rafmacalaba/datause-encoder-data huggingface dataset:
34 SENA, the economy-wide small business association, ACOPI, and the garmnit industry association. The non-electrical machinety industry is technologicaly more complex dun the others and, perhaps reflecting this differmce, specialist technology agencios rat than industy associations emerge as the leading collective providers. However, even for this subsector, courses (rather dn high-intesity support) emerge as dte dominant source of coUective support, with SENA the leading provider.2&!

In Japan, tchnical centers under the umbrella of local govemenmts are the primary providers of broad-based collective technical support. Their role was most significant in two of the three subsectors surveyed,29t where (as Table 14 shows) about half the rms surveyed used, their services, with _56%_ of these conferring upon them a usefulnes score of 3 or higher. While in the past the technical centers also provided high-intensity support, at present their efforsappear largely to be confined to sponsoring courses. providing test and inspection services, and providing assistn on caummon technical problems that aise from normal business practices, including a modest nmmber of firm visits.

A further filmctioh oa Japan's collective technical support system - difficult to measure - but, by all accounts, very useful is as a node in an iformationu-ch networtl The Japan silverware case study includes one especially vivid example of how ths network functions. A Tsubame entrepreeu came up with the idea of making silverware with handles whose shape could adjust to fit the needs of individual disabled users. The collective support network (in this instance the local business asscation) helped him identify a range of semia and exhibitions held throughout Japan which plausibly could provide b with useful informaton. Evetally he came upon a company which was developing precisy the mtrial he needed for his 2-1 -Intrestinly, 27% of the Colombian machinery firms sampled (12 of44j also repod support from Columbia's product standards certification agency - with an aerage usefulness score among users of _3.5._ There was no evidence of sundards agenies playningany usefil role - in Indonesia. These agencies appantly are important in both Korea and Japan however, ffieir roles were not eamined systematically in the Korean and Japanese case studies.

-221 -In the third subsef r - auto parts in Ohta - only 18% of firms sureyed (6 of 33) used the center; however, the average usefulness score for these six users was 3.8.;
World Bank (2010a), _Natural Hazards, Unnatural disasters. The Economics of Effective Prevention_ , Washington, D.C.

World Bank (2010b). _Climate Risks and Adaptation in Asian Coastal Megacities_ . Washington, DC: World Bank.

Zakour, M.J. and D.F. Gillespie (2013): Disasters and the Promise of Disaster Vulnerability Theory. In Community Disaster Vulnerability: Theory, Research, and Practice, eds. Zakour, M.J. and D.F. Gillespie, 1-15. New York: Springer.

42
complemented with monitoring and qualitative data collection both during and after program implementation. First, systematic quantitative “monitoring” data were collected during implementation (prior to endline survey) with information on program rollout. Second, semistructured interviews with parents, teachers, school directors, and implementers in a small random set of treatment and control villages (of each type) were conducted after an initial round of data analysis to explore hypotheses that emerged.

# **4. Main evaluation findings: Implementation and participation**

# _4.1. Implementation and compliance with experimental design_

When evaluating larger-scale programs scaled up from small pilots, a key question is whether the scaling-up is implemented in ways that affect the experimental design. In this case, deviations from the experimental design resulted from contamination of the control villages and uneven implementation of the interventions in the treatment villages. Deviations from the experimental design affected all three programs but were particularly severe among the HBPs and CBPs.

# <u>FPSs</u>

Implementation of the FPSs was relatively good. By June 2011, 82 percent of treatment villages had access to a preschool (Table 1). Because some control villages also benefited from construction of a preschool (13.1 percent of control students lived in a village with access to preschool), differential take-up at the village level<sup>5</sup> fell below 70 percent. Interviews with technical staff of the Construction Department revealed that the random assignment of control and treatment villages was not easily accepted by the teams responsible for building the primary schools and was not respected in two instances. In all, 3 control villages were treated.

Importantly, while some schools were supposed to be at least partly finished for the start of the 2009/10 school year, in reality no new primary school (with a preschool attached) was finished before October 2010, the beginning of 2010/11 school year. Some were finished even later during that school year. The endline data were collected at the end of that school year

> 5 This does not account for individual take up rates

7
20 are for salary, 20% are for transportation, 5% are spent on producer training activities, and the rest (5%), are for administration, utility and other miscellaneous costs.

