rafmacalaba/datause-encoder-data huggingface dataset:
- 24 -

Menezes, Flavio, Paulo Monteiro. 2000. Auction with endogenous participation. _Review of Economic Design_ , Vol. 5, pp. 71-89.

- Milgrom, Paul, Robert Weber. 1982. A theory of auctions and competitive bidding. _Econometrica_ , Vol. 50, pp. 1089-1122.

- Mohanty, Madhu. 2001. Testing for the specification of the wage equation: double selection approach or single selection approach. _Applied Economics Letter_ , Vol. 8, pp. 525-529.

- Moody, C.E., W.J. Kruvant. 1988. Joint bidding, entry and the price of OCS leases. _Rand Journal of Economics_ , Vol. 19, pp. 276-284.

- NOA. 2007. _Improving the PFI Tendering Process_ . The National Audit Office.

- Ohashi, Hiroshi. 2008. Effects of transparency in procurement practices on government expenditure: a case study municipal public works. CIRJE Discussion Paper No. 548, University of Tokyo.

- Paarsch, Harry. 1992. Deciding between the common and private value paradigms in empirical models of auctions. _Journal of Econometrics_ , Vol. 51, pp. 191-215.

- Poirier, Dale. 1980. Partial observability in bivariate probit models. _Journal of Econometrics_ , Vol. 12, pp. 209-217.

- Porter, Robert, Douglas Zona. 1993. Detection of bid rigging in procurement auctions. _Journal of Political Economy_ , Vol. 101, pp. 518-538.

- Price, Michael. 2008. Using the spatial distribution of bidders to detect collusion in the marketplace: evidence from timber auctions, _Journal of Regional Science_ , Vol. 48, pp. 399-417.

- Rezende, L. 2005. Auction econometrics with least squares. Mimeograph, University of Illinois, Urbana-Champlain, IL.

- Tunali, Insan. 1986. A general structure for models of double selection and an application to a joint migration/earning process with remigration. _Research in Labor Economics_ , Vol. 8, Part B, pp. 235-282.

- Ware, Glenn, Shaun Moss, Edgardo Campos, Gregory Noone. 2007. Corruption in public procurement: a perennial challenge, in _The Many Faces of Corruption: Tracking Vulnerabilities at the Sector Level_ , eds. by Edgardo Campos and Sanjay Pradhan, pp. 295-334. The World Bank.
were initially highly concentrated in the coastal areas also increased in several inland provinces in later years.

# **3.2 Provincial Internet Penetration Data**

Data on the internet rollout across Chinese provinces over time is central to the analysis. Our main measure of internet penetration is the number of internet users per province-year, obtained from various editions of the annual reports on the internet produced by the China Internet Network Information Center (CNNIC) for years 1999-2007. To account for the heterogeneous sizes of Chinese provinces and the private good nature of internet use, our final measure is scaled by province population; that is, the number of internet users per 10,000 people, designated in what follows as _number of internet users per capita_ .

An alternative measure of internet penetration is the number of .CN domain names, also obtained from the CNNIC for years 1999-2007. A .CN domain name represents a personal computer used to access the Internet, a server computer hosting a website, the website itself, or any other service communicated via the Internet. The fact that our measure covers top level .CN domain names, rather than just domain names of industrial, commercial, and financial enterprises, implies that it represents a broader measure of internet penetration or activity (including that of academic institutions, government agencies, non-profit organizations, and others) and is thus less vulnerable to endogeneity problems with respect to manufacturing firms’ outcomes. The number of .CN domain names can be interpreted as the total internet content produced in a province by businesses, government, and households, and hence goes beyond the number of internet users per capita by also capturing the depth of the internet penetration _._ Since the number of .CN domains have both private and public good aspects, it is not clear whether our final measure should be the number of .CN domains scaled by the population of the province or the absolute number of .CN domains. We will, therefore, use both the number of .CN domains per 10,000 people, designated in what follows by _number of .CN domains per capita_ as, and the _number of .CN domains_ .

