rafmacalaba/datause-encoder-data huggingface dataset:
assumptions seem baseless given the evidence.<sup>8</sup> Therefore it is not possible to know from the household surveys if enrollment in particular grades increased over time.

In addition, the rules of the stipend do not state that girls must be of the correct age-forgrade in order to receive the stipend. Thus in order to check if there is a change in the enrollment patterns of children in the treatment district, I compare the enrollment probabilities over time of 10-14 year old girls in the treatment district with 10-14 year old girls in the comparison districts. I make the choice of 10-14 year olds on the grounds that it is the same age range as chosen by Chaudhury and Parajuli (2006, 2008) in their evaluation of the program and because an inspection of the age-grade profile from the PSLM 2004 (see Figure 1) suggests that girls in this age range are predominantly enrolled in grades 6 through 8.<sup>9</sup> Having verified the appropriateness of focusing on 10 to 14 year-old girls, I examine how the enrollment probabilities for these girls change over time in the treatment and comparison district households in the LEAPS data. Consistent with the targeting criterion of the stipend program and with existing evidence of the effects of the program, girls in the treatment district are significantly less likely to be attending a public school at baseline (Figure 2).<sup>10</sup> The average probability of attending a public school for 10 to 14 year old girls is 0.36 for the treatment district and 0.46 for the comparison district. This 10 percentage point difference at baseline is significant – both statistically and in magnitude. The gap narrows over time but continues to be statistically significant in rounds 2 and 3. In round 4, there is no statistically distinguishable difference between girls in the treatment and comparison districts. This difference in trends

> 8 See for instance Holmes, 1999.

> 9 This figure reinforces the point about schooling trajectories made earlier.

> 10 The figure plots sample averages for the treatment and comparison groups along with the associated confidence intervals.

9
clients' creditworthiness. In these instances, the distinction between information acquisition and information provision (consulting) begins to blur.' Unlike an equity investor., a bank is not interested in the impact of a manager's marginal decision on the firm's profitability, but it nevertheless is interested in the irmpact on the firm's probability of barJcuptcy. Ihe more likely bankruptcy becomes, the larger the incettives of a bank to influence management. This may happen when balance sheet data poin,t at a iaegadive trend, but in Harm 118] we also discussd the importance of consulting and advisory services above in the case of business start-ups, because the probability of failure is larger for new enterprises.

Consulting is a word used with varying definitions, and we use it here in the broadest sense. Any salesman of a product is typically more of an expert regarding his product than his client. In case he would not only sell the product, but -also recommend the optimal use of it, he would in this sense perform a consulting activity, as he not only sells a product, but also information.

P:'kel (4] discussed a long list of consulting activities of banks, and financial advisory services are naturally on top of the list, as they are directly related to the banks' product. Consulting is never only providing information, but it is necessarily preceded by acquiring information on the respective problem in order to yield an analysis that helps the management of the firm. Thus, when a bank should decide that a particular client demands more investigation for example in his financial planning, ihey may actually provide a consulting service to him after they find out about the state of financial planning in the respective firm.

Bickel (4] argued that such kinds of consulting services provided by banks are especially prominent in their relationships with small firms, because the bank has many services to offer particularly to small firms. Bank staff typically has a business oriented training, while the managers of small firms typically started their business or got to their position in the firm, because they were experts in their product's market.<sup>6</sup> This is also an experience that was mentioned by virtually every banker that was interviewed in the course of this project: the critical phase in any business is when it grows to a size that the manager becomes more and more involved in administration, representation, and planning, and gets more disattached from the product. In this situation, the bank can assist its clients, ultimately for its own good.

5In this context, a quote by Alfred Herrhausen [22,p. 103]: "The control institutions of the corporations became consulting institutions, because the important feature of a control institution is not to uncover mistakes made in the past, but to prevent mistakes from being made." (Free translation) This corresponds to the changing role of internal auditing departments as voiced by Puchta [4<sup>0</sup> ,p.<sup>65</sup> ].

6Bickel [4,p.202] _5_
percent is concentrated in Tanzania where it could be argued that this is supported by historical ties linked to a shared social ideology from 1960s coupled with a history of Indian settlement.

