rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Solutions**

Although developing human capital is an easily agreed goal, finding cost-effective programs that lead to long-term increases is a challenge. Education represents both a human and social right for children everywhere, as well as a great investment. Monetary returns to schooling, through increased productivity and access to good jobs, and its contribution towards the elimination of poverty, make it a high priority investment everywhere. Despite the high proportions of resources allocated to education around the world, the challenge remains to find cost-effective ways to increase schooling among the poorest and to increase learning levels. There have been hundreds of empirical studies on the determinants of learning trying to estimate which factors affect school attainment and performance. Few have been based on randomized control experiments or used rigorous evaluation techniques. Nevertheless, there is little evidence to date that simply spending more money on schooling will improve outcomes. For example, in a review of almost 100 studies estimating the effects of resources on education outcomes, only 55 percent produced significant effects, and not all were positive (Hanushek 1995). Solutions to the education problem require, therefore, a closer look at rigorous, or randomized, evaluations.

Schooling clearly is a priority investment for children. There is a need to enroll all children in primary school, as well as pre-school. Getting children into school, and making sure that they complete their primary education, is effective for raising earnings in the future. Appropriate programs that address the supply and demand side can be very effective. Simply enforcing compulsory school legislation can be effective in raising school levels and increasing future earnings, as are programs designed to increase the supply of schooling, such as mass school construction programs; these findings have been verified by natural experiments. Duflo (2001) examines a large-scale school construction program in Indonesia by combining differences across regions in the number of schools constructed with differences across cohorts induced by the timing of the program. She shows that school construction led to overall increases in average schooling and wages. The returns to education range from 6-10 percent.

There is some evidence from India that spending, if well targeted, can improve outcomes. Banerjee _et al_ . (2005) show evidence in favor of building new non-formal schools in unserved areas as a cheap way of expanding enrollment. Case and Deaton (1999) show that raising school 4
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Leader (Allahabad), 1910.

Lonsdale, John and Brice Berman, _Unhappy valley. Conflict in Kenya and Africa_ (2 vols., London, James Currey, 1992)

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- Malik Kudaisya, Medha, _The life and times of G. D Birla_ (Delhi, Oxford University Press, 2003).

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- Markovits, Claude, _The global world of the Indian merchants1750-1947. Traders of Sind from Bukhara to Panama_ (Cambridge, Cambridge University Press, 2000)

- Gerald M Meier and Joseph E Stiglitz , _Frontiers of Development economics_ (Oxfrod University Press, New York, World Bank, 2000)

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Naoroji, Dadabhai _, Poverty and un-British rule in India_ (1901, Delhi, 1962) North, Douglass C, _Institutions, institutional change and economic performance_ (New York, Cambridge University Press, 1990)

- Pomeranz, Kenneth, _The great divergence. Europe, China and the making of the modern world_ (Princeton, NJ, Princeton University Press, 2000).

- Peterson, Derek, _Creative writing: Translation, bookkeeping and the work of the imagination in colonial Kenya_ (Portsmouth, NH, Heinemann, 2004).

- Rao, Vijayendra and Michael Walton (eds.), _Culture and public action_ (Stanford CA, Stanford University Press, 2004)

- Rao, Vijayendra, ‘Symbolic public goods and the coordination of collective action: a comparison of local development in India and Indonesia’ in Pranab Bardhan and Isha

34
those having a literacy rate greater than the median and interact this with Credit/NDP or with initial income and Credit/NDP.

In the OLS regressions, we can see that while the credit/NDP parameter value is high at 1.3 and significant in the low HC group, it is significantly lower in the high HC group (-0.4). This result is robust in the **dep_iv** 2SLS regression and the parameter values are of similar magnitude. None of the parameters are significant in the **pop_iv** regression, but the pattern is the same. The parameters on the interaction terms between initial income and the high HC group are not significant.

The parameters from Table 5 can be used to estimate the economic significance of these results. Assuming a 10% drop in financial depth in year 1990-91, the model implies a district in the low HC group will lose 1.3*(10%)= 13% in growth over the following decade. Contrast this with the situation of a district in the high HC group where the lost growth due to the same loss of financial depth would only be 0.30*(10%) = 3.0%, less than a third the original value. These calculations show that the size of the effect of financial underdevelopment is large and so is the mitigating effect of higher human capital.

These numbers indicate that financial development plays an important role in growth. They also imply that human capital (as measured by literacy rates) and financial development may be in fact substitutes in the aggregate production function. This result is a surprising one in the context of standard growth models where human capital and ability to generate external funds for physical investment would be compliments.