Table 8: Monthly ATPl and ATPb extensionist cost in 1997

||Units per month|Cost per unit ($US)<br>Monthly cost (|$US)|
|---|---|---|---|
|Fixed costs||||
|Salary|1|178<br>178||
|Fuel|20 gallons|2.4<br>48||
|Maintenance of vehicle|1|10<br>10||
|Per diem|20 days|1.33<br>27||
|Sub-total|||263|
|Variable cost||||
|Stationary and office supply||10||
|Training||20||
|Electricity and water||_5_||
|Taxes, rents||I||
|Insurance||2||
|Sub-total|||38|
|Total per month|||301|
|Totalper<br>year|||3612|

Source: Estrada-Rizo and Garcia (1997) Figure 6: Share of main components in extension provision cost (ATPl) 1995-97 <!-- Start of picture text --> i U~~~~~~~~~~~~~ Misc<br>iOO% = ~~..u...m...m... Ms<br>0 Office SupIy and Utilities<br>70% ::::::Maulnuu umemm<br>60e M Administration<br>S<br>o 50%<br>40% 0Training of Producers<br>400%<br>30% O Transportation<br>20%<br>10% 0 Salaries, per diem,, and<br>Social<br><!-- End of picture text -->
tonnes in China (4.81% and 8.41% of fuel ethanol productions in U.S. and Brazil by volume<sup>1</sup> ), including a 200,000 tonnes fuel ethanol project using cassava as feedstock **.** Two major oil companies - China National Petroleum Corporation (CNPC) and China Petrochemical Corporation (SINOPEC) - and a large agri-business company - China National Cereals, Oil & Foodstuffs Corp (COFCO) - have engaged in the production of fuel ethanol through stipulated investments, stock holdings and other mechanisms. SINOPEC, CNPC, and another major oil company – China National Offshore Oil Corp (CNOOC) - are also involved in the production of biodiesel (Table 2).

**Table 2: Designated Biofuels producers in China**

||Location|Initial Capacity<br>Approved(1000<br>tonne)|Current<br>Capacity (1000<br>tonne)|Supply region|Feedstock|
|---|---|---|---|---|---|
|Fengyuan|Bengbu, Anhui<br>Prov.|320|440|Anhui ,<br>Shandong,<br>Jiangsu, Hebei|Corn|
|Tianguan|Nanyang, Henan<br>Prov.|300|500|Henan, Hebei,<br>Hubei|Wheat, Cassava|
|COFCO<br>Zhaodong(Former as<br>Huarun)|Zhaodong,<br>Heilongjiang<br>Prov.|100|280|Heilongjiang|Corn|
|Jilin Fuel Alcohol|Jilin, Jilin Prov.|300|500|Jilin, Liaoning|Corn|
|Guangxi Bioenergy|Beihai, Guangxi<br>Prov.|200|200|Guangxi|Cassava|
|CNOOC|Hainan Prov.|60<sup>a</sup>|60|Hainan|Jatropha/waste<br>oil<sup>b</sup>|
|CNPC|Nanchong,<br>Jiangsu Prov.|60<sup>c</sup>|-|-|Jatropha|
|SINOPEC|Guizhou Prov.|50<sup>c</sup>|-|-|Jatropha|

Note: a. The project has been completed in 2010, but production is suspended for modifications in 2011; b. Planned feedstock is jatropha, but according to authors’ field survey, the current feedstock is waste oil due to the jatropha planting base is still under construction; c. The project has been approved by government, but hasn’t initiated yet.

The biodiesel productions in China are now carried out mostly by private enterprises using waste oil as feedstocks. Total capacity is about 1.5 Million tonnes (Liu et al. 2011), and even 2 million tonnes (CRES, 2010). About 10 enterprises’ capacity are higher than 100,000 tonnes, i.e., Zhuoyue (100,000 tonnes) in Fujian Province. And some companies adopted advanced technologies (i.e., biological enzyme catalysis process by River in Hunan province (20,000 tonnes)).

> 1 Fuel ethanol productions in U.S. and Brazil are 49 and 28 billion liters respectively in 2010 according to REN21 (2011).

5
by three reference households (consisting of children and people of working age, in good health, without disabilities, and living in Bucharest<sup>16</sup> ). The basket includes a food budget and kitchen equipment required to prepare, serve, consume, and preserve this food. Furthermore, it considers the necessary budget for physical activity and other food functions, such as its social function. The food basket has been developed per nutritionist recommendations and national dietary guidelines, considering the cultural specificities of Romanian eating habits. The feasibility and acceptability of the basket have been verified through focus group discussions involving citizens with different socio-economic backgrounds. Estimates show that in 2015, the monthly budget required for a healthy diet in Romania was EUR 128 for a single person and EUR 499 for a family of two adults and two children. If the budget needed for physical activity and other food functions (eating out, holidays, etc.) is also considered, the total monthly food budget amounted to EUR 156 for a single person and EUR 578 for a family of two adults and two children.<sup>17</sup> When converted to 2020 Euros using official food CPIs, these monthly food expenditures correspond to 181 EUR for a single person (able or unable to work) and EUR 670 for a family of two adults and two children. These estimates suggest that the average monthly per capita food basket cost in 2020 euros is approximately 126 EUR and 180 EUR when considering non-food components/other functions. This is equivalent to 4.1 EUR per capita per day and 5.9 EUR (considering other functions). Table 1 presents these estimates.