Table 3: BRICS’ total direct investment (in-stock) in millions of USD in selected African countries, 2001-2012

||Brazil|Russia|India|China|South Africa|Total|
|---|---|---|---|---|---|---|
|Angola|2073.3|||2386.4|376.2|4835.8|
|Botswana||0.5|75.5|3.6|4569.6|4649.3|
|Cameroon|||20.7|301.6||322.3|
|Chad||||513.2||513.2|
|Congo, Rep.||||1121||1121|
|Congo, Dem. Rep.||||3025.1||3025.1|
|Equatorial Guinea||||802.2||802.2|
|Ethiopia|||12.1|2058.2||2070.3|
|Gabon|||174.6|766.7||941.3|
|Côte d’Ivoire|||1.6|271||272.6|
|Kenya|||41.3|1345.5|1261.9|2648.7|
|Liberia|580.2||35.4|532|29|1176.5|
|Madagascar|||15|361.4|41|417.4|
|Tanzania||-8.6|458|515.6|13533.3|14498.3|
|Uganda|||1182.6||2473.7|3656.3|
|Zimbabwe|||46.5|1967.1|4384.2|6397.9|
|Total|2653.5|-8.1|2063.3|15970.4|26669|47348.2|

Source: Authors’ computations based on UNCTAD (2016) China’s FDI spans a number of countries with the biggest recipients including (as shown in Table 3): The Democratic Republic of Congo, Angola, Ethiopia, Kenya, the Republic of Congo Equatorial Guinea, Chad, and Côte d’Ivoire. Although many of these are resource rich, China’s recent investments are increasingly going to the manufacturing and construction sectors (in both resource and non-resource rich countries). Shen (2015) shows that 44 percent of Chinese FDI (based on data from investment promotion agencies of 6 African countries in 2011, namely, Nigeria, Ethiopia, Zambia, Liberia, Rwanda and Ghana) flows to the manufacturing sector in 17
after the application of the H-S weights (see Atkinson, 1987 and Ravallion, 1992 for further details of these tests). Figure 1 compares the cumulative distribution of per capita household expenditures with and without H-S weights for 1985 and 1986. This shows that for both years, the unweighted distribution lies entirely to the left and above the original one. This first order dominance indicates that whichever poverty index or poverty line is selected, measured poverty will be greater for the unweighted distribution. This adds further support to our conclusion that the original uncorrected data yield overestimates of poverty in C6te d'Ivoire for 1985 and 1986.

Lastly, as a methodological point, the revisions in poverty estimates following the application of the corrective H-S weights is the net results of two effects, operating respectively on the mean and the distribution. Because household size is related to the level of expenditures, the re-weighting changes the mean level of expenditures (see previous section). However, since large households are located along the distribution with different frequency, the re-weighting will also alter the distribution (even if there were no change in the mean). It is possible to compute the contribution of each effect on the change in Poa. Such computations showed that the mean effect dominates and is negative (i.e., it reduces Pa). The signs and magnitudes of the distribution effects vary across regions. As we have seen, the net effect in most cases was to reduce the estimate of poverty incidence.

In summary, more than was the case for the estimate of household expenditures, the measured incidence of poverty is significantly affected by sampling bias in the CILSS. For several socio-economic groups and regions, uncorrected figures over-estimate poverty and extreme poverty by 20-30 percent - biases which could well be the difference between whether or not such groups become targets for government programs to alleviate poverty. Moreover, the removal of sampling bias leads to a reversal of the originally observed trend - in poverty change between 1985 and 1986 thus potentially leading to a different assessment of policies conducted over the period.

29
# **2.2  Sustainable Timber Supply**

Much of the literature on direct-use benefits of the Amazon forest is associated with extraction of forest products. Timber represents the largest value component of these (IBGE, annual; Wunder et al., 2008). It is recognized however that most timber extraction in the Amazon is accomplished using rotations insufficient to restore original productivity. In Brazil, IBAMA approved forest management plans require only 25-year rotations. According to research largely undertaken in the Eastern Amazon (Uhl , et al., 1991; Almeida et al., 1995; Barreto et al., 1998; Merry, et al., 2009), a rotation of at least 30 years, and the employment of reduced impact logging (RIL) techniques (Bacha and Rodrigues, 2006; Barreto et al., 1998; Killeen and Portela, 2011) are desirable to ensure both profitability and species conservation in the long run. Data for variation in biomass volumes across much of the Amazon (Saatchi et al., 2006) have made it possible to establish regional values for potential timber harvest (Merry et al., 2009).

