rafmacalaba/datause-encoder-data huggingface dataset:
countries. In Peru, the share of loans held by foreign banks grew throughout the late 1990s, while in Colombia it declined slightly. Still, foreign banks held only about a quarter of total loans in both countries throughout most of the period.

By the end of the period, foreign banks held over 50 percent of Argentina's banking sector loans, and almost 45 percent of Chile's. Chile's foreign bank share climbed throughout our sample period. In Argentina, most of that growth occurred prior to our sample period. However, data on lending to small businesses for Argentina was only available since 1998, and thus our sample period is shorter than that for Chile. The key point, however, is that foreign banks comprised a substantially larger share of bank loans in Argentina and Chile than in Colombia or Peru.

Judging from the U.S. empirical evidence one might expect that small borrowers would fare relatively worse in Argentina and Chile. Those countries experienced greater foreign penetration and had much larger banks than in Colombia or Peru. Moreover, in Argentina, foreign entry coincided with a massive consolidation in which many small banks left the market either through merger or failure. Section V tests these conjectures explicitly.

# **III. The Data**

To analyze domestic and foreign bank lending to small businesses, we assembled a comprehensive bank level database for Argentina, Chile, Colombia, and Peru during the 1990s. These countries' central banks and bank superintendencies were our main sources of data. We gathered information on the origin (foreign or domestic) of each bank in these countries throughout the sample. Moreover, we identified whether foreign banks entered into the banking system through a process of mergers and acquisitions with previously existing institutions or by creating a new institution or _de novo_ bank. In general, the first type of entry is likely to result in higher bank concentration, especially if foreign banks acquire large domestic institutions. Second, we gathered data on the distribution of bank lending by size. When information about small business loans was not available as a separate category, small business loans were defined by the size of the loan, instead of borrower size. Finally, we collected 6
The basin’s population, which amounts to nearly fifteen million, has increased by 53.4% over the past 30 years and represents 42.4% of East Java’s population (2000) with a density of 1,249 (Table 3). In 2001, the population of municipalities accounted for 25.3% of the total population in the Brantas River basin, while that for East Java Province was 10.7%. This is due to the presence of the two important industrial cities of Surabaya and Malang in the basin, which have grown to 2.5 million and 743,0000020inhabitants, respectively. With these major cities, the GDP for the basin has increased 225 fold in Rupiah terms over the past 30 years and in 2000 contributed 56% to East Java’s GDP. In that year, industrial production in the basin – a significant 77% of East Java’s entire industrial production – was of the order of RP 7.724 trillion (~ US$ 858 million). The trend of GDP in East Java and the Brantas Basin is shown in Figure 2.

It should be noted that though industrial employment has grown from a mere 30,000 in 1970 to 959,300 in 2000, the bulk of basin residents still work in the agricultural sector with small- to medium-sized land holdings. Irrigators comprised 76% of surface water diversions in the Brantas in 2000, while industrial and domestic uses diverted 9% and 14%, respectively. (PJT I 2000 data).

**Table 3: Population**

|**Location**|**Area**<br>**km**<sup>**2**</sup>|**Population**<br>**2000**|**Density**<br>**(Per km**<sup>**2**</sup>**)**<br>**2000**|
|---|---|---|---|
|Indonesia|1,920,000|207,600,000|108|
|Java|132,000|121,293,000|919|
|East Java|48,000|34,766,000|724|
|Brantas Basin|11,800|14,735,200|1,249|
|Surabaya City|300|2,445,000|8,150|

**Source:** Central Bureau of Statistics, Indonesia

**Figure 2: GDP Trend in East Java and Brantas Basin**

<!-- Start of picture text --> Gross Domestic Product (Agriculture) Gross Domestic Product (Industry) Gross Domestic Product (Total)<br>(billion Rp.) (billion Rp.) (billion Rp.)<br>40000 50000 200000<br>E. Java Brantas Basin E. Java Brantas Basin E. Java Brantas Basin<br>40000<br>30000 150000<br>30000<br>20000 100000<br>20000<br>10000 50000<br>10000<br>0 0 0<br>1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000<br><!-- End of picture text -->

**Source:** Ramu 2004

10
- [4] Bell, C., and van Dillen (2012), ‘How does India’s Rural Roads Program Affect the Grassroots? Findings from a Survey in Upland Orissa’, Policy Research Paper No. 6167, World Bank, August.

