rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

Social funds have been defined as “agencies that finance small projects in several sectors targeted to benefit a country’s poor and vulnerable groups based on a participatory manner of demand” (Jorgensen and Van Domelen 1999). After the first social fund was set up in Bolivia in 1987, the idea attracted immediate support, and by May 2001 the World Bank had provided financing to more than 98 social funds in 58 countries.

This is the context in which the Egyptian Social Fund for Development (SFD) was established in 1991, when the government sought to widen the social safety net in the wake of an economic reform and social adjustment program. Run by a semi-autonomous agency that reports to the office of the Prime Minister, the SFD is one of the three main safety net programs in Egypt, along with the system of subsidies of basic foodstuffs, and the Ministry of Social Solidarity’s cash transfer program.

The mandate of the SFD is to (i) reduce poverty by supporting community-level initiatives, (ii) increase employment opportunities, and (iii) encourage small-enterprise development. In doing this, it is tasked with mobilizing national and international resources, and cooperating with governmental bodies, NGOs, and community and private sector groups. Since its inception, the SFD had disbursed a total of LE 11.4 billion as of the end of the first quarter of 2008, equivalent to approximately US$2.5 billion; this represents about US$2.36 per person per year<sup>2</sup> , or about 0.15 percent of GDP. An estimated 32% of Egyptians are potential beneficiaries from SFD projects, in the sense that they live in villages or wards where there is at least one SFD intervention.

Of its total spending, the SFD devoted 62 percent to supporting the development of small enterprises, 9 percent to microcredit, and the remaining 28 percent to financing community infrastructure. The SFD acts as a source of funding for projects, which are implemented by other agencies.

The focus of this paper is on SFD’s activities in community infrastructure and microcredit. Broadly, the question of interest is whether the SFD has had an impact on living standards. More specifically, our purpose is to measure the effects of SFD interventions on the immediate outputs (such as the number of households connected to sewers), on the short-term direct outcomes (for instance, the under-five mortality rate), and on the long-term impact on poverty, illiteracy, and employment.

> 2 Source for population information: UN World Population Prospects: The 2006 Revision.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 2 of 36_
**Table 1. Contingent Valuation MBDC Bid Design**

|Fees per month for<br>fiveyears|DefinitelyYes|ProbablyYes|Not Sure|ProbablyNo|DefinitelyNo|
|---|---|---|---|---|---|
|Free(0yuan)||||||
|3yuans||||||
|5yuans||||||
|10yuans||||||
|15yuans||||||
|20yuans||||||
|25yuans||||||
|30yuans||||||
|40yuans||||||
|50yuans||||||
|60yuans||||||
|70yuans||||||
|80yuans||||||
|90yuans||||||
|100yuans||||||
|150yuans||||||
|200yuans||||||
|250yuans||||||
|300yuans||||||
|350yuans||||||
|400yuans||||||
|450yuans||||||
|500yuans||||||

18
statistical comparison, although we will include them in the discussion on infra-regional variation below.<sup>iii</sup> Figure 1 shows that the median African country has a significantly shallower financial system than the median non-African country. We present three standard indicators of financial development: Liquid Liabilities to GDP, Bank Deposits to GDP and Private Credit to GDP, using data for 2011. While the median non-African developing country has Liquid Liabilities of 47 percent of GDP, the median African country has only 32 percent. Similarly, the median deposit to GDP ratio outside Africa is 38, compared to 25 percent in Africa, while the median Private Credit to GDP ratio is 34 percent outside Africa, but only 18 percent inside Africa. Comparing the African gap between deposit and credit data also shows that African banks are less effective in intermediating society’s savings, a topic we will return to below.

**Figure 1: Aggregate Financial Development in International Comparison**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>Non-African developing<br>15<br>countries<br>10<br>5 Sub-Saharan Africa<br>0<br>Liquid liabilities Bank deposits to Private credit by<br>to GDP (%) GDP (%) deposit money<br>banks and other<br>financial<br>institutions to<br>GDP (%)<br><!-- End of picture text --> Source: Global Financial Development Indicators, World Bank It is important to note that behind the median there is wide variation across Africa.