We will consider, as an additional measure, the length of the fiber optic line (in kilometers) per province-year obtained from the NBS for years 1999, 2002, 2003, and 2005-2007. At the beginning of the period, fiber optic lines in China were primarily in the backbone (city-to-city) network. The final user had access to the network through digital subscriber lines (DSL) technology but had to be within a relatively short distance of the fiber optic line to get the full 9
||**Table 1: Average del**<br>|**ay minutes per per**<br>|**iod**<br>|
|---|---|---|---|
||**1996/97**|**1997/98**|**1998/99**|
||**Minutes**|**Minutes**|**Minutes**|
|**Railtrack**|560,384|551,836|609,044|
|**Operator**|306,261|369,852|447,787|
|**Total**|866,645|921,688|1,056,831|

Source: OPRAF

# **No future indicators of quality in the regime, or inappropriate indicators**

In the rail example given above, an additional problem with the incentive package was the absence of leading indicators of quality against which the businesses could also be judged. If indicators of the health of the network in particular had been formalized within the regulatory regime, then this would have provided an additional impetus to operate the network reliably and safely.

Where leading indicators of quality do exist (and they tend to be informal in nature), they tend to be based upon the investment that has been undertaken. This can create inadequate incentives for capital cost efficiency because regulators are attempting simultaneously to regulate costs and quality by setting a single variable: the firm’s investment programme, which is seen as both a contemporaneous and leading indicator of quality. In these cases, there is evidence that network businesses are becoming more capital-intensive (and less flexible in their response to regulatory incentives) as a result. If quality can be subjected to a separate output-based regime, even a crude one, this dual treatment of capital can be avoided.

A type of future indicator scheme that relies upon information regarding the quality of the assets (as opposed to the level of investment, _per se_ ) does apply in the water sector in England and Wales. The companies must submit to the regulator satisfactory asset management plans, on which it is possible for the regulator to judge the ongoing health of the network assets. No explicit system of fines and rewards are 11
Energy Economics 29 (4), Modeling of Industrial Energy Consumption, Pages 636664.

Diakoulaki, D., Mavrotas, G., Orkopoulos, D., Papayannakis, L., 2006. A bottom-up decomposition analysis of energy-related CO2 emissions in Greece. Energy 31 (14), 2638-2651.

Ebohon, O.J., Ikeme, A.J., 2006. Decomposition analysis of CO2 emission intensity between oil-producing and non-oil-producing sub-Saharan African countries. Energy Policy 34 (18), 3599-3611.

Ediger, V.S., Huvaz, O., 2006. Examining the sectoral energy use in Turkish economy (1980–2000) with the help of decomposition analysis. Energy Conversion and Management, 47 (6), 732-745.

Energy Information Administration (IEA), 2007. International Energy Outlook 2007. US Department of Energy, Washington, DC.

Federal Ministry of Economic Cooperation and Development (FMECD), 2007.

International Fuel Prices, 2007, Fifth Edition, FMECD, Germany.

- Han, X., Chatterjee, L., 1997. Impacts of growth and structural change on CO2 emissions of developing countries. World Development, 25 (3), 395-407.

- Hatzigeorgiou, E., Polatidis, H., Haralambopoulos, D., 2008. CO2 emissions in Greece for 1990-2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques. Energy 33 (3),  492-499.

Hudson, R.A. (ed.), 1997. Brazil: A Country Study. Washington: GPO for the Library of Congress. http://countrystudies.us/brazil.

IEA, 2007. International Energy Agency (IEA) Database Vol. 2007, Release 01: (a) CO2 Emissions from Fuel Combustion; (b) Energy Balances of Non-OECD Member Countries – Extended Balances (c) Energy Balances of OECD Member Countries - Extended Balances; (d) Energy Statistics of Non-OECD Countries - Basic Energy Statistics; (e) Energy Statistics of OECD Countries - Basic Energy Statistics; (f) Energy Balances of Non-OECD Member Countries – Indicators; (g) Energy Balances of OECD Member Countries – Indicators.

http://caliban.sourceoecd.org/vl=213511/cl=25/nw=1/rpsv/iea_database.htm.

31
**Figure 3: Bangladesh: Bilateral Trade Growth Rates (%)**

<!-- Start of picture text --> 50<br>44.33<br>45 42.65<br>40<br>35<br>30<br>25<br>18.47<br>20 16.24<br>13.32<br>15<br>10<br>5<br>0.79<br>0<br>1981-1990 1991-2000 2001-2009<br>Export to India Import from India<br>(%)<br><!-- End of picture text --> _Source_ : Calculated based on IMF DOTS.

