rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Number of Projects by IEG Rating & FCS status 2001-12<br>So<br>8wm<br>sN<br>re<br>58<br>Bo<br>28Eas<br>ire)red<br>:<br>woN Po i |<br>°<br>1 2 3 4 5 6<br>IEG Outcome Rating<br>HEE Always+Partial FCS [EE Never FCS<br><!-- End of picture text -->

<!-- Start of picture text --> FCS IEG Outcome Ratings by Sector Boards<br>£aD<br>ised<br>o& 2<br>6<br>ot<br>2<br>3°<br>®<br>a Oo<br>€ € €& & € © @ S$ FF SY SK<br>Sector Boards<br>ARD - Agriculture & Rural Dev., ED - Education, EMT - Energy & Mining<br>ENVPS - Environment, FPD - Financial & Private Sector Dev., HE - Health, Nutrition & Pop.,<br>UD-- PublicUrban SectorDev., WAT Gov.,- SDV Water - Soc. Dev., SP - Soc. Protection TR - Transport,<br><!-- End of picture text --> <!-- Start of picture text --> IEG Outcome Ratings by Sector Boards<br>Z8ED<br>EMT &&<br>ENV os<br>* <2<br>FPD 8%<br>GIcHE oe<br>PS ¢$ e<br>:TRsP z&&¢<br>up oe<br>wat S%<br>&<br>4% Stlstactory Outcorne Rating<br><!-- End of picture text --> <!-- Start of picture text --> Non-FCS IEG Outcome Ratings by Sector Boards<br>£oT<br>is<br>@oE<br>robe<br>re<br>2<br>ot<br>On<br>cS<br>oO<br>€ € €& & € © @ S$ FF SY SK<br>Sector Boards<br>ARDENV-- Environment, Agriculture & FPDRural - FinancialDev., ED - Education,& Private SectorEMT Dev.,- Energy HE & - Health,Mining Nutrition & Pop.,<br>PSUD -- PublicUrban SectorDev., WAT Gov.,- SDV Water - Soc. Dev., SP - Soc. Protection TR - Transport,<br><!-- End of picture text -->
22

**Figure 20:  Average Maturities and Cost of External Financing by Size of Firms**

<!-- Start of picture text --> Maturity composition of Cost of debt borrowing for firms of<br>external financing structure different sizes<br>100%<br>50%<br>39%<br>80% 40%<br>60% 30% 23%<br>16%<br>40% 20% 11% 13%<br>20% 10%<br>0% 0%<br>Mega Large Medium Small Micro Mega Large Medium Small Micro<br>Share of LT liabilities Share of ST liabilities Weighted average cost of debt borrow ings<br>Sources: SBIF, FECUs, auther’s estimates<br><!-- End of picture text --> A major obstacle to increased access to commercial loans by micro firms is the overreliance of financial institutions on real estate as collateral. Although Chilean legislation provides for several security mechanisms, mortgages ( _hipotecas_ ) over immovable assets is the security preferred by financial institutions. These are governed by the Civil Code, and are typically authorized by a notary and registered at the Registry of Mortgages and Liens of the Real Estate Register of the community where the property of the collateralized immovable asset is registered. Not all Real Estate Registries are interconnected and some use the traditional “book’s method”. However, users of the system are generally satisfied with its reliability<sup>9</sup> . In addition, most costs associated with the creation and registration of mortgages are considered reasonable<sup>10</sup> .

Financial institutions also accept pledges on movables, but at a higher interest rate than with real estate collaterals because various difficulties with the related formalities increase the risk of such securities for banks. Pledges ( _prendas_ ) on movable assets (inventories, receivables, machinery, etc.) are considered less secure guaranties<sup>11</sup> , so that

> 9 In recent years, and particularly since 1995, in Santiago the registration system has been modernized to a certain extent, incorporating computer technology to the recording process, leading to enhanced efficiency and transparency. Nonetheless, inquiry mechanisms are still quite antiquated and cumbersome. In fact, there are no computerized mortgage inquiry mechanisms (only certificates). However, an automation project is now under study and expected to improve service.