Even excluding the most financially developed African economies, such as Mauritius and South Africa, there is a wide range in Private Credit to GDP across the low- and lower6
81 Table 7 Life Expectancy at Uittb *ad lafeat Nrtality. Ratie by **Sax** ed Maternal Mortality feo **Regimes** In **1360 ead 1350**

||Life<br>E<br>**Fem**<br>to<br>1960-65<br>(1)|xpectancy<br>**ale**<br>Mele<br>1960-a5<br>t2)|**at b**<br>**Male**<br>1960<br>(3)|**irth**<br>**Level**<br>-<br>1360<br>(4)|latest<br>**_?Male_**<br>to**Male**<br>a<br>1360<br>(5)|**Mortality**<br>ablo Level<br>ea 1960<br>(6)|Maternal<br>**mortality**<br>**pot**<br>100.000 Live Birtbs<br>ca 1963<br>(7)|
|---|---|---|---|---|---|---|---|
|High Income<br>Zurepo<br>Oceania<br>North America<br>Japan<br>Israel|1.09<br>1.05<br>1.07<br>1.10<br>1.00<br>1.04|1.11<br>1.09<br>1.06<br>1.12<br>1.07<br>1.05|66.6<br>67.0<br>61.7<br>66.9<br>66.5<br>66.1|**60.5**<br>69.7<br>64.6<br>69.4<br>73.6<br>71.0|.0 *<br>(22)|17.4<br>(22)|30<br>30|
|&outh Africa<br>Latin America|1.07<br>1.06|1.05<br>1.07|52.1<br>54.9|60.9<br>62.1|.03<br>|44.09<br>|270|
||||||11|Cli||
|Zast<br>**Asia**<br>|1.06|1.05|51.0|64S|<br>.79|34.2|167|
|(excluding<br>Japan)||||||||
|<br>South lEst Asic|1.06|1.06|43.2|533.||||
|Other Zest Asie||||||||
|(e.g.Hong_tons,_<br>Korea)|1.07|1.06|54.2|63.4||||
|China|1.06|1.04|53.4|66.5||||
|South Asia|.97|.96|43.3|50.7||||
|(middle)|||||.62|51.0|616|
||||||(4)|(4)||
|West Asia|1.05|1.06|43.2|56 2||||
|Africa|1.07|1.06|40.6|49.3|--|--||
|North Africa|1.05|1.05|45.5|54.7|90|58.1|500|
|SublSheran Africa<br>Cexculding S.Afric|<br>1.08<br>a)|1.07|39.1|47.7|.01<br>(4)|$6.4<br>(4)|676|
|World|1.05|1.04|50.9|57.9||||

*Nubier of countrio avered in parenthesoe: Not necessarily representative of region because of data limitations.

Sour0ss: Fizst tour colunra weighted by populations. Zetimates fro United Netions. D-caarscje Indicators oas. New York. 1962; Colums 5 and 6 **are** unweiiibted **averages** of countries reporting in 1903iUDlls nh I Yo;rbook. Table 16; Colum 7 are *ftimates **weighted by** population of reoisons from **Rosec** alt., 1930. Toble 34. For country lovel *ptimates that are probably mrc reliable for **Attica** ead **Asia. see** Appendix Table S.
At the same time, recent evidence shows that most firm managers saw the contraction in demand to be the most important effect of the crisis rather than the credit crunch (Correa and Iootty, 2009), and that the sales growth of innovative and young firms was more affected by the crisis than other enterprises (Correa and Iootty, 2010). These findings are consistent with the notion that firms that lacked access to external funds were most vulnerable to the drop in demand, which leads to two additional hypotheses that we will test in our survival regressions:

**_Hypothesis 2:_** _Because of their greater reliance on internal funds, much of which derives from sales, we expect young, small firms to be more vulnerable to the indirect effects of reduced demand brought about by the crisis. We therefore expect them to have a lower likelihood of survival than others._

**_Hypothesis 3:_** _Firms with a pre-existing relationship with a financial institution are more likely to attract external funding to compensate for reduced internal funding via sales resulting from the crisis.  We therefore expect firms that had a loan, line of credit, or overdraft facility to have a greater likelihood of survival than others._

