rafmacalaba/datause-encoder-data huggingface dataset:
Audit is one such service that all federations studied here provide. Even when external auditors audit SHG accounts, federations help SHGs prepare for the process. Capacity building of SHGs is another such service; all the SHG federations studied provide capacity building services to their member SHGs. This involves training the SHG members, leaders, and SHG accountants. By providing a common forum for review of SHG performance and enabling them to plan for further development, federations also create space for internal learning.

Finally, federations help resolve conflicts. Such conflicts include those between SHG members, between SHGs, between SHGs and village leaders, and between SHGs and banks. Federations make available leaders and staff acceptable to both sides of a conflict to mediate such conflicts. In some cases, the federation strengthens the position of SHGs when the conflict is with outsiders.

# **Reduction in Transaction Costs**

The main innovation of the SHG-bank linkage program was in reducing the costs that banks incur when lending to the poor. This was achieved because SHGs consolidate what could potentially have been 15-20 individual accounts into one single SHG account. Even on a per account basis, the costs of lending to SHGs appear to be less than that of lending directly to individuals. A NABARD study found the transaction cost on a SHG loan account to be 35.6% less than that of an average priority sector loan on the first loan and 55.7% less on the second loan (Puhazhendhi.V. 2000).

Apart from reduction of paperwork and staff time because of account consolidation, transaction costs for banks are low because SHG promoter agencies bear a high proportion of the SHG loan monitoring costs. Although bank staff are expected to make periodic visits to the SHGs and lend to them based on factors such as the quality of accounts, regularity of meetings and credit history (for repeat loans), the reputation of the promoter agency plays a significant role in the bankers’ decision. Banks partly substitute the task of assessment and monitoring with reputation of the promoter organization. However, this social collateral that the promoter organization provides becomes insufficient when the number of SHGs increase and when amounts being lent to individual SHGs increase. In such situations, SHG federations appear to fulfill this role better as they are nearer to SHGs and have better information on SHG performance. In the case of large promoter organizations, SHG federations are also closer to the bank branches lending to SHGs.

While the lower transaction costs to banks because of the presence of SHG federations is yet to be reflected in interest rates (even after lending rates to SHGS were deregulated), insurance companies already offer discounted premiums when federations facilitate sale of insurance products to SHG members. The federations offer a level of aggregation that makes it financially viable for the companies to deal with this clientele. Federations also diminish the probability of fraudulent claims because of their better local knowledge.

- 16 -
- 73 -

relatlve to other costs were constant during the estLmatLon period. Provided that this constancy contlnues to hold during th& policy implementation period, then the prlce elasticlties estLmated allow for the assocLated capital costs of switching at historLc average prlces. The method will therefore be at its most mLeleadLng when the relative capltal costs assoclated wlth fuel switching ln the future are very different from those in the past, and also when the values durlng the estimatLon perlod themselves were highly variable. If, durlng the estLmation period the capltal costs of fuel switching were variable and no account is taken of this, then the estimated price elasticitLes will be biased because of the standard womLtted varLables' effect of econometric theory. This effect is likely to be compounded ln forecasting if the relation between capital cost to fuel cost movements changes from that of the estimation perlod. Hence a situation in which ox ante fuel substitutLon elastLcLties are estimated from data covering a period of expansion when the relevant capital costs are those for alternative plant on a greenfLeld slte (plus assoclated infrastructure), whlle the future is of low growth in which there is ex past fuel switching through conversion of existing plant, will mean that the sensitlvlty of fuel cholce to price changes is likely to be much lower ln the future than the past. Thus, where all the relevant fuels have been used ln the past and where the future capital cost structure is likely to be fairly simllar to that of the past econometric estimates, econometric models may give a good guide to substitution possibilitLes desplte their lack of detailed cost data.

The relative substltutability of fuels depends both on the unit fuel cost and the unit capital cost. Although these are clearly plant speclfic (dependLng on the size of the capital employed) various estimates have been given. A recent survey by Barnes 119911 compares dlfforent costs for generatlng electrlcity as shown in table 4.11.
2021 and Section 2).

Although higher temperatures reduce workers’ productivity, they do not seem to affect firm investments. We measure investments as a binary variable, if the firm invested in the corresponding fiscal year or not, and as the log of total investment value. In both cases, we do not find systematic evidence that higher-than-expected temperatures reduce firms’ quantity of investments in LMCs (Table 2, rows 4-5). In fact, small and medium firms exhibit strikingly little heterogeneity by income level in both measures of investment relative to the patterns observed in revenues, labor productivity, and wages. Consistent with the decline in labor productivity, however, we find that higher temperature deviations tend to reduce the innovative capacity of small and medium-size firms in LMCs who are less likely to introduce new products or processes in those years (Table 2, row 6).