# **4.    Robustness Checks and Refinements of the Main Results**

To see how the result of a negative interaction term between financial development and human capital depth play out across India and as a robustness check, Table 9 shows the results of regressions in sub-samples of districts. The sub-samples are divided at the 75th percentile of population density, and between northern 22
48

# **Figure 3: Changes in financial protection, service coverage, and UHC index values**

<!-- Start of picture text --> UHC=30 UHC=40 UHC=50 UHC=60 UHC=70 UHC=80<br>Colombia 2010 (84)<br>Brazil 2006 (82)<br>Brazil 1998 (82)<br>Colombia 2001 (80)<br>Costa Rica 2004 (81)<br>Costa Rica 2010 (80)<br>Mexico 2010 (79)<br>South Africa 2003 (79)<br>South Africa 1997 (79)<br>Mexico 1998 (76)<br>Philippines 2008 (75)<br>Peru 2011 (71)<br>Vietnam 2008 (61)<br>Philippines 1998 (66)<br>Kenya 2006 (64)<br>Ghana 2007 (63)<br>Vietnam 1997 (58)<br>Guatemala 2006 (56)<br>Guatemala 1999 (53) India 2006 (57)Ghana 1995 (59)<br>Kenya 1999 (57)<br>India 1998 (52)<br>Peru 1996 (50)<br>Indonesia 2009 (47)<br>Indonesia 2000 (47)<br>Ethiopia 2007 (35)<br>Ethiopia 2001 (29)<br>0 20 40 60 80 100<br>Financial Protection<br>100<br>80<br>60<br>Service Coverage 40<br>20<br>0<br><!-- End of picture text --> Source: authors’ calculations using data described in text.

Notes: FP index reflects narrow definition of impoverishment, i.e. includes only households who cross the poverty line as a result of out-of-pocket payments. Data are for latest year. Year indicated against country name is the average year of the data.

48
how many times each grid cell has been struck by a cyclone (frequency) and with what wind speed (severity) (Figure 3).

Landslide hazards are summarized as probabilities. These probabilities are derived through a combination of trigger and susceptibility factors defined by various parameters, including slope, lithological or geological conditions, soil moisture condition, vegetation cover, precipitation, seismic conditions, and Shuttle Radar Topography Mission (SRTM) elevation data.

**Table 1: Data sources for the hazard component**

|Hazard|Description|Unit|Source|
|---|---|---|---|
|Cyclones|Tropical cyclones wind speed<br>buffers based on compilation of<br>tracks (1975-2007) and GIS<br>modelling.|Estimated Saffir-<br>Simpson categories|UNEP/GRID-Europe|
|Floods|Flood frequencies generated by GIS<br>modelling, observed flood data<br>from 1999 to 2007, obtained from<br>the Dartmouth Flood Observatory<br>(DFO) and the UNEP/GRID-<br>Europe PREVIEW flood dataset.|Expected average<br>number of event per<br>100 years|UNEP/GRID-Europe/<br>Dartmouth Flood<br>Observatory|
|Earthquakes|Modified Mercalli Intensity based<br>on GIS modelling using the Global<br>Seismic Hazard Assessment<br>Program (GSHAP) dataset.|Simulated Modified<br>Mercalli Intensity<br>(MMI)|Center for International<br>Earth Science Information<br>Network (CIESIN),<br>Columbia University|
|Landslides|Landslide probabilities triggered by<br>earthquakes and precipitation based<br>on GIS modelling taking into<br>account slope factor, lithological<br>(or geological) conditions, soil<br>moisture condition, vegetation<br>cover, precipitation, and seismic<br>conditions.|Expected annual<br>probability and<br>percentage of pixel<br>of occurrence of a<br>potentially<br>destructive landslide<br>event times<br>1,000,000|Norwegian Geotechnical<br>Institute / International<br>Centre for Geohazards|

Note: See Dilley et al. (2005) and UN-ISDR (2011) for details.

9
of different lenders. Workers’ Compensation Funds<sup>50</sup> associated with large scale employers show the highest approval rates, while their funding is restricted in purpose and amount per client. The second best option is borrowing from a friend or relative. While the formal institutions (banks, finance corporations and cooperatives) offer lower active interest rates, they are also much more selective, turning down three out of four loan requests. As figure 3.13 shows, the banked population is far more likely to get a bank loan approved than the unbanked population. Higher loan approval rates in the informal sector therefore drive the unbanked towards borrowing at higher interest rates, reflecting a significant cost of being unbanked.

**Table 3. 5  Loan approval rates by different credit providers**

||Commercial<br>Bank|Finance<br>Corporation|Cooperative|Friend or<br>Relative|Prestamista<sup>51</sup>|Store|Workers’<br>Compensation<br>Fund|Informal<br>Saving|
|---|---|---|---|---|---|---|---|---|
|Approval Rate|21%|23%|25%|95%|87%|87%|100%|80%|
|Annual Int. Rate|15%|16%|13%|N/A|150%|150%|15%|N/A|

_Source: DANE household study of Bogotá, 2003_

**Figure 3. 13  Loan approval rates of banked and unbanked individuals**

<!-- Start of picture text --> 100%<br>90%<br>77.4%<br>80% 72.1%<br>70%<br>60%<br>51.0%<br>50% 43.8%<br>40%<br>30% 21.3% 25.8%<br>20%<br>10% 1.4% 5.1% 2.1%<br>0%<br>Banked Unbanked Total<br>Approved Not approved In course<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 27. **Credit is not used because it is not perceived as a necessary economic strategy or desirable financial service.** Lack of demand is given as the main reason for not requesting a loan **,** independent of whether people are banked or unbanked or whether loans come from informal or formal sources. When