# **B. Foreign Bank Participation**

A second set of hypotheses comes from the literature on the effects of foreign bank participation. A key insight is that multinational banks operate internal capital markets through which they allocate capital and liquidity to their subsidiaries and branches. Morgan et al. (2004) derive a model in which multinational banks respond to shocks in both their home countries and the host countries of their affiliates, reallocating capital to equalize its rate of return across the countries in which they operate.<sup>10</sup> They derive two main propositions: first, there is a support effect, by which multinational banks extend capital to affiliates that have been affected by a financial shock; and second, there is a substitution effect, by which multinational banks shift capital among affiliates in response to real sector shocks that affect the profitability of lending in a country.

Recent empirical evidence is consistent with both the ―support‖ and ―substitution‖ propositions. Using data from 45 of the largest multinational banks from 1991 to 2004, de Haas and van Lelyveld (2010) find a negative relationship between GDP growth in the home country

> 10 For ease of exposition, we use affiliates to refer to both branches and subsidiaries in foreign countries.

8
Table 1 Descriptive statistics for major variables used for analyzing the determinants of net crop revenue

||All|farm<br>|Irriga|ted farm<br>|Rain|fed farm<br>|
|---|---|---|---|---|---|---|
||Mean|Standard<br>deviation|Mean|Standard<br>deviation|<sup>Mean</sup>|Standard<br>deviation|
|Net cropping revenue per ha<br>(Yuan/yr)<br>|10146|12280|12319|12846|7464|9736|
|Spring temp (<sup>o</sup>C)|13.2|4.7|13.8|3.5|11.05|4.7|
|Summer temp (<sup>o</sup>C)|24.2|3.2|25.1|2. 6|22.6|3.4|
|Fall temp (<sup>o</sup>C)|13.7|5.6|14.4|4.9|11.1|5.6|
|Winter temp (<sup>o</sup>C)|0.3|8.5|0.9|6.7|-3.3|8.9|
|Spring prec (mm/month)|76.2|65.3|81.7|79.1|53.2|43.4|
|Summer prec (mm/month)|144.2|62.5|128.4|72.1|139.8|51.9|
|Fall prec (mm/month)|56.8|32.5|48.6|31.4|53.8|33.2|
|Winter prec (mm/month)|23.2|24.1|28.2|27.8|15.0|19.0|
|Share of land areas with<br>clay soil (%)|30|38|31|40|17|31|
|Share of land areas with<br>silt soil (%)|31|39|28|36|43|43|
|Plain (1=Yes; 0=No)|0.45|0.50|0.75|0.43|0.35|0.48|
|Road (1=Yes; 0=No)|0.97|0.18|0.97|0.18|0.95|0.22|
|Distance to township<br>government (km)|6.1|4. 5|5.2|3.6|7.1|5.2|
|Share of irrigated areas<br>in village (%)|48.9|39.9|||||
|If participate production<br>association (1=Yes;<br>0=No)|0.03|0.18|0.05|0.22|0.01|0.11|
|Share of labor without<br>receiving education (%)|7.5|18.5|6.1|16.1|9.6|21.6|
|Cultivated land area per<br>household (ha)|0.72|1.00|0.57|0.72|0.99|1.29|

Note: The observation for all households is 8405, the observation for irrigated households is 2750 and the observation for rainfed households is 2119.

22
confidence intervals for firms younger than five years old in each census year and shows that the average size of the youngest firms almost doubles between 1983 and 1995<sup>23</sup> and then reduces.