Notably, temperature deviations do not affect the energy intensity of small or medium firms in LMCs—defined as total costs of energy inputs over sales (Table 2, row 7). The absence of temperature effects on energy intensity despite their negative impact on labor productivity suggests that either many small and medium-size firms in LMCs have not invested in energy-consuming cooling solutions such as air conditioning or that their ability to use them effectively is constrained by the lack of reliable electricity access. However, higher than usual temperatures raise the energy intensity of large firms in LMCs. This is consistent with the weaker impact of heat on large firms’ labor productivity and suggests that large firms in LMCs tend to invest in energy-consuming adaptation solutions such as air conditioning. Similarly, large firms in UMCs and HCs are more energy intensive when they face higher temperatures relative to long-term trends while small and medium firms do not, potentially pointing to the importance of firm-level constraints to adaptation.

Taken together, the results suggest that higher than average temperatures lead to an economically large decline in labor productivity in LMCs causing revenue losses for firms. The decline in labor productivity also leads to cuts in labor compensation and lower willingness or ability to experiment with new products and production processes. Large firms in LMCs 19
the recessionary period 1991-93, when world GDP growth averaged only 1.1 percent (see Table 1). The 3.7 average world trade elasticity in 1991-93 was even higher than in 1994. World trade had experienced negative growth in the previous two recessions, negative 3.0 percent in 1974-75 and negative 1 percent in 1981-82. Have we therefore observed a decoupling of world trade and output movements?

Table 1. World Trade Growth, Output Growth, and Trade Elastici Y5

|3.7|6.1|4.1|8.7|
|---|---|---|---|
|3.2|3.3|1.1|2.9|
|1.2|1.8|3.7|3.0|

a. Estimates and Forecast.

- b. Growth rate of export plus import volumes of merchandise.

c. World trade elasticity = growth rate of world trade / growth rate of world output. Source: International Economics Departnent, World Bank, August 1995.

Three major factors worked in favor of sustaining trade growth in 1991-93: the desynchronization of business cycles in the United States, Japan, and Europe; the continuation and even acceleration of growth in the developing world, notably in East Asia and Latin America which became the new growth poles; and the effective transfer of purchasing power through a surge in private capital flows to highly absorbent LMIC regions, namely East Asia and Latin America.<sup>6</sup> In contrast to the two previous business cycles, when the United States, Japan, and Europe hit their cyclical troughs at about the same time, in 1975 and 1982-83, fluctuations in activity in the three OECD regions in the early 1990s were desynchronized to a considerable extent. The United States reached its peak in the last cycle in 1989 and a trough in 1991, and has been in an upswing since then, while Europe and Japan reached their respective peaks in 1990 and 1991 and did not reach troughs till 1994. In contrast to the two previous cycles, when OECD countries were subject to common external shocks-the two oil price hikes-and pursued similar policy responses, including sharp monetary tightening, the last downturn in the industrial countries was inspired more by forces internal to each country or region. In the United States, these forces were corporate balance sheet deterioration at the end of the 1980s and fiscal consolidation; in Japan, the "bubble economy" in the second half of the 1980s and the subsequent collapse in stock and property markets; in Europe, the stimulus of German unification and the subsequent monetary tightening needed to curb inflationary consequences.

> 5 Global forecasts are created using BANK-GEM. Long-term forecasts were created in February 1995 for _Global Economic Prospects_ **_and_** _the Developing Countries 199S (GEP95)._ Most recent history and short-term forecasts were updated in August 1995 for _Global Economic Prospects and the Developing Countries 1995-Short-Term Update_ (SecM95-969). Forecasts are subject to future revisions. **6** For income and regional groupings of economies, see Appendix A.

8
# **Figure 1: The evolution of the US external payments position: 1946-2008**

<!-- Start of picture text --> Merchandise trade account and income from asssets Merchandise trade account and income from<br>held abroad, five‐year averages, 1946‐1990 asssets held abroad, 1991‐2008 bn. USD<br>bn. USD<br>Investment income<br>Investment income<br>600<br>100<br>Goods trade Goods trade<br>300<br>50<br>0<br>0<br>‐300<br>‐50<br>‐600<br>‐100<br>‐900<br>‐150<br>Overseas private investment, foreign aid and military Overseas private investment, foreign aid and<br>expenditure, five‐year averages, 1946‐1990 bn. USD military expenditure, 1991‐2008 bn. USD<br>1500<br>145<br>1200<br>Overseas investment and credit<br>900<br>Foreign aid<br>95<br>600<br>Overseas military spending*<br>300<br>0<br>45<br>Overseas investment<br>‐300<br>Overseas credit<br>Foreign aid* ‐600<br>Overseas military spending<br>‐5 ‐900<br>(*) Net military transactions before 1960 (*) 1991‐2007<br><!-- End of picture text --> <u>Source: Compiled from HSUS (2006)</u><sup>6</sup> , Bureau of Economic Analysis (International Economic Accounts), and US AID Greenbook (2007)<sup>7</sup> 6 Historical Statistics of the United States, Millennial Edition On Line, Carter S. et al., eds., Cambridge University Press 2006.