- [5] Binswanger, H., Khandker, S., and Rosenzweig, M. (1993), ‘How Infrastructure and Financial Institutions Affect Agricultural Output and Investment in India’, _Journal of Development Economics_ , 41(2): 337-366.

- [6] Escobal, J., and Ponce, C. (2002), ‘The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor’, GRADE, Working Paper 40-1, Lima.

- [7] Fan, S., Hazell, P., and Thorat, S. (2000), ‘Government Spending, Growth and Poverty in Rural India’, _American Journal of Agricultural Economics_ , 82(4): 10381051.

- [8] Foster, A., and Rosenzweig, M. (2004), ‘Agricultural Productivity, Growth, Rural Economic Diversity and Economic Reforms: India, 1970-2000’, _Economic Development and Cultural Change_ , 52(3): 509-42.

- [9] Jacoby, H.G., and Minten, B. (2009), ‘On Measuring the Benefits of Lower Transport Costs’, _Journal of Development Economics_ , 89(1): 28-38.

- [10] Khandker, S.R., Bakht, Z., and Koolwal, G. B. (2009), ‘The Poverty Impact of Rural Roads: Evidence from Bangladesh’, _Economic Development and Cultural Change_ , 57: 685-722.

- [11] Murty, M.N., and Goldar, B.N. (2007), ‘Estimation of National Parameters for Project Appraisal in India’, mimeo, Institute of Economic Growth, Delhi, April.

- [12] Spriggs, J. (1977), ‘Benefit-Cost Analysis of Surfaced Roads in the Eastern Rice Region of India’, _American Journal of Agricultural Economics_ , 59(2).

- [13] van Dillen, S. (2008), ‘Income and its Variability in a Drought-prone Region: Seasonality, Location and Household Characteristics’, _European Journal of Development Research_ , 20(4): 579-596.

- [14] Warr, P. (2010), ‘Roads and Poverty in Rural Laos’, _Pacific Economic Review_ , 15(1): 152-169. DOI: 10.1111/j.1468-0106.2009.00494.x.

- [15] World Bank (2010), ‘Project Appraisal Document (PAD)’, Report No. 57081-IN, Washington DC, July.

27
**Figure 8: Comparison of runoff generated by WatBal and the UNH data for 97 ‘food production units’ in Africa**

<!-- Start of picture text --> 1800<br>y = 0.9737x<br>R 2 = 0.8957<br>1600<br>1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>0 200 400 600 800 1000 1200 1400 1600 1800<br>UNH - runoff (mm)<br>WATBAL - runoff (mm)<br><!-- End of picture text --> 52
where the trade balance _TBt_ is financed by the net flows of capital, In an economy which is open on both trade and financial fronts, imports and total domestic output net of exports is allocated between total consumption and investment, where the difference between exports and imports are balanced by the financial flows as indicated by equations (23) and (24). The rest of the framework, such as the optimisation problem of the Ricardian and the liquidity-constrained households, firm’s profit maximisation behaviour, and the permanent and transitory shock structures remain similar, as in the closed economy framework. By normalising the variables with respect to the permanent component of productivity at period _t_ –1, the detrended system of equations are obtained. The Supplemental Appendix S3 contains the detrended system of equations pertaining to the open economy.