In order to obtain true life-cycle effects and to see if firms indeed grow as they age over their life cycle, we would want to compare the size of older firms at any given point in time with their size when they were born. In the absence of panel data we are unable to look at the employment of the exact firm when it was born and hence rely on the mean employment in its birth cohort. Thus we construct Establishment Size Ratio, which is the size of firms in a particular age bin scaled by the mean size of the firms in their birth cohort. Since our sample period extends from 1983 to 2005, the oldest firms in our sample for which we can obtain this comparison are firms that are 20-24 years old in 2005 with their birth cohort being firms that were <5 years old in 1983. Columns 3 and 4 in Table 5 present regressions with the Establishment Size Ratio. Col. 3 shows that firms that are 20-24 years old are 1.419 times the size of average firms in their birth cohort. The constant in col. 3 is again the reference category of firms younger than five years old and since we are scaling each firm’s employment by the mean employment of their birth cohort (firms younger than 5 years old when the firms was born) and using OLS we would expect it to be 1 as it is. Col. 4 shows that after controlling for industry, location, and year dummies, firms that are 20-24 years old are 1.86 times the size when they were born. Col. 4 also shows that there must be heterogeneity across industries (e.g. labor vs. capital intensive), states (e.g. regulation), and firm types (e.g. organization structure) in the size of the firms younger than 5 years old since the constant is no longer 1.

Overall Table 5 shows that manufacturing plants in India grow as they age.

> 23 Hsieh and Klenow (2013) use data from 1995 for their main analysis thus scaling the employment of all firms with that of the youngest firms in 1995, which is also the year when the youngest firms were the largest in our sample period.

27
**Figure 5: Overall Manufacturing Imports and Pollution Intensity, 1997-2002 (in 10,000 VD, in 1997 constant dollars)**<sup>**9**</sup>

<!-- Start of picture text --> 1800000<br>1600000<br>1400000<br>1200000<br>1000000<br>800000<br>600000<br>400000<br>200000<br>0<br>1997 1998 1999 2000 2001 2002<br>airp waterp toxp imports<br>Imports and Pollution Intensity<br><!-- End of picture text --> Together, the figures on output and import reveal that domestic output and imports from water pollution intensive sources increased during this period and may be indicative of an increase in domestic demand for water pollution intensive commodities and raw materials. Since the composition of exports depends on both domestic and international demand, we do not see similar trends in exports. Instead we see that Vietnamese toxic pollution intensive exports were higher than water and air pollution intensive exports. A possible reason for imports being dirtier than exports could be due the imports of intermediate goods that may have a higher pollution content.

Another way to look at industrial output and trade performance is to see how the various sectors have evolved in terms of their output and exports. Manufacturing industries can be classified into “clean” and “dirty” and their trends observed over a period of time. This would tell us whether there has been any trade-induced shift towards cleaner or dirtier production. Mani and Wheeler (1998) developed a classification to distinguish

> 9 Note: the unit of measurement of exports and imports is 10,000 Vietnamese Dollars. We apply the IPPS coefficients to the exports and imports units to obtain the amount of pollution generated by media, in the same monetary units.

10
# **9. Conclusions**

The Thailand Village Fund was, for much of the past decade, the largest microcredit institution in the world, and it remains one of the biggest: 40% of rural, and 10% of urban, households borrow from the VF in a given year, proportions that have remained steady at least since 2006. The VF is unusual in the degree to which its operations have been devolved to Thailand‟s 80,000 villages and urban wards. Endowed with an initial working capital of a million baht, elected village-level committees manage and run their lending operations, with only limited guidance or oversight from the center.

Originally conceived mainly as a vehicle for boosting rural development, especially by providing credit for non-traditional activities, Village Funds in practice mainly lend to poorer, agricultural, households, and most of the money is used, ostensibly, for agricultural pursuits or as consumer credit. The average VF loan is equivalent to 8% of a household‟s income, and is much smaller than loans from other formal sources. VF loans are the main source of credit for about 10% of adults; 40% of adults do not borrow; and the remaining 50% borrow chiefly from other sources, especially the Bank for Agricultural and Agricultural Cooperatives.

How well does the VF model of locally-run rotating credit associations work? Some Village Funds have misused funds (Laohong 2005), or seen their initial working capital melt away; of the 3,011 village funds surveyed in 2010, 14% did not provide data on lending; in these areas, 22% of households reported borrowing from the VF, compared to 34% in areas where the VF reported full information. This means that there is some survivorship bias in our results, although given that most VFs did provide data, and clearly some of those that did not are functioning nonetheless, we believe that this problem is manageable.