> 10 The fees charged by the registrars are fixed by the Ministry of Justice. At present, these fees have a ceiling of CH$259,000 for acts or contracts in excess of CH$128,000,000. In the case of indeterminate acts or contracts, fees equal 2 or 3 per thousand of the fiscal appraisal value of the asset in question and, for want of such a value, they are set at CH$2,585.

> 11 Several interviewed lenders mentioned pledges as “second class guaranties”.
located. Additionally, studies based on surveillance data in other areas of Kenya find large drops in malaria rates. O’Meara et al. (2008) find that in Kilifi, Kenya hospital admissions for malaria decreased from 18.43 per 1000 children in 2003 to 3.42 in 2007. Okiro et al. (2007) find a similar decline across coastal Kenya.

_Maize yields_ . The MV sites experienced striking increases in maize productivity, from 1.9 to 5.0 tons per hectare in Sauri, 0.8 to 3.5 tons per hectare in Pampaida, and 2.2 to 4.5 tons per hectare in Sauri. The data needed to track maize productivity trends for comparison areas is not generally available. However, the Tegemeo Institute in Kenya has an ongoing panel survey of rural agricultural households in Kenya that makes it possible to follow increases in productivity over time. Kibaara et al. (2008) report that overall maize yields in the Western Lowlands region (near Sauri) were about 1.25 tons per hectare in 2007,<sup>12</sup> which is much lower than the 2008 yield in Sauri. This suggests that Sauri started out, before the intervention, with abnormally high maize yields for its region. Kibaara et al. (2008) also show that maize yields more than doubled across the Western Lowlands region, outside the MV intervention site, between 2004 and 2007—due to roughly a doubling in fertilizer usage, high-yielding variety usage, and the number of varieties planted, all of which occurred outside the MV intervention site during the same period.

_Child mortality_ . Although the MVP protocol (MVP 2010) stipulates that under-five child mortality is the primary indicator for the evaluation, child mortality rates are not included in the mid-term evaluation report. Because they calculate these rates for five-year periods preceding the data of the survey, it is not yet possible to calculate these rates for a pure period of MVP interventions. The DHS data, however, can tell us the child mortality trends in the MVP countries. The most recent DHS surveys show large declines over 2003-2008/09: from 114.6 to 73.6 per 1000 in Kenya, 111.2 to 80.0 per 1000 in Ghana, and 200.7 to 156.8 per 1000 in Nigeria.<sup>13</sup> Any future rigorous impact evaluation of the MV intervention will need to take account of these large changes occurring around the intervention sites.

> 12 Kibaara et al. (2008, Table 8) shows a yield of 0.506 tons/acre in Western Lowlands in 2007 (that is, 1.25 tons/hectare). In 2004 it was 0.231 tons/acre.

> 13 The child mortality figures are for the five-year period preceding each survey.

15
Preston, Samuel H. (1980), “Causes and Consequences of Mortality Decline in Less Developed Countries during the Twentieth Century.” In Richard A. Easterlin (ed.), _Population and Economic Change in Developing Countries,_ Chicago: University of Chicago Press.

Williamson, Jeffrey G. (1984), “British Mortality and the Value of Life: 1781-1931,” _Population Studies_ 38 (March): 157-72.

Williamson, Jeffrey G. (1997), “Globalization and Inequality, Past and Present,” _World Bank Research Observer_ 12 (August): 117-35.

Williamson, Jeffrey G. (2002), “Land, Labor and Globalization in the Third World 18701940,” _Journal of Economic History_ 62 (March): 55-85.

34
<!-- Start of picture text --> {e)<br>09<br>S<br>ne)<br>aS)<br>°<br>2<br>Q©<br>©<br>Cc fe) ° °<br>S<br>Cc fe)<br>S o}<br>9}<br>fal fe)<br>fe)<br>O°<br>ww, °<br>1}<br>fe)<br>2<br>-.05 0 .05 1 15<br>Slant against women, good vs bad measure<br><!-- End of picture text -->
smaller farmers generally over reporting their land relatively more than larger farmers, and with the largest farmers actually under reporting land size.