# _Calibration to Indian Data_

In order to calibrate the open economy, value of the interest rate elasticity of indebtedness is set to 0.001, as in Aguiar and Gopinath (2007). The steady state level of debt to GDP ratios for the pre- and post-reform periods are set to the average values of the external debt to GDP ratios in 1971–91 and 1992–2012, respectively. The respective values are 16.30% and 21.39%.<sup>6</sup> The value of the risk-free world interest rate is set to satisfy the condition * that β (1 + _R_ ) = μ _g_ , where μ _g_ − 1 is the mean growth rate of the permanent component of TFP. The value of this parameter is set to 2.79% based on the estimated permanent component of TFP as outlined in the Consumption Volatility

> 6 The annual series of external debt are sourced from WDI. The data spans from 1971–2012 and are in current US$. The GDP data, also in current US$, are sourced from WDI.

25
<!-- Start of picture text --> No obstacle Minor obstacle Moderate obstacle Major obstacle Very severe obstacle No obstacle Minor obstacle Moderate obstacle Major obstacle Very severe obstacle<br>excludes outside values excludes outside values<br>No obstacle Minor obstacle Moderate obstacle Major obstacle Very severe obstacle<br>excludes outside values<br>8 100<br>6<br>80<br>4<br>60<br>2<br>Total Factor Productivity Capacity Utilization (%)<br>0 40<br>-2<br>20<br>40<br>30<br>20<br>10<br>Losses due to Outages (% of annual Sales)<br>0<br><!-- End of picture text --> Figure 1: Degree of power constraints, declining total factor productivity and capacity utilization in %, and increasing losses in % of total sales <!-- Start of picture text --> No obstacle Minor obstacle Moderate obstacle<br>80<br>60<br>40<br>20<br>0<br>AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR<br>Major obstacle Very severe obstacle<br>80<br>60<br>40<br>20<br>0<br>AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR<br>excludes outside values<br>Losses due to Outages (% of annual Sales)<br><!-- End of picture text --> Figure 2: Breakdown losses by degree of power constraints and by region 14
columns 1 and 2 demonstrate a negative and statistically significant relationship between the degree of competition facing our partner bank and the additional probability of a thin file applicant borrowing from our partner bank if they exceed the threshold. The estimates in column 2 imply that increasing the level of competition by one standard deviation would correspond to an additional 9.1 percentage point increase in the likelihood a thin file applicant took a loan from our partner bank as a result of being marginally over the threshold.

The pattern for borrowing from competing financial institutions is reversed, albeit less precisely estimated. The estimates in columns 3 through 6 demonstrate a positive, though not statistically significant relationship between the degree of competition facing our partner bank and the probability of a thin file applicant borrowing from a competing lender around the threshold. Table A7 presents results from our alternative measure of competition, log NBFI branches per capita. The results are qualitatively similar.

Table A8 presents the effects on our partner bank’s and on competing lenders’ profits, measured using the Inverse Hyperbolic Sine (IHS) function. Note that profits for our partner bank and its competitors vary across the threshold due to 1) the differential likelihood that borrowers are approved for a loan, and 2) the differential likelihood that they accept loans from each lender. Columns 3 and 4 demonstrate that thick file borrowers just above the cutoff are on average 242% (p-val 0.00) more profitable for our partner bank than those just below the cutoff. On the other hand, estimates of our partner bank’s additional profits from thin file borrowers are much smaller and not statistically significant. But for NBFIs, thin file borrowers just above the threshold are 301% (p-val 0.04) more profitable than those just below the threshold. Therefore, most of the additional profits resulting from screening new thin file borrowers accrued to competing financial institutions.

Table A9 presents estimates of the effect of the EFL threshold on the resulting defaulted balances for borrowers at our partner bank and its competitors. We find no evidence that marginally approved applicants that took loans from our partner bank were differentially reliable from those who took loans from competing financial institutions.

13
# **Redefining the Quality Framework**

The approach employed here provides a broader and more inclusive perspective on education planning and reform than is frequently invoked in traditional educational studies. It builds on the theories and findings from related studies on educational quality. By analyzing the lessons and experiences across countries during the last two decades, the paper addresses gaps and hindrances in improving education quality and thus develops a new framework of quality education.