For the Madre de Dios región in Peru, Giudice et al. (2012) developed a rent model that estimates optimal harvest fees, compares scenarios of timber harvest for the next 20 years, and calculates potential revenues to the State. They concluded that annual forest revenues to the State could increase from the current US$ 1.0 million to an average of US$ 23.4±1.4 million per year if the fee structure suggested by the study were adapted. The study also concluded that timber harvesting in Madre de Dios could increase by up to 200% over the next 20 years, without jeopardizing the integrity of conservation areas.

An attempt at an overall valuation of sustainable timber production potential in national and state forests in the Brazilian Amazon, by Medeiros et al (2011), concludes that this could generate net revenue on the order $1.2-2.2 billion at current timber prices.

IMAZON has also conducted detailed studies of the timber frontier, viability of sustainable harvesting, transport costs, etc. It will be difficult, however, to predict the future value of sustainable timber supplies. It may be assumed that scarcity coupled with more effective regulatory control to keep loggers out of protected areas, will increase this value, but substitution by plantation stocks may make natural timber management less attractive.

IMAZON has surveyed all functioning enterprises in timber extraction poles in the Brazilian Amazon every five years since 1998 (including 2004 and 2009), reporting that although some consolidation has occurred, there were still about 2,225 timber operations throughout the region at the end of the last decade, mostly small in scale averaging 3,400 m<sup>3</sup> /yr processed volume (Barreto et al., 2005). Although there has been a general production decline and reduction in the number of logging enterprises, the average value of gross timber revenues is higher per m<sup>3</sup> . Volume declining per firm suggests depletion of accessible timber stock, which may be related to the decline in deforestation activity over the past 7 years.

11 | P a g e
15 NPLs. When the government-owned banks variable is added to the right-hand side, this latter effect loses significance (Table 2). Private monitoring has significant effects across the board in Table 1 and with the same signs found in earlier research, but with government-owned banks (Table 2) only remains significant in the regression of overhead costs.<sup>7</sup> These results differ somewhat from BCL (2006), where the addition of government ownership also decreased the size of the coefficients but for the most part they remained statistically significant. One difference here is that the sample is smaller due to differences in the construction in the indexes. In our earlier work, the use of principal components in constructing the indexes allowed us to use observations even when a few questions were not answered, and more importantly we were able to fill in a large number of missing values or clearly incorrect responses by going back to the country authorities (after the survey results had already been posted). In this paper, we took the raw data ‘as is’ from the World Bank website, and for countries with missing values, had to drop that observation as we were producing the indexes by summation, and summing responses with different numbers of replies would clearly lead to a mischaracterization of the data. This approach was dictated by the desire to be consistent with the Survey IV results; although we will attempt to fill in missing values in BCL (2012) for that survey, that work is not yet complete.

The difference in the index construction did not change materially the results on official supervisory powers, which remains insignificant with or without the addition of government-owned banks. That supervision has no impact on bank fragility, as measured by NPLs, thus remains troubling. Tighter entry requirements are linked with an increase in efficiency, even when government banks are added, and restrictions on banks’ activities lead to lower bank development and higher interest margins in both Tables 1 and 2. Interestingly, restrictions on activities are not linked with greater stability here, when stability is measured by NPLs, but this coefficient was significant in our

> 7 This result likely is due to the highly negative correlation between private monitoring and government ownership of the banking sector. As is easily understood, it makes no sense for the private sector to bother monitoring state-owned banks, as the risk is the same across all such banks.
of categories) and to no downward movements during this period. As mentioned, these results are based on the lower-bound estimate and provide information on a low mobility (conservative) scenario (see Table A5 and Table A6 for upper bound estimates); true transitions will be in between the two bounds.

**During 2006 and 2011 upward mobility continued to be strong.** Around 9 percent of individuals moved out of poverty into the next higher economic class, and an also large share (13 percent) made it to living between $5 and $10/day (Table 2). Overall, those living above $5/day, including the middle class, are substantially higher in this period. Based on lower bound estimates, downward mobility took place in less than 1 percent of the population.

**Table 1. A large share of people escaped poverty in the early 2000s**

Transition matrix for intra-generational mobility in Moldova, 1999 to 2004 (percent of individuals)

||||**Des**<br>|**tination (2004)**<br>|||
|---|---|---|---|---|---|---|
|||**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|**Total**|
||**<$2.5**|**48.2**|29.0|0.8|0.0|_78.1_|
|**Origin**|**$2.5-$5**|0.0|**5.8**|9.7|0.2|_15.7_|
|**(1999)**|**$5-$10**|0.0|0.0|**2.0**|2.4|_4.4_|
||**$10+**|0.0|0.0|0.0|**1.8**|_1.8_|
||Total|_48.2_|_34.9_|_12.5_|_4.4_|_100.0_|

Source: Calculations based on HBS data.