These findings go in the same direction as the results of De Groote and Traorè (2005) for Southern Mali. As stated above, De Groote and Traorè find that there is an approximate linear negative relationship between measurement error and plot size. Farmers have a tendency to overestimate small plots (which they define as below 1 hectare<sup>4</sup> ) compared to larger ones, which they somewhat underestimate. Also, the GPS and self-reported plot area measures in our sample display a correlation of 0.97 (which drops to a more reasonable, but still respectable, 0.77 when we trim the right end of the distribution of a few very large plots), well above the 0.15 found by Goldstein and Udry (1999) in their Ghana dataset.

One additional issue in our data, that is immediately apparent from the visual inspection of the distributions of the two land measures in Figure 1, is the considerable tendency of respondents (or enumerators) to round their reported plot size to nearest acre or half acre. This „heaping‟ in the response pattern is not uncommon (Roberts and Brewer, 2001) but we suspect it may be particularly important in the case of land measurement since it is bound to matter proportionally more to the left of the distribution, as the same amount of rounding represents a larger percentage of the actual plot size.

Figure 1 also confirms that the two distributions are, overall, quite similar. However, while the means are not much different, at specific points the distributions deviate considerably, in a way that appears to be driven by heaping in the self-reporting distribution as opposed to a smooth curve for the GPS measure. Finally, the comparison of the two distributions appears to support the case for treating the GPS measure as the more accurate of the two.

The systematic patterns in the difference between land measurements we have observed above have the potential to introduce a bias in the estimation of agricultural/land productivity. Reasoning through the data it is apparent, as we already mentioned, that the kind of pattern we observe in our data should, if anything, generate a bias opposite in sign to that needed to corroborate the IR hypothesis. If small farmers report to be cultivating more land than they actually are, their „true‟ yields are actually even larger than what one would compute using self-reported land quantities. To explore that point descriptively, Figure 2 draws the relationship between deciles of cultivated land and farm yields (computed as the ratio of the value of agricultural production over farm size). The slope of both lines is negative, pointing to an inverse yield-farm size relationship in both cases, but the line is even steeper when the GPS land measure is used.

Table 3 summarizes farm yields computed using GPS and self-reported land areas. Farms are categorized as small, medium or large. Small farms, those cultivating landholdings smaller than1.45

> 4 1 hectare is equal to 2.47 acres.

7
motivated by a concern with preserving the integrity of the goalposts used for the SDGs and the Bank’s own twin goals – is in fact reasonably robust.

# **5. Results**

This section presents new estimates of global poverty based on the $1.90 line and the 2011 PPPs. In presenting the results, we highlight how the new estimates compare to the most recent estimates based on the $1.25 line and the 2005 PPPs. When considering the comparisons, it is important to note that there are many changing elements in the estimates. In addition to incorporating the new poverty line and the 2011 PPPs, the new results are based on a number of other updates to the data, such as new surveys, increased use of micro-data, changes to welfare aggregates, revised inter-temporal deflators, and updated population figures. While this paper will use the short-hand reference to the 2005 PPP and 2011 PPP poverty estimates, the comparisons will reflect differences in all the elements noted above. For example, the present update includes estimates for six countries for which international poverty estimates have not been available at all in the past.<sup>56</sup> These simultaneous changes to the data make it impossible to identify from the comparisons reported here, the precise isolated effect of introducing the 2011 PPPs and the $1.90 line in our analysis. In addition to the findings in this paper, more extensive analysis of recent trends in global poverty, and the outlook for the future, is available in World Bank (2015b) and Cruz et al. (2015).