What are the major constraints against achieving the quality goal across countries? Studies suggest that three central institutional incentives affect learning outcomes: (i) choice and competition, (ii) school autonomy and (iii) school accountability (Bruns, Filmer and Patrinos 2011; World Bank 2003). These institutional incentives were reconceptualized as key policy levers of education systems; that is, autonomy, accountability and assessment – or, the ‘three A’s’ (World Bank 2010; Patrinos 2010a). Apart from the above three institutional factors, three structural quality elements are also pivotal to achieving the goal of quality, yet they are often neglected. These are Attention to teachers, Attention to early childhood development, and Attention to culture, each addressing a dimension contingent upon successful education reform. Together, these **_<u>6As</u>_** represent an input-output-outcome approach tackling the issue of quality. In this framework, quality is viewed as more than a goal; it also connotes aims, measures and destinations in concrete terms. Moreover, it integrates institutional, structural and contextual considerations. Policy makers may use these quality drivers to guide decisions about important education subsystems to achieve improved learning. Similarly, education projects and programs could be designed following the **_<u>6As</u>_** in line with the vision of the education authorities. The **_<u>6As</u>_** framework is illustrated in Figure 2.

4
with shared kitchen and electrical meter. After matching with An Thinh’s customer records, we verified that 267 households or 16 percent of the population were unconnected to piped water as of July 2015, at which time we offered them free connections (i.e., waived the connection fee). While almost all of these 267 households eventually did connect, only about half did so in time for the baseline survey (see below).

**Pre-experiment price linearization** Prior to the price randomization, starting in July 2015, we “linearized” the water tariff schedule once and for all to avoid having to deal with self-selection of households onto price blocks (as in, e.g., Reiss and White 2005, McRae 2015, and Szab ́o 2015). At the time, the official schedule had three blocks: for consumption below 10 m<sup>3</sup> , the price was VND5,300 per m<sup>3</sup> , increased to VND7,200 per m<sup>3</sup> for any consumption above 10 m<sup>3</sup> and below 20 m<sup>3</sup> , and again to VND8,000 per m<sup>3</sup> for any higher consumption.<sup>7</sup> With the linearization, every household in the three villages was charged a uniform VND5,300 per m<sup>3</sup> price and the water utility was reimbursed for the revenue loss relative to the block-price schedule. This linearized price schedule prevailed throughout the experiment, with randomized discounts applied at various points as described below. In June 2017, however, provincial authorities raised the water price to a uniform VND7,950 per m<sup>3</sup> , a 50 percent increase, which we passed on to all consumers and which was broadly announced by An Thinh.

**Baseline data collection** We collected baseline data in October 2015. Out of the universe of 1,526 connected households in that month, including some of those that we had offered free connections to in July, the survey covered 1,488. Reasons given for non-participation were refusal (11 households), family events (10 households), commercial water users mis-registered as residential users (8 cases), physical incapacity (6 households), and the remaining 3 cases were households who no longer used piped water. The survey questionnaire collected data on household demographics, assets, income, and consumption, as well as a module on domestic water use.

**Quarterly monitoring** In November 2015, we began a series of “monitoring” surveys, implemented at the time of bill payment, in which we collected quarterly information on household demographics (whether households had family or friends returning/staying with them and for

> 7The exchange rate at the time was approximately US$ 1 = VND21,000.

6
20

# **6. Conclusions**

This paper has attempted to make an initial contribution to the analysis of the determinants of corruption at the firm level in Mauritania, using both descriptive and econometric analysis, on the basis of the ICS data for 2005. The paper builds on the work conducted in Uganda by Svensson (2003) and in Indonesia by Kuncoro (2006). The overarching aims of the present work were to understand whether Mauritanian firms deem corruption as an obstacle to operate and grow; identify the profile of firms that are more likely to make informal payments; and quantify the size of these payments. The analysis conducted in this paper has yielded the following key results:

- Perceptions of corruption can be potentially misleading. As illustrated above, in the case of Mauritania, data on the perception of corruption at the firm level show that: (i) corruption is not considered to be one the most taxing factor impeding firms’ growth in Mauritania, and: (ii) the perception of corruption as an obstacle to growth is significantly lower that in neighboring countries. However, the cost of corruption to firms is significant – both when expressed in percentage of firms’ annual sales or contract value – and higher than in the comparator group’s countries. By broadening the analysis beyond perception, it is apparent that corruption is internalized by firms and considered common, accepted practice in Mauritania.