5
bulk<sup>27</sup> of accruing oil revenues with the aim at increasing the return to private investment and raising the country growth rate, Takizawa, Gardner and Ueda (2004). This result does not simply follow from the fact that marginal return on investment spending in these countries would be higher than the return on external financial assets. When the initial capital stock is low and government spending has positive externalities on production, the country would be better off spending more of its oil revenue upfront by increasing both return to private investment and the country’s rate of growth. Hence, if oil revenue investment lead to long-term benefits (e.g.; health, education and infrastructure), then spending can permanently raise income, thus minimizing the tradeoff between current and future generations.

Managing revenue volatility is a tricky task; nevertheless, strengthening the fiscal<sup>28</sup> management, adopting clear rules of the game upfront, and improving transparency and accountability can help smooth the problem during the bust cycle. Managing the bust cycle might be less an issue if revenues are well-managed during the boom period; therefore improving public finance management (PFM) should be a priority. Investing in the economy to the extent of the absorptive capacity could be a wise decision, to spread the natural resource wealth into the country and in other to increase the standard of living. Building up assets should also be a serious alternative depending of the magnitude of oil revenues and country absorptive capacities.

# **V – Addressing Institutional Issues: Avoiding the Rent-Seeking-Disease in Sub-Saharan Africa’s Oil Exporting Countries**

Existing empirical evidence seems to demonstrate without exception that countries that have managed to circumvent the resource curse demonstrate superior organizational settings with sound institutions or good leadership able to insulate windfalls from political interferences: Norway for example is one of the perfect illustrations for that matter. With natural resource wealth such as oil, there is so wealth

> 27 Provided that there is not absorptive capacity issue or it is taken into account in SSA’s oil exporting countries.

> 28 Also see Achille Toto Same; Windfall Management for poverty Reduction: Improving Public Finance Management. The Case of Chad. World Bank‘s Policy Research Working Paper 4596. April 2008.

26
# 1. Introduction

This paper serves as an analytical background paper to the ‘Resilient Shores’ report (Rentschler et al. 2020. Specifically, it describes the methodologies and data sources employed to provide a quantitative assessment of the exposure of coastal provinces in Vietnam to various natural hazards. All analyses conducted here build on a review of the current state of the literature as summarized in the report. As methodologies are described, this paper also presents some analysis outcomes. For a full overview over the results and for policy implications of these findings, readers are referred to the main report and the technical annex to the report.

Geographically, the scope of this work covers all 28 coastal provinces in Vietnam (figure 1.1). As it is aimed at providing an overview over the effects of natural hazards on the Vietnamese people, the thematic scope is wide. Natural shocks can directly affect people (like a roof being blown off by a typhoon), they can affect their livelihoods (like the destruction of a field of crops), or they can disrupt infrastructure services that people rely on for work and life. Specifically, this paper considers four categories of natural hazards: flooding, typhoon winds, coastal erosion, and droughts and saline intrusion. The impacts of these hazards are estimated on various types of agriculture and aquaculture production; on existing and fastgrowing human settlements; on health care facilities and schools; on industrial zones; on hotels; on power generation plants; and on the electricity distribution grid. As a detailed analysis of the exposure of the national transportation network was published recently (Pant et al. 2019), roads and railways are not analyzed here.

**Figure 1.1: Overview of the 28 coastal provinces and the regions included in the analysis**

Source: Google Maps (background image).

3
Policy Research Working Paper 8932

# **Abstract**

Recent work on China’s accession to the World Trade Organizations pays little attention to the wave of reforms in China in the 1980s and 1990s. These reforms created the preconditions for accession and strongly influenced its outcomes. The preeminence of processing trade at the time of accession sharply reduced the impact of accession-related tariff reductions on exports and set the stage for China’s increases in domestic value added and reduction in China’s involvement in global production sharing since that time. The assessment in this paper, based on export data and simulation results on the ex ante accession-related effects on export volumes in the literature, finds that the accession must have increased China’s real export growth by at most 6 percentage points between 1997 and 2005. This effect is substantial, but not as large as suggested by the difference between the pre- and post-accession export growth rates in the four years before and after accession. This is because the influence of cyclical fluctuations related to the Asian financial crisis and the U.S. dot-com crash dampened export growth in the period before accession in 2001 and accelerated it afterward.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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/prwp. The authors may be contacted at eianchovichina@worldbank.org or w.martin@cgiar.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
household expenditures, CSP recipients may have responded powerfully to these messages, perhaps especially so given a tradition of deference to authority in Cambodia.

