows are recorded when the money is received, which means that nonmonetary transaction might not be fully integrated in the accounting system). It suffices for this work to use the GFSM 2001 reporting. Even for countries that have adopted the GFMS 2001, the GFMS 2001 framework should be used exclusively from the transaction reporting rather than the transaction recording viewpoint.

42
# **Development of Biofuels in China: Technologies, Economics and Policies**

# **1. Introduction**

The total energy consumption of China surpassed 3.25 billion tons of coal equivalent (tce) in 2010, 37.7% higher than the 2005 level (NBS, 2011). This rapid increase in demand has placed significant pressure on energy supplies in China, especially that of oil. From 2002 to 2010, China’s apparent oil consumption increased by 7.8% per year from 249 million tonnes to 455 million tonnes, with the overseas oil dependence ratio increasing to 55% in 2010 (CNPC Research Institute of Economics and Technology, 2011). It is projected that the domestic oil demand will increase to 650 `±` 50 million tonnes by 2030 and 750 `±` 50 million tonnes by 2050 (RGCMLEDS, 2011a). Since the domestic production of crude oil production will, most likely, stabilize at around 200 million tonnes by 2020 (Tong, et al, 2009), the gap between domestic supply and demand provides development opportunities for alternative fuels, including biofuels.

China promulgated the Medium and Long-Term Development Plan for Renewable Energy in 2007, which included sub-targets of 2010 and 2020, broken down into various renewable energy technologies (Table 1).

**Table 1:Renewable energy targets in the medium and long-term planning**

|Target|Unit|2010 Target|2020 Target|
|---|---|---|---|
|Percentage<br>of<br>renewable<br>in<br>energy<br>consumption|%|10|15|
|Sub-targets||||
|Hydro power installation capacity|Million kilowatts|190|300|
|Biomass power installation capacity|Million kilowatts|5.5|30|
|Biomass briquette consumption|Million tonnes|1|50|
|Biogas for heat in rural areas|billion m<sup>3</sup>|15<sup>a</sup>|30<sup>a</sup>|
|Non-grain fuel ethanol consumption|Million tonnes|2|10|
|Biodiesel consumption|Million tonnes|0.2|2|
|Wind power installation capacity|Million kilowatts|5|30|
|Solar PV power installation capacity|Million kilowatts|0.3|1.8|
|Solar water heater installation capacity|Million m<sup>2</sup>|150|300|

Note: The 2020 targets might increase to 500 GW (300 GW of hydropower, 150 GW of wind power, 30 GW of biomass power, and 20 GW of solar PV) from 362 GW mentioned in Table 1 (Martinot and Junfeng, 2010).

In 2010, the installed capacity of hydro power (213 million kilowatts), wind power (44 million kilowatts), biomass power (6.7 million kilowatts), solar PV power (0.8 million kilowatts) and solar water heater (168 million square meters), and the consumption of biomass briquette (2.5 million tonnes) have already surpassed the corresponding targets for 2010 (see Fig.1). And the consumption of biogas (14.26 billion m<sup>3</sup> ) in rural area was almost achieving its targets for 2010. However, the only exception is the fuel ethanol. The realized fuel ethanol production in 2010 was only 1.86 million tonnes with only 0.37 million tonnes 2
who are primarily interested in the estimation outcomes can skip this section and go to Section 3 directly.

# **2.1 Conceptual Model**

Our conceptual model is built on the conventional IO table, which includes information on sales of intermediate goods and services by one industry to another in the domestic economy. By construction, summing up entries horizontally across each row and vertically across each column will both give the total gross output of an industry. The vertical summation is analogous to the _cost_ approach of measuring a country’s gross output, which decomposes gross output into different types of intermediate and primary factor inputs. The horizontal summation is analogous to the _sales_ approach of measuring a country’s gross output, which decomposes an industry’s gross output into its various domestic usages and exports. To study the intra-national trade between different types of firms based on their ownership and size, we first split the non-competitive IO table with 42 industries from China’s National Statistics Bureau (NBS hereafter) into 6 sub-accounts.<sup>10</sup> The 6 sub-accounts are constructed based on 3 ownership types – SOEs, FIEs, and Others (i.e., non-FIE private), and 2 sizes – large and small-and-medium. Thus, there are altogether 252 groups (42 industries x 3 ownership types x 2 sizes). To estimate the volume of domestic transactions between each pair of firm groups, there will be 252 x 252 (including the within-group transactions between different firms) unknowns to estimate. See Fig. 1 for an illustration of the extended IO table.