**Table 1. Estimates of Food Basket based on European Commission (2015), 2015 and 2020 euros.**

**Panel a. By Household composition (2015 euros)**

||**In 2015**<br>**Euros**|
|---|---|
|Single Person (Average Monthly, Euros)|128|
|Single Parent and One child (Average Monthly, Euros)|254|
|Single parent and Two Children (Average Monthly, Euros)|380|
|Family of two adults and one child (Average Monthly, Euros)|382|
|Family of two adults and two children (Average Monthly, Euros)|499|
|Single Person (Average Monthly, Euros)- Including physical activity and other functions|156|
|Family of two adults and two children (Average Monthly, Euros)- Including physical activity and other<br>functions|578|

**Panel b. By Household composition (2020 Euros)**

> 16 Notice the food basket does not take into account spatial differences in living standards since it was estimated for those living in Bucharest.

17 Source: EC, Directorate-General for Employment, Social Affairs and Inclusion, “The Romanian Food Basket”, 2015. <u>https://op.europa.eu/en/publication-detail/-/publication/7cd42604-b82f-11e5-8d3c-01aa75ed71a1/language-en</u> 17
(2002) wish to examine whether poverty is associated with higher or lower levels of deforestation in southern Mexico. A standard concern is that there are other variables which are correlated with _X_ and which also affect _Y_ . Failure to control for these variables then gives biased results. One of the most basic uses of GPS is to allow researchers to better control for geographic and locational characteristics in their regressions. Such characteristics are increasingly found to be relevant to outcomes of interest for development economists and practitioners. For example, Deininger and Minten obtain data from a GIS on soil quality, rainfall, elevation, slope and other geographic features, and find that higher levels of poverty are statistically associated with _greater_ likelihoods of deforestation. However, when they re-estimate the model without using the GIS data they find poverty to be associated with _lower_ levels of deforestation. The problem here is that the poor live on worse quality land which limits the benefits of deforestation, so failing to control for land quality gives an opposite result.

Propensity-score matching has become a popular tool for investigating policy impacts (see Ravallion, 2006 for a recent review). The basic idea is to compare individuals subject to a policy to similar individuals not subject to the policy. Typical variables used for matching are household socioeconomic characteristics, and an often crude set of community-level variables. Brady and Hui (2006) argue that GIS can be used to more explicitly include geography in matching. They present three arguments for doing so:

- 1) lots of individual data that we would like to match on is unmeasured, and so place can serve as a proxy for unmeasured individual characteristics;

- 2) near-by places are more likely to share community characteristics, such as culture, trust, and government ability; and

- 3) geographic matching can be visually persuasive, if you see sudden changes in outcomes across administrative borders when a program is in one community and not its neighbor.

Nevertheless, they acknowledge that in some cases places which are most comparable in terms of cultural or socioeconomic characteristics may not be geographically close. Therefore it is important that matching not only be done on geography. Although the U.S. labor literature has emphasized the importance of comparing participants in training programs and non-participants from the same local labor markets (see Heckman et al. 1997), the matching literature to date has generally not explicitly included geographic proximity as a criteria when matching individuals in

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- 29 -

vI. Accept Indirect cost recovery whore It exists, but insist on an accounting of tho oquity Issues of irrigation rent transfer.

These propositions yleld a number of knplcatlons for irrigatlon lending that should kmprove existing practice. The first two knply that the Bank would be more flexible concerning institutional preferences but would Insist more strongly on arrangements that preserve the sustainability of Irrigation Investments and associated natural rosources. A key feature of the emphasis on sustainabillty requires the development of an "OLM Plan" with the borrower at the tkne of appraisal. This plan would go beyond the provision of adequate O&M funding to specify the level of effort required to sustain the Investment. Covenants requiring compliance with the O&M Plan would be part of ioan agreements and their vilation would Initiate a mandatory Bank response. Repeated violatlon would usually end In cessation of Irrigation lending.