# **_5.1 Updated country-level estimates of poverty_**

Although the updated poverty line of $1.90 in 2011 PPPs reflects the same real standard of living as the $1.25 line did in 2005 PPPs (on average across poor countries), the new line does of course lead to changes to poverty counts in individual countries. Figure 5 illustrates three examples of changes in country-level poverty estimates between the $1.25 line at 2005 PPPs and the $1.90 line at 2011 PPPs. For Armenia, the 2011 PPPs resulted in a revision of PPP-adjusted dollar value of consumption that was close to proportional to the change in the poverty line from $1.25 to $1.90, leading to very minor changes in poverty.<sup>57</sup> For Botswana, the revision of PPP-

> 56 Kiribati, Kosovo, Samoa, Solomon Islands, Tonga and Vanuatu have been added to the PovcalNet database in this latest update.

> 57 The direction and magnitude of the changes can also be assessed in terms of the difference between the 1.90 line and the “poverty equivalent” lines discussed in the preceding section. The poverty equivalent

34
|CRITICAL ELECTRICITY AND WATER SERVICES REHABILITATION PRO|J Fabrice Karl Bertholet|
|---|---|
|CRITICAL ELECTRICITY AND WATER SERVICES REHABILITATION PRO|J Fabrice Karl Bertholet|
|CRITICAL ELECTRICITY AND WATER SERVICES REHABILITATION PRO|J Fabrice Karl Bertholet|
|CRITICAL ELECTRICITY AND WATER SERVICES REHABILITATION PRO|J Fabrice Karl Bertholet|
|SECOND INDIGENOUS AND AFROECUADORIAN PEOPLES DEVELOPM|David Tuchschneider|
|SECOND INDIGENOUS AND AFROECUADORIAN PEOPLES DEVELOPM|David Tuchschneider|
|SECOND INDIGENOUS AND AFROECUADORIAN PEOPLES DEVELOPM|David Tuchschneider|
|SECOND INDIGENOUS AND AFROECUADORIAN PEOPLES DEVELOPM|David Tuchschneider|
|Matruh Resource Management(02)Project|Marie-Helene Collion|
|Matruh Resource Management(02)Project|Marie-Helene Collion|
|Matruh Resource Management(02)Project|Marie-Helene Collion|
|Matruh Resource Management(02)Project|Marie-Helene Collion|
|Matruh Resource Management(02)Project|Marie-Helene Collion|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|CL: Science for the Knowledge EconomyProject|Daniela Marotta|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|VN-Red River Delta Rural Water Supplyand Sanitation Project|Hoa Thi Hoang|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|National Education Assessment System|Amit Dar|
|CommunityWorks 2 Project|Radhika Srinivasan|
|CommunityWorks 2 Project|Radhika Srinivasan|
|CommunityWorks 2 Project|Radhika Srinivasan|
- **, , and J. Van Reenen** , “Dynamic Count Data Models of Technological Innovation,” _Economic Journal_ , 1995, _105_ (429), 333–344.

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- **Brambilla, I.** , “Multinationals, Technology, and the Introduction of Varieties of Goods,” 2006. NBER Working Paper No.12217.

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- **Eaton, J. and S. Kortum** , “Trade in Ideas: Patenting and Productivity in the OECD,” _Journal of International Economics_ , 1996, _4:3-4_ , 251–278.

- **Feenstra, R. and G. Hanson** , “Foreign Direct Investment and Relative Wages: Evidence from Mexico’s Maquiladoras,” _Journal of International Economics_ , 1997, _42_ , 371–394.

- **Griffith, R., Harrison R, and J. Van Reenen** , “How Special is the Special Relationship? Using the Impact of U.S R&D Spillovers on U.K. Firms as a Test of Technology Sourcing,” _American Economic Review_ , 2006, _96(5)_ , 1860–1874.

- **, S. Redding, and J. Van Reenen** , “Mapping Two Faces of R&D: Productivity Growth in a Panel of OECD Industries,” _Review of Economics and Statistics_ , 2004, _86(4)_ , 883–895.