# **3.2. Trade Complementarity and Intra-Industry Trade**

The trade complementarity index (TCI) indicates whether trade is likely to grow between countries. Research indicates that trade complementarity between India and Bangladesh is relatively limited, although it has grown.<sup>18</sup> The TCI at the disaggregated level (6-digit HS) for 2002 and 2007 offers mixed results for the two countries (Table 3). Bangladesh had a higher trade complementarity than India in both years,<sup>19</sup> though Bangladesh‘s decreased and India‘s increased. The scale of trade is driving the complementarity. Hence, although India‘s TCI score is lower, the number of commonly traded products is far higher, so India fulfills a larger proportion of Bangladesh‘s import demands; similarly, Bangladesh‘s higher TCI score for fewer traded products indicates a smaller trade creation potential with the existing basket of goods. The TCI scores do indicate, however, that bilateral trade between India and Bangladesh has the potential to grow, since trade in similar product lines has grown. This will deepen production networks between the two countries.

The scope for production networks and vertical trade between the two countries can be judged by the intensity of intra-industry trade (IIT) at the disaggregated (6-digit HS) level. IIT occurs when a country simultaneously imports and exports similar types of products within an industry or sector. There are two types of IITs––horizontal and vertical.<sup>20</sup> IIT has been described also as a measure of the degree to which

> 18 In some of the literature, it is observed that the lack of complementarity and high degree of competition in export structures imply daunting prospects for expanding regional trade in South Asia. Refer, for example, Pitigala (2005).

> 19

> The TCI measures the degree to which the export pattern of one country matches the import pattern of another. A high degree of complementarity is assumed to indicate more favourable prospects for a successful trade arrangement. The index lies on the range 0–100, with 100 indicating perfect complementarity. The latest period for Bangladesh trade data in UN Comtrade and other comparable sources was 2007. Before calculating TCI, data coordinates at HS nomenclature H2 were matched for both the countries for both years. For further details on TCI, refer Mikic and Gilbert (2007, p. 72).

> 20 Greenaway _et al_ . (1995).

8
9 America (with PAHO) (Wagstaff et al. 2015), then in the Universal Health Coverage Study Series (UNICO) countries (Wagstaff et al. 2016), and more recently globally (with WHO) (Wagstaff et al. 2018a; Wagstaff et al. 2018b). The 2018 HEFPI data set includes the datapoints contributed by the World Bank to the joint 2017 WHO-World Bank data set (80% of the total), but also many others generated (by the World Bank) since. With the new datapoints, the financial protection part of the 2018 HEFPI data set is larger than the 2017 WHO-World Bank data set and covers more countries. On both the health equity and financial protection sides of the HEFPI database, the country coverage has expanded as well – from 109 countries in 2012 to 193. The number of household surveys used has increased even more dramatically – from 285 in 2012 to over 1,600.

The datapoints in the 2018 HEFPI data set, like those in the previous three data sets, have, wherever possible, been computed from the original microdata. For some surveys, this was not possible, and we have had to make do either with published reports (as in the case, for example, of the STEPS surveys) or with studies by researchers who have used the same methods as us (see e.g. Van Doorslaer and Masseria 2004; Van Doorslaer et al. 2006a). One goal behind re-analyzing the original microdata was to ensure maximum consistency across surveys, countries and years. Sometimes, this means that our data are not identical to those on the websites and in the reports of the organizations that produced the microdata. For example, we use the same (2006) standards for childhood stunting and underweight in all surveys. The DHS reports, by contrast, use whatever standard was in force at the time the survey was done. In addition to ensuring consistency, there was a second reason to re-analyze the microdata: to ensure we have data for different ‘wealth’ or income groups, and a summary measure of inequality, namely the concentration index and its standard error (Kakwani et al. 1997).

# **Overview of the 2018 HEFPI database**

Figure 2 gives an overview of the 2018 HEFPI database in terms of indicators. The darker shaded boxes contain indicators used already in the MDGs. The lighter shaded boxes contain
described further in the following sections. The key outcome variable is per capita emissions of CO2. We form a control group capable of replicating Australia’s pre-existing trend in emissions per capita prior to implementation of the new climate policies (as well as matching on other relevant characteristics). Candidate pre-period characteristics include emissions shares from different sectors, GDP per capita, and energy consumption of different emitting fuels (coal, natural gas, and petroleum products).