- Econometric evidence on _bribe propensity_ and _intensity_ suggest that medium-size firms are the ones that suffer most from corruption in Mauritania. Larger firms are more established and connected, do not fear exiting the market and less likely to be harassed.  Smaller firms are less visible and may be able to escape the control of public official, by operating largely in the informal sector. The results add value to the hypothesis that these firms are disadvantaged in two fundamental ways in Mauritania: first of all, they are squeezed by the presence of powerful business groups/large firms which have de facto monopolies in important sectors of the economy. Secondly, they are the most likely firms to pay bribes and pay the highest amounts in percentage of their total annual sales, which places an additional burden on their ability to grow.
32 To give a better idea of the path of relative price changes over time, Figure 4 presents cumulative relative price changes for main CPI categories as well as selected items from these categories.

The main messages from Eiga3 and Fig.4 can be summarized as follows:

# * **The biggest relative price shifts took place between 1989-1991.**

As Fig 3 and Fig. 4 suggest after 1991 no significant changes in the top and bottom relative price shifts occurred. Graphs confirm the notion that following the period of profound changes prior to 1992, most prices have stabilized and the process of adjustments that is still going on in some cases 3 is by far less abrupt than in the initial stages of transition.

# * **Increases in the prices of utilities have undoubtedly led[ the inflationary process.**

Out of _5_ biggest relative price increases, 3 took place in the sector of utilities. Prices of utilities rose by as much as 5 (gas) to 5.4 (central heating and water supply) times more than the CPI index. Pricing of most utilities are up till now controlled by the government which sets the pace of upward adjustments. (The issue of controlled prices will be taken up in more detail in the next section.)

# *** Relative prices of staple foods have risen significantly.**

Prices of bread and dairy products (see Table 7 below) have outpaced inflation more than twice. This substantial relative price increase may be partially due to extraordinarily low prices of these foods before 1989 reflecting high subsidies which were part of the government's social safety net at that time.

*** Commodities from the category** _electronics_ **experienced by far the largest and most permanent decline**<sup>**36**</sup> in its relative price.

> Slow gradual upward adjustments continue in the case of for example: central heating and hot water supply.

> 36 The relative price of the category _electronics_ continues to decline systematically and reached the lowest value of RP=0.25 at the end of Mar-97
- **iii** -

# **Table of Contents (Cont.)**

# Page No.

|REFER|ENCES**..................................................**<br>BOXES|38|
|---|---|---|
|Box 1.|Leasing Institutional Infrastructure from Abroad .....................................|..............<br>3|
|Box 2.|Chilean Superintendency of AFP ...................................................|3|
|Box 3.|EDI Chile (Electronic Data Interchange in Chile) ....................................|..............<br>17|
|Elox 4.|Insurance in Bolivia ..................................................|19|
|B3ox 5.|Voluntary Saving and Cooperative Pension Schemes in<br>Costa Rica and Paraguay ..................................................|26|

# ANNEX INSTITUTIONAL INFRASTRUCTURE **AND** PENSION SERVICES

|A. General Framework .................................................|Al|
|---|---|
|B. Application of the Framework to Pension Services Infrastructure<br>and Pension Services .................................................|A3|
|C. The Small Country Case: Selected Types of Institutional||
|Infrastructure and Pension Services .................................................|A7|
|D. Options for Development of Institutional Infrastructure||
|to Support Pension Reform .................................................|AIO|

# **TABLES**

Table I. Institutional Infrastructure for Pension Services .A4