The third and fourth propositions address Issues of Irrigation finance and cost recovery. The clear lesson from the Kruoger-Schlff-Valdes study Is that most countries prefer to kmpose direct and Indirect taxes on agricultural commodity outputs. This taxatlon Is usually massive and further taxes on farmers can only be hkposed on cost recovery or equity grounds. Often additional taxatin will not be justified. This determination requires, however, a thorough-golng economic analysis that goes beyond direct taxation. Repilcation of the KSV methodology (where necessary) would seem to be an obvious first step In such an analysis. An obvlous corollary of the KSV study Is that irrigation finance Is most appropriately addressed as a policy adjustment Issue.

The last two propositions deal specifically with Issues of officlency and equity that arise In connection with the analysis of irrigation service fees. They are compiemontary to the third and fourth propositions In that they are concerned with the Issues of tax
revenues and expenditures managed by rural local governments across provinces. This unusual aspect of China’s public finance system has been noted in earlier studies (see, for example, World Bank 2002). It is also confirmed in the four provinces where fieldwork was conducted for this study. In each of the four provinces, the county plus township shares of expenditures are more than 50 percent of the (subnational) total, significantly higher than the national average of 43 percent (Figure B).<sup>11</sup>

**Figure B.  Shares of Budget Expenditures by Level (2003)**

<!-- Start of picture text --> Yunnan<br>Henan<br>Sichuan<br>Jiangxi<br>National<br>0% 20% 40% 60% 80% 100%<br>County + Township Prefecture Province<br><!-- End of picture text --> Source: Compendium of Local Fiscal Statistics, 2003.

The share of expenditures accounted for by the provincial government also varies significantly **.** The provincial share is perhaps more indicative of choice since, unlike other subnational governments, provincial governments have no clear role in the direct provision of services. While averaging 28 percent nationwide (when the four provincial-level municipalities and Tibet are excluded), this share ranges from lows of 13 and 14 percent in Zhejiang and Guangdong, to highs of 49 and 43 percent in Qinghai and Ningxia respectively.<sup>12</sup> The summary descriptive statistics in Table 2 for the 27 provinces and autonomous regions

> 11 By themselves, these different shares of the rural sector may be merely reflections of the different degrees of urbanization across provinces. For example, as measured by the share of agriculture in provincial GDP, the four fieldwork provinces are all significantly more “agricultural” (and rural) than the national average. The shares of the primary sector in provincial GDP in 2003 were 1.44, 1.42, 1.37, and 1.22 and in 2004 1.40, 1.34, 1.34, and 1.23 times the national average in Sichuan, Yunnan, Jiangxi, and Henan, respectively (Statistical Yearbook 2004, 2005).

> 12 MOF (2003); Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties, 2003.

9
of proprietorships. Second, cross-country regressions are subject to the usual biases of endogeneity and simultaneity. While in the growth regressions we control for these biases by employing instrumental variables, our estimations of credit composition across countries do not control for endogeneity and we therefore do not interpret the findings as causal relationships. Finally, due to data constraints, we focus on bank lending to households and ignore lending to households by non-financial institutions, an increasing phenomenon in many high- and middleincome countries.

The remainder of the paper is organized as follows. Section 2 discusses the construction of our main variable of interest. Section 3 presents the results from cross-country growth regressions when introducing separate measure of enterprise and household credit. Section 4 explores country characteristics and policies associated with credit composition. Section 5 concludes.

# **2. Decomposing Bank Lending**

Standard financial sector indicators focus on the aggregate value of credit to the private sector by deposit money banks, but do not distinguish between lending to households and lending to firms. We compile data from national central bank reports, annual bulletins, and other statistical sources where disaggregated credit data are available. Our dataset includes 45 countries spanning different time periods depending on data availability but with a significant overlap during the period from 1994 to 2005. In order to avoid discrepancies between different countries we standardized our data collection methodology by focusing on the collection of data on credit to non-financial corporations and/or private enterprises/businesses by deposit money banks, where available. If private credit is reported for various economic sectors, we define business credit as the sum of loans to industry, construction, services, agriculture, and trade. We then use the credit series from the Financial Structure Database of Beck, Demirguc-Kunt and Levine (2000) to obtain the distribution of credit into enterprise credit and household credit as the difference between overall credit and enterprise credit. While we have annual data available, we will use mostly averages over the period 1994 to 2005.

Table 1 presents the large variation in overall banking sector development and the relative importance of enterprise and household credit across our 45 sample countries. Specifically, we present Bank Credit to GDP – total claims of deposit money banks on the 4