In the IO table, _Z, Y, E, X,_ and _M_ represent, respectively, intermediate inputs, domestic final demand, exports, total output, and imports. We use a two-alphabet superscript to denote one of the 6 firm groups. The first alphabet denotes ownership type (S, F, or O) while the second subscript denotes size (L or S). A combination of a size and an ownership type gives us a firm group, _g_ . Specifically, _g_ can be _SL_ , _SS_ , _FL_ , _FS_ , _OL_ , or _OS_ , which represent Large SOE, Small SOE, Large FIE, Small FIE, Large Others, and Small Others, respectively. Subscripts _i_ and _j_ are for supplying and buying product categories (42 of them), which we will mostly refer to as sectors from now on.

Fig. 1 shows our extended IO table with 6 firm types. The last two rows report value added and the column sum of gross output, respectively. The last three columns are respectively domestic final use, exports, and total gross output, which is equal to the row sum by construction (i.e., the IO balance

> 10 The non-competitive IO table assumes that imported and domestic products are not substitutable, in contrast to the standard IO table that assumes perfect substitutability between imported and domestic products. When competitive IO tables are used, only one set IO coefficients are needed. The underlying Leontief or linear production functions assumed in either approach have their obvious drawbacks, but we consider our approach, which permits different IO coefficients on imported and domestic inputs across sector-pairs, to be more suitable for the purpose of our study.

7
The process of registering in Belo Horizonte still requires more steps and complications than in a number of other countries that have pursued entry reforms. Moreover, in addition to facing taxes, firms which do register face a relatively large cost in terms of the need to hire an accountant. Faced with these costs of _being_ formal, it appears few informal firms want to formalize unless they are forced to do so by enforcement. We are unable to measure whether firms benefit from being forced to formalize, since the number of firms induced to formalize is too small, and our follow-up survey also suffered from high item non-response on sales and profits questions. But evidence from other countries (McKenzie and Sakho, 2010; de Mel et al, 2012) suggests that while some informal firms benefit from formalizing, the majority appear not to. Being informal is thus likely to be privately optimal for many firms.

This suggests three directions for government policy. The first is to re-consider where it is desirable even to try and attempt to bring these firms into the formal sector. It may not make much sense for the smallest firms, but given the limited tax base and that firm owners with revenues in the range which qualifies for SIMPLES are likely to be at least in the middle of the income distribution, there may be a public benefit to formalizing these firms even if there is no private benefit. Our results suggest the fiscal benefits of bringing service firms of the average size in our study into the formal sector would outweigh the costs of the inspections needed to make this happen. The second avenue for policy is then to simplify further the ease of formalizing, and more importantly, to revisit the need for an accountant which dramatically increases the cost of being formal. Efforts to link formality with access to government programs and bank financing might help induce some firms to register, but many firms will not benefit from such approaches. Improved enforcement is thus the third part of policy efforts. Our research shows enforcement can induce formalization, but that there are limits. Rather than having separate inspectors for different forms of registration, having municipal inspectors able to enforce municipal, state, and federal registration should have stronger impacts. And given that many of the firms the inspectors said they had closed down were open again at the time of our follow-up survey, there appears to be scope for improving the degree of enforcement that inspection actually entails. Combining enforcement with carrots may offer the greatest impact, since firms may be far more receptive to information and lower costs of registering when they have an enforcement incentive to register.

If enforcement appears promising as a way to bring larger informal firms into the formal sector, our study also shows the difficulties for researchers of evaluating such efforts. It does not seem ethically possible for researchers to survey firms about their formality status and then use this information to determine whether they are selected for enforcement. The approach pursued in this paper of listing 26
**Table 15. Combined scenarios: GDP shares by sector (%)**

||**2012**|**base**|**allgrw**|**allgrw-tr**|
|---|---|---|---|---|
|Agriculture and other primary|10.3|9.4|8.1|8.1|
|Industry|24.3|23.2|30.1|29.8|
|Industry -manufacturing|16.4|14.9|21.1|20.9|
|Industry -other|7.9|8.3|8.9|8.9|
|Services|65.4|67.4|61.9|62.0|
|Services - private|53.3|53.6|47.8|48.0|
|Services - government|12.1|13.8|14.1|14.1|
|Total|100.0|100.0|100.0|100.0|

Note: Columns other than 2012 show results for 2020.

**Figure 22. Combined scenarios: Annual consumption growth by household type (%-age point deviation from base)**

<!-- Start of picture text --> 4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0 allgrw<br>1.5 allgrw-tr<br>1.0<br>0.5<br>0.0<br><!-- End of picture text --> Note: For notation, see Table 3.

Figure 22 shows that the combination of the growth scenario with increased transfers clearly evens out the growth rates of consumption between the household types. Furthermore, as shown by Figure 23, the increase in income inequality is more mitigated while the reductions in the headcount poverty rate and the poverty gaps both are stronger.

34
Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes.

We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis.

16
**Figure 1: Firms in small countries use more imported inputs than firms in large countries**

<!-- Start of picture text --> ALB<br>EST<br>BWA<br>GABLSOARM<br>BEN<br>MKD RWA BFA<br>URYLTUMDACOGCRI<br>FJI SVNLVA BIH SLVTJKHNDGINBOLECUMDGNPL<br>MNG GEOHRV ZMB<br>CPV MUS PER VNM<br>PRY MWIGTM YEM<br>CHL<br>KGZBGRTCD CMR KENCOL BGD<br>SVKNIC SEN PHL<br>LAO KAZAGOROMGHAVENTZAARG ZAR BRA<br>AZEHUN CIVMOZUZBUGAPOLUKRTUR RUS<br>ZAF<br>PAN<br>MEXNGA<br>IDN<br>12 14 16 18 20<br>Population (logs)<br>80<br>60<br>40<br>20<br>% of material inputs that is of foreign origin<br>0<br><!-- End of picture text --> We note that our dependent variables are defined at the firm-level while the main explanatory variable is defined at the country level. Hence, reverse causality is unlikely to be an issue with our estimation results. However, we control for a number of variables, motivated by existing evidence, to guard against the omitted variable bias problem.

Our firm-level controls taken from ES include the (log of) number of permanent full-time employees at the firm ( _Employment_ ), age of the firm (logs), business climate measures including the percentage of time spent by a firm’s senior managers dealing with government regulations ( _Time tax_ ), a dummy variable equal to 1 if the firm was inspected during the last year by tax officials and 0 otherwise ( _Inspected_ ) and the average level of severity (on 0-4 scale) of various obstacles to the firm’s current operations as reported by the firms ( _Obstacles_ ). The obstacles include: tax rates, tax administration, obtaining licenses and permits and corruption. We experimented with controls for sector fixed effects but this did not change our results much.

4
Lundberg, Shelly and Robert A. Pollak. 1993. "Separate Spheres Bargaining and the Marriage Market," _Journal_ _of Political Economy_ , University of Chicago Press, 101(6/Dec):988-1010.

Lundberg, Shelly, Robert Pollak, and Terence J. Wales. 1997. “Do Husbands and Wives Pool Their Resources? Evidence from the U.K. Child Benefit,” _Journal of Human Resources_ , 32(3):463-480.

Miller, Grant and B. Piedad Urdinola. 2007. Time vs. Money in Child Health Production: the Case of Coffee <u>Price Fluctuations and Child Survival in Colombia,</u> **Processed** .

Ravallion, Martin and Quentin Wodon. 2000. “Does Child Labour Displace Schooling? Evidence on Behavioural Responses to an Enrollment Subsidy,” _The Economic Journal_ , 110(462/Conference Papers/Mar.):C158-C175 Stock, James H., Jonathan H. Wright and Motohiro Yogo. 2002. “A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments,” _Journal of Business & Economic Statistics_ , 20(4/JBES Twentieth Anniversary Issue on the Generalized Method of Moments/Oct.):518-529. Strauss, J. and D. Thomas. 1995 “Empirical Modeling of Household and Family Decisions,” in J. Behrman and T.N. Srinivasan, ed _._ , <u>Handbook of Development Economics, 3B, Elsevier Science B. V.</u> Tiefenthaler, Jill. 1997. "Fertility and Family Time Allocation in the Philippines," _Population and Development Review_ 23(2):377-397.

29
Docquier (2008) find an association between emigration and adult deaths from AIDS in African countries with high HIV incidence. When a larger sample covering developing countries from all regions is used, Bhargava et al. (2011) find no significant effect of medical brain drain on either infant and child mortality rates or child vaccination rates. These studies rely on cross-country panel analysis, with the associated limitations in being able to establish causal relationships. Measurement of these health externalities of skilled migration at a more micro-level is an important area for future research.

# **Conclusions**

Economists have been theorizing about brain drain for almost half a century. But until recently, there has been little empirical evidence to support or contradict these theories. The new evidence should counteract some of the myths and assuage some of the most common concerns about brain drain. Brain drain rates are not skyrocketing. Africa is not the most affected region for brain drain; small island states are. Most skilled migrants are not doctors. But neither are they taxi drivers – they enjoy massive increases in living standards as a result of migrating. The rise in skilled migration does not appear to be crowding out migration opportunities for unskilled migrants: instead, skilled and unskilled migration have increased together. Skilled migrants are remitting back about as much as the fiscal cost of their absence. Existing preliminary estimates of the production externalities of brain drain are quite small.

Yet we are still some way from a comprehensive global answer on the effect of brain drain on sending country growth and development outcomes, and further still from knowing the efficacy of policies chosen with high-skilled migration in mind. Data limitations continue to be a huge challenge to work in this area: there is a pressing need for better data which tracks the flows of high-skilled workers back and forward, as well as for specialized surveys to better understand the consequences of these movements (for discussion of these issues, see Commission on International Migration Data for Development Research and Policy, 2009). We have discussed eight key questions about brain drain. Here we pose and leave for further research five more questions for which answers are needed to better understand the overall impacts of high-skilled migration.

19
6 M. FAY, D. MARTIMORT, S. STRAUB from the Private Participation in Infrastructure (PPI) database, which includes information on the financial structure of projects. Finally, Section 5 concludes. Proofs of the main results and the description of the data are relegated to appendices.

# 2. THE MODEL

A public authority wants to develop a public service which requires building an essential facility worth an outlay investment _I_ . To do so, the public authority relies on the private sector while it still regulates the service, notably fixing its price. To finance such infrastructure, the public authority also relies on the private sector. In a first phase of contracting, a private firm, which is cashless, approaches outside financiers to obtain the required funds. In a second stage, the firm operates the service under the regulatory constraints. At this level of abstraction, this setting is consistent with the kind of privatepublic partnerships that are now fairly common throughout many sectors (transport, energy, water, environmental services, etc.) both in developed and developing countries. It may also conveniently represent the case of highly leveraged state-owned enterprises (SOEs) in regulated sectors (say a power plant or a water utility).

Consumers. There is a continuum of potential consumers with mass one. Each of those consumers expresses demand for one unit of the service. Let denote by _p_ the price of the service. In all the above examples, this price remains regulated by the public authority and we will later assume that the public authority chooses its level. Consumers differ according to their valuation _v_ ̃ for this unit of service. We assume that _v_ ̃ is drawn on R+ according to an atomless cumulative distribution _H_ . We denote by _h_ = _H_<sup>_′_</sup> the corresponding density function which is positive on R+.

A consumer with valuation _v_ ̃ thus buys this unit of service when _p < v_ ̃. We shall interpret the probability 1 _− H_ ( _p_ ) as the aggregate demand for the service. From this, it follows that consumers’ (overall) surplus can be defined as: In this context, we might also define the elasticity of demand as _ε_ (1 _p_ )<sup>=1</sup><sup>_−_</sup> _ph_<sup>_H_</sup> ( _p_<sup><u>(</u></sup> )<sup>_<u>p</u>_</sup><sup><u>)</u>.Forfuture</sup> reference, we will suppose that the following monotonicity condition of the hazard rate, a quite familiar assumption in the screening literature,<sup>5</sup> holds: Assumption 1 The Firm. Beyond the fixed-cost _I_ of setting up a key facility, the firm has a marginal cost _c_ ̃ of providing one unit of service. This random variable is drawn on R+ according to the atomless cumulative distribution _F_ . We denote the corresponding positive density by _f_ = _F_<sup>_′_</sup> . We denote by E _c_ ̃( ̃ _c_ ) the average cost. Mimicking what we already did on the demand side, we will also impose (but somewhat refine) another standard monotonicity condition of the hazard rate on the cost distribution, namely:

> 5See Bagnoli and Bergstrom (2005).