We analyze the impact of the program among the first cohort of eligible children. These children filled out the application forms in May 2005, and the list of scholarship recipients was posted in November 2005. We use data on children at two points in time. First, we have access to the composite dropout score, as well as the individual characteristics that make up the score for all 26,537 scholarship applicants. Second, we fielded a household survey of 3453 randomly selected applicants and their families in five provinces: Battambang, Kampong Thom, Kratie, Prey Veng, and Takeo.<sup>10</sup> The household survey was collected between October and December of 2006, approximately 18 months after children filled out the application forms. Since application forms were filled out at the end of 6<sup>th</sup> grade, estimates of program effects on the school enrollment of applicants based on the household survey refer to the beginning of 8<sup>th</sup> grade.<sup>11</sup> Table 1 summarizes the characteristics of CSP recipients and non-recipients, as reported on their application forms—separately for all applicants (left-hand panel) and applicants within ten ranks of the cut-off of the score (right-hand panel). The first four columns of each panel show that, as expected, recipients are generally poorer than non-recipients. For example, in the full sample, CSP recipients are less likely to own a bicycle (54 percent of recipients own one versus 76 percent of non-recipients); less likely to own a radio (25 versus 39 percent); and less likely to live in a dwelling whose roof is made of solid materials such as tiles, cement, concrete or iron (44 versus 65 percent). The differences between recipients and non-recipients are smaller when we limit the sample to children whose value of the score is closer to the cut-off.

> 10 The sample was based on randomly selected schools in these five provinces. The survey was limited to applicants ranked no more than 35 places above the cutoff in these schools. This restriction was imposed to maximize the number of schools, while maintaining the density of observations “around” the cut-off—an important consideration when estimating program effects based on regression-discontinuity, as discussed below.

> 11 We also have access to a third set of data. These come from four unannounced visits to the 100 CSP schools (in February, April, and June 2006, and in June 2007) in which the physical attendance of applicants was verified. These allow us to validate our schooling impacts on recipients (as we discuss below), but do not allow an analysis of labor impacts nor sibling effects.

14
before. The results for GRAVo and C ∗intlo are reported in Annex Table A6. The panel results following equation (3) are reported in Table 7. In there it also includes two sets of control variables: oo oo ECIPE’s data restrictiveness index, which again is split up between the CB and DR component; as well as the WEF’s Network Readiness Index. Both variables vary over time.

The table shows that the coefficient results are largely consistent with the ones found in Table 5 for the cross-section analysis. Yet, the significance for some coefficient outcomes is weaker, such under the conditional model for the CB component in column 5 (although still negative) and for the DR component in column 2, now coming out as insignificant. That said, the conditional model for the Digital+ sector for the CB component (column 1) is much stronger, as well as the negative coefficient outcome for the limited model which now is significant at the 5 percent level.

Table 7: Panel results following equation (3) using Data Restrictiveness Index as control

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Publishing**|**(4)**<br>**Telecom**|**(5)**<br>**IT & Info**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**OP|-0.307|0.524|-0.064|-0.223|1.369***|0.117|
||(0.122)|(0.234)|(0.857)|(0.510)|(0.001)|(0.752)|
|MODEL**CB**CT|0.765***|0.065|0.470|0.633**|-0.550*|0.395|
||(0.000)|(0.871)|(0.162)|(0.038)|(0.097)|(0.152)|
|MODEL**CB**LT|-0.695**|-0.495|-2.360***|-0.650|-0.060|-0.703|
||(0.017)|(0.296)|(0.000)|(0.147)|(0.899)|(0.273)|
|MODEL**DR**OP|-0.064|-0.996***|-0.665**|0.165|-1.194***|-0.310|
||(0.738)|(0.001)|(0.050)|(0.581)|(0.000)|(0.347)|
|MODEL**DR**CT|-0.194|0.713|-0.276|-0.220|1.174***|-0.062|
||(0.344)|(0.124)|(0.384)|(0.436)|(0.002)|(0.825)|
|MODEL**DR**LT|0.027|-0.861*|0.012|0.728*|-1.099***|0.502|
||(0.925)|(0.083)|(0.979)|(0.080)|(0.002)|(0.193)|
|DTRI CB|-1.596***|0.745|0.576|0.212|1.809**|-1.304|
||(0.001)|(0.248)|(0.499)|(0.741)|(0.037)|(0.147)|
|DTRI DR|1.066**|0.414|3.691***|-0.607|-1.278*|0.039|
||(0.011)|(0.470)|(0.000)|(0.361)|(0.070)|(0.955)|
|WEF NRI|0.868***|1.050***|1.074***|0.749***|0.941***|1.201***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp-yr + imp-yr|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|33748|33748|33748|33748|33748|33748|
|P-R2|0.985|0.967|0.958|0.961|0.938|0.972|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex 33