In our view, the more important point is that the quantity and nominal value of VF loans have been maintained over time; moreover, this information comes from the SES surveys of households, who have no incentive to pad the numbers. The implication is that the VF model has proven, in the aggregate, to be sustainable. This is noteworthy, especially as the loans flow disproportionately to the poorest two-fifths of the population. The institutional arrangements work because VF committees apparently desire to stay relevant, and so work to maintain their capital resources and credit ratings – pitching interest rates low enough to be attractive, but high enough to replenish loans that are not repaid, and using their local knowledge to direct credit to bankable borrowers. Most VF committees appear to do their work conscientiously; we find little evidence of elite capture, and the honoraria that committee members pay themselves do not generally appear to be excessive.

44
# V. Conclusion

This study relies on a Tropical Cyclone Integrated Assessment Model to predict the damages that climate change may cause to tropical cyclones. The A1B emission scenario is combined with four climate models. A tropical cyclone generator is then used to create 3000 tropical cyclones in each of five ocean basins for both the current climate and the future climate. The path and magnitude of each of these storms is followed for each scenario. The results of this analysis suggest that climate change will cause tropical cyclone intensity to increase in both the North Atlantic and North West Pacific ocean basins. The reliance on modeling individual storms in this study leads to far more accurate measures than using blanket assumptions about increased intensity. The analysis is the first to be able to detect different outcomes in different locations.

The study also estimates a new damage function for tropical cyclones. Data from the United States is used to estimate the relationship between damage per storm and storm intensity. The analysis suggests that minimum pressure provides a more accurate measure of storm intensity than maximum wind speed. The results of this analysis suggest that damages are highly sensitive to storm intensity. Damages are expected to double with a 1.2 percent decrease in minimum pressure.

International data is then used to estimate the relationship between national damages and income and population density. The results of this analysis suggest that damages increase with income but fall slightly with population density. A doubling of income increases damages by 40 percent. Doubling population density reduces damages by 20 percent. The results using the international data indicate that the vulnerability of countries does not vary in proportion to income and population as previously thought. The income and population elasticity of damages is less than unitary. As incomes rise in the future, damages from tropical cyclones will increase, but not as fast as previously thought. Future income and population is predicted to increase tropical cyclone damages from $26 billion to $55.5 billion USD/yr with the current climate. Future damages will double even without climate change. However, damages as a fraction of GWP are expected to fall from their current rate of 0.04 percent in 2010 to 0.01 percent of GWP in 2100.

26
In the stable country, as well in an unstable one, the slope of the curve is negative indicating a limited absorptive capacity: higher amounts of aid are hard to manage and result in a decrease in the success of the projects. Instability has two different effects on the success of the projects, as shown on figure one: the downward shift (1) indicates that vulnerability harms the projects, but the lower slope of the curve (2) represents the cushioning effect of aid. Hence when aid increases, the difference the success of the projects between stable and unstable countries is reduced. The cushioning impact may be so high that it fully compensates for the initially lower success of projects. We shall see that, in the most vulnerable countries, aid might even have increasing returns to scale.

The intersection point (x) is the level of aid that totally compensates for the negative effects of vulnerability so that the chances of success of a project become equal in a stable and an unstable country. Once aid exceeds this point, it could be supposed that a further increase in aid would have a similar impact in the outcome of projects in both countries. Until this point aid increase makes the chances of success of the projects in an unstable country closer to what it is in a stable country due to the cushioning effect of aid.

From these hypotheses, we can deduce our econometric model. The explained variable is the success rate of the projects. We expect that vulnerability is harmful to the projects, the level of aid might also have a negative effect if the country has a limited absorptive capacity, but the multiplicative variable (Aid * Vulnerability) should have a positive impact on the success of the projects showing that when a country is vulnerable, aid dampens the shocks and therefore reduces the negative impact of vulnerability.

# _Source of the data and measurement of instability_

Our database includes all the projects that have been funded by the World Bank. Among these 6954 projects, 4701 projects started between 1981 and 2002, our period of study. This number goes down to 4597 if we eliminate projects that were dispersed in more than one country. In the main equation, 2894 projects will be preserved after elimination of the projects of which some data are not available.

8