Note: Results presented are for the lower-bound mobility estimates using the synthetic panel methodology by Dang et al (2011). See Table A5 for upper-bound results.

**Table 2. Movements out of poverty continued in the second half of the decade**

Transition matrix for intra-generational mobility in Moldova, 2006 to 2011 (percent of individuals)

||||**D**<br>|**estination (2011)**<br>|||
|---|---|---|---|---|---|---|
|||**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|**Total**|
||**<$2.5**|**13.9**|9.0|0.0|0.0|_22.8_|
|**Origin**|**$2.5-$5**|0.1|**30.2**|13.1|0.0|_43.4_|
|**(2006)**|**$5-$10**|0.0|0.3|**22.8**|4.3|_27.4_|
||**$10+**|0.0|0.0|0.1|**6.2**|_6.3_|
||Total|_14.0_|_39.5_|_36.0_|_10.5_|_100.0_|

Source: Calculations based on HBS data. Note: Results presented are for the lower-bound mobility estimates using the synthetic panel methodology by Dang et al (2011). See Table A6 for upper-bound results.

**Focusing on the crisis period more closely – time period between 2008 and 2011 - shows a slight decrease in upward mobility.** Compared to a total of 9 percent of individuals moving out of poverty from 2006 to 2011, only 3.8 percent made it out in this narrower period covering the crisis (Table 3). The share of the population moving up from other economic categories was also smaller. Overall, a larger share of people can be categorized as “stayers” in the period 2008-2011, as downward economic mobility remained very low.  These results stand even if adjusting for the number of years covered in each period.

19
Besides using our analysis of government banks to assess the helping-hand/grabbing-hand views, we make two specific improvements on existing studies of government-owned banks. First, we use data collected from each country's bank regulatory agency. Thus, the data cover all banks and the definition of "government owned" is consistent across countries. Second, we control for differences in the regulatory and supervisory environment in assessing the links between government ownership and bank development, performance, and stability. For instance, we examine whether government ownership is better than private ownership with a weak regulatory environment.

# 111. **Data**

## **III.A. The Dataset**

We designed and implemented a survey funded by the World Bank to collect information on bank regulations and supervisory practices for more than 107 countries. Barth, Caprio, and Levine (2001b) describe the survey questions and data collection process in detail. The completion of the survey entailed numerous steps: collecting initial survey responses, reconciling different responses from different officials in the same country, cross-checking the data with a survey by the Office of the Comptroller of the Currency (OCC), which included some overlap in the information requested, further reconciling any inconsistencies, and checking our data with information collected by the Institute of International Bankers, and the Financial Stability Forum's Working Group on Deposit Insurance, which provided input on the accuracy of responses for some deposit insurance systems. Thus, in numerous cases, we repeatedly communicated with the authorities to obtain accurate information.

The regulatory and supervisory data are primarily from 1999, with some responses in late 1998 and others in early 2000.10 In some cases, we group the responses to individual questions into aggregate indexes that we define below. This paper uses those countries with more than one million people and confirms the results when restricting the sample to countries with more than 200,000 people. We have made the data available at the following website: www.worldbank.org/research/interest/intrstweb.htm.

'° Of the 107 responses received, 13 were received in November 1998, 65 were received in 1999, and 29 in 2000, with 19 15
Policy Research Working Paper 8782

# **Abstract**

The Three-Gap Model examines the determinants of low-quality health care by examining the patterns and determinants of three gaps. Using four measures of performance—target performance, actual performance, capacity to perform, and knowledge to perform—this paper defines three gaps for each health worker: the gap between target performance and what they have the knowledge to do (the know gap), the gap between their knowledge and their capacity to perform (the know-can gap), and the gap between their capacity and what they actually do (the can-do gap). The paper demonstrates how the patterns of these gaps across health workers in a sample can be used to diagnose failures in the system as well as evaluate the outcomes of policy experiments. Using data on pediatric care from hospitals in Liberia, the paper illustrates how the model can be used to investigate the potential for improvements in the quality of care from several possible policy interventions. The analysis of the relationships between these gaps across health workers in a health system help to paint a better picture of the determinants of performance and can assist policy makers in choosing relevant policies to improve health worker performance.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at ibnatf@terpmail.umd.edu, kleonard@arec.umd.edu, and rmohammed@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team