In estimating effects, our ideal thought experiment is a comparison between the true emissions trajectory of Australia and a counterfactual Australia which never experienced the rapid changes in environmental policy around 2010 that were truly observed (but otherwise equivalent in all respects). To form an appropriate donor pool for estimation of this counterfactual emissions trajectory by synthetic control, we exclude from the donor pool any economy subject to a nationallevel carbon price or ETS implemented from 1980-2017. This approach has some weaknesses: For example, we are unable to extract any information from variation in policy stringency or sectoral coverage, and we risk measurement error by omitting subnational policies or renewable generation incentives that may be present among donor economies. However, our approach also has some strengths: It is relatively transparent and simple, and appropriately ignores many policies that offer weak incentives for market participants and have little practical implication for nationallevel emissions (including sub-national policies that may suffer from significant within-border leakage). Additionally, for potentially omitted policies in the donor pool, our strategy is conservative in the sense that any policy effective in reducing emissions that shows up in the donor pool should bias our estimates toward finding a null effect (a ‘control’ economy that is ‘treated’).

# **3.1 Data**

The outcome variable for the main analysis is the emissions of CO2 per capita. Emissions data come from the _Emissions Database for Global Atmospheric Research_ (EDGAR). The EDGAR dataset uses a bottom-up methodology to calculate CO2 emissions in the power, industry, buildings, transport, and agriculture sectors. The methodology is applied consistently to all countries to facilitate comparison across economies.<sup>6</sup> We also use the EDGAR data to calculate

> 6 Further information and downloads are available at https://edgar.jrc.ec.europa.eu/report_2022.

9
as not all types of credit may have the same impact on productivity and growth or financial stability, looking at data by purpose, regardless of maturity, is of interest to policy makers.

The main purpose of this paper is to construct a data set on the structure of bank credit allocation in LAC. In partnership with the Association of Supervisors of Banks of the Americas (ASBA), we circulated a survey on individual bank loan portfolio composition and maturity. For the countries that did not respond to the survey, we used publicly available data from bank supervisory authorities or central banks. In total, we assembled data for 21 countries in the region across 2004–2014 using data from supervisory sources. To compare the credit allocation structure in LAC to that of other regions, we compiled data from Bankscope. We then calculated the shares (over total loans) of loans by maturity and purpose of credit obtained from these sources, and then multiplied these shares to International Financial Statistics (IFS) credit data to obtain credit volumes. In all, we constructed data on credit by maturity for 107 countries and on credit by purpose for 103 countries. To our knowledge, this is the most comprehensive cross-country data set on bank loan by maturity.

Armed with these data, we analyzed patterns and trends of bank credit allocation across regions and we explored how these patterns differ in LAC. Benchmarking the credit structure in LAC, we found that LAC is financially underdeveloped because short-term credit to gross domestic product (GDP) is lower than in peers, while long-term credit is at par with peers. In terms of loan by purpose, consumer and commercial loans are lower in LAC. We also explored patterns of credit growth through nonparametric regressions. The results indicate that short-term credit grows faster than long-term credit as income grows in low-income countries, but the situation reverses when countries reach high- or middle-income status. Reflecting this trend, mortgage loan share rises with income. Econometric analysis of the determinants of long-term credit around the world and in LAC is left for future work. However, a simple correlation analysis indicates that stable macroeconomic environments and sound political and institutional factors are associated with more long-term credit and mortgages, in line with results in the empirical literature. In addition, in line with previous empirical studies, foreign and public (government) bank ownership is negatively correlated with credit depth. Foreign ownership negatively affects short- and medium-term credit but not long-term credit, while public (government) ownership is more negatively correlated to medium- and long-term credit.

4
Stiglitz, E. and Weiss, A., 1981. _Credit Rationing in Markets with Imperfect Information._ American Economic Review, 1981, vol. 71, issue 3, pg 393-410.

Svensson, L., 1997. _Optimal Inflation Targets, ‘Conservative’ Central Banks, and Linear Inflation Contracts_ . American Economic Review, vol. 87 (March), pp. 98-114.

Svensson, L., 2005. _Monetary Policy with Judgement: Forecast Targeting._ CEPR Discussion Papers 5072, C.E.P.R. Discussion Papers.

Svensson, Lars E. O. 2010. “Inflation Targeting and Financial Stability.” Policy Lecture at the CEPR/ESI 14th Annual Conference on “How Has Our View of Central Banking Changed with the Recent Financial Crisis?” October 28–29, Central Bank of Turkey, Izmir.

Taylor, John B., 1993. "Discretion versus Policy Rules in Practice," _Carnegie-Rochester Conference Series on Public Policy_ , 39, pp.195-214.

Taylor, John B., 2009. _Getting Off Track: How Government Actions and Interventions Caused, Prolonged and Worsened the Financial Crisis_ . Stanford: Hoover Institution Press.