22
this fact, I can instrument for the supply-determined, portion of credit/NDP and asses its impact on growth.<sup>14</sup> As a robustness check I employ an alternative strategy that relies on measures of urban density as proxies for the ability to profitably overcome the fixed costs of bank branching. Both instrumentation strategies pass the usual battery of instrument tests and give similar results.

The paper is structured as follows: Section 2 briefly describes recent economic history in Indian and various efforts at developing greater banking penetration and higher levels of human capital. Section 3 is an analysis of the district NDP data which shows that 1) financial constraints are a significant bottleneck to growth at district level, 2) it is the least financially developed districts that suffer the most from this constraint, and 3) literacy and banking sector development appear to be substitutes in the aggregate growth process. Section 4 is a series of robustness checks and refinements on the main results. Section 5 concludes.

# **2.    India in the 1990s**

Through most of the 1970s, India’s growth averaged 3.75% per year.<sup>15</sup> This rate of growth was not enough to produce significant convergence to the frontier and was soon eclipsed by the rapid growth rates elsewhere in Asia during the 1980s. Due partly to the contrast with the rest of Asia, the 1980s saw a significant shift in policy to a more pro-business attitude and a loosening of centralized socialist policies while growth accelerated to 5.8% (Rodrik and Subramanian (2004)).

Facing a balance of payments crisis in the early 1990s, the Indian government made two major changes which were the lowering of tariffs and other quantitative restrictions on trade, and liberalizing financial markets. These changes fed into the pro-business shift of the 1980s and growth continued apace.

Additionally, the move to loosen central control opened up state and district governments to greater

> 14This instrumentation strategy is similar in concept to the one used by Calomiris and Mason (2003), who use subnational data to eliminate the effects of shocks to the money supply and employ lagged measures of bank balance sheet weakness as instruments for future credit market outcomes.

> 15 Rodrik and Subramanian (2004) dub this the “Hindu” rate of growth, “...disappointing but not disastrous...”

7
The remainder of this paper is organized as follows. The next section explains how drastic unemployment reduction episodes are identified. Section 3 examines the incidence, and correlates of, episodes of unemployment reductions. Section 4 assesses to what extent episodes can be predicted, and provides evidence of a robust association between the quality of the overall regulatory framework and the incidence of reductions. Section 5 examines which aspects of the regulatory framework matter most. Section 6 concludes.

# **2 Identification and Incidence of Episodes of Unemployment Reduction**

# **2.1 Identification**

Following Hausmann et al. (2005) on growth accelerations and Freund (2005) on current account reversals, we use an event study approach. We define an episode of unemployment reduction to be a substantial reduction of unemployment that is sustained for a protracted period of time and rule out overlap between episodes. Specifically, a decline in the unemployment rate starting at period t qualifies as an episode if the following conditions are satisfied:<sup>4</sup>

- (i) Unemployment declines at least 3 percentage points over a 4 year period

- (ii) The decline in unemployment over this 4 year period is at least 25% of total initial unemployment.

- (iii) The drop in unemployment must remain below the critical unemployment reduction thresholds for at least another 3 years.

- (iv) Unemployment strictly declines in the first year of the episode.

- (v) An unemployment reduction episode did not commence in the previous 7 years (e.g. in the previous 7 years there was no year in which conditions i, ii, iii and iv were

- simultaneously met that was not itself preceded by the onset of an unemployment

- reduction in the previous 7 years); unemployment reduction episodes are not contiguous.

> quality products and improved competition as its proponents advocate. In follow-up work, Djankov et al (2008) also highlight the important of enforcement, by demonstrating that transparent contract enforcement is instrumental in preventing self-dealing—where agents exploit power to maintain excess share of profits, thus retarding investment and growth.

> 4 We also implemented two additional conditions, notably that unemployment declines were not driven by a single outlier alone, and that unemployment during the episode was always strictly lower than unemployment at the onset of the episode, but these conditions were not binding, in the sense that they were always satisfied when the other conditions were satisfied.

4