Woodford, M.. 2003. _Interest and Prices: Foundations of a Theory of Monetary Policy_ . Princeton: Princeton University Press.

Woodford, M., 2011. _Inflation Targeting and Financial Stability_ . Speech at Einaudi Institute for Economics and Finance, Rome.

Wyplosz, C., 2009. _What Do We Know about Monetary Policy that Friedman Did Not Know?_ Commission on Growth and Development. Working Paper N. 63.

36
order to avoid a bad reputation or to maintain the necessary credibility for further loans. The evidence from the IHS is in panel B of Table 2: 65 percent of the smallholders that organize themselves into clubs have indeed better access to credit.

The loans collectively obtained by the club are used mainly to buy inputs (fertilizer) in bulk at the beginning of the rainy season (October to December). In principle, this allows farmers to have access to cheaper and higher quality inputs. The evidence supports this statement. While only 13 percent of clubs from the IHS and 11 percent from the TCS actually pay lower prices for inputs, 66 percent of clubs (in the TCS) have in fact access to input of better quality. This implies that farmers pay roughly the same prices as non-members but get instead more efficient inputs.

The third major prerogative of burley clubs is the realization of economies of scale, especially in transporting tobacco. Once again, the IHS and the TCS provide complementary evidence, presented in panel C of Table 2. Around 67 percent of club members surveyed by the IHS benefit from overall transportation services (the actual question in the questionnaire refers to “transport of bales to the market,” without specifying price or quality). However, according to the TCS, only 5 percent of the clubs enjoy lower prices for transportation due to membership. To resolve this apparent discrepancy, we collected additional qualitative information from group interviews. Members stated that transportation of their output with the club (through NASFAM or TAMA) is not necessarily cheaper but is instead more secure and safer. Moreover, while club members can be compensated for any losses to the harvest while in transit, farmers transporting the crop individually had to absorb any such burdens or risks themselves. In the end, the realization of economies of scale does not allow club members to fetch higher net prices but it does allow them reduce some risks associated with the marketing of tobacco.

Another major service provided by the clubs is as supporting networks and safety nets, which has significant implications in terms of labor supply, effort, absorption of negative shocks and externalities from positive shocks. The evidence from the Tobacco Club Survey plus interest, from the amount of the sale of tobacco (the difference is deposited directly into the club’s bank account).

13
Table _5: Estimate of dynamic model of wholesale interest rate convergence - Equations (1), (19_

||_A Cons_|_trained_|_B Co_<br>_specific_|_untry-_<br>_dynamics_|_C Eff_<br>_Libera_|_ect of_<br>_lization_|_D Co_<br>_dyn_|_mmon_<br>_amics_|_D'Liber_<br>_changes_|_alization_<br>_dynamics_|_E Count_<br>_autoco_|_ry-specific_<br>_rrelation_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|
|_a_|0.079|(8.3)|0.042|(6.1)|0.046|_(6.5)_|0.040|(3.3)|0.044|(5.9)|0.054|(3.9)|
|_b_|0.135|(3.3)|0.071|(3.0)|||||||-0.014|(5.4)|
|c|0.017|(10.4)|0.011|(9.0)|0.008|(5.5)|0.009|(4.6)|0.007|(5.0)|0.010|(3.9)|
|_d_|0.320|(24.6)|country||country||0.683|(28.5)|||0.759|(17.1)|
|_dum *a_|||||||0.063|(3.5)|||0.141|(5.4)|
|_dum*b_|||||0.102|(2.9)|0.148|(2.8)|0.093|(2.5)|0.077|(3.1)|
|_dum*c_|||||0.009|(4.1)|0.011|(3.8)|0.008|(3.7)|0.035|(5.8)|
|_dum*d_|||||||-0.449|(16.5)|-0.207|(6.5)|-0.211|(6.2)|
|Sample|26 develo|ping|26 devel|oping|26 devel|oping|26 devel|oping|26 devel|oping|26 devel|oping|
||80:03-96|:12|80:03-96|:12|80:03-96|:12|80:03-96|:12|80:03-96|:12|80:03-96|:12|
|Method|SUR||SUR||SUR||SUR||SUR||SUR; 18'|order auto|
|RSQ/DW|0.097|2.06|0.157|2.02|0.161|2.03|0.103|2.03|0.164|2.03|0.332|1.98|

Note: 'Country' means country-specific coefficients estimated (not reported); Method: SUR is Seemingly unrelated regressions system estimate of pool coefficients; 'auto'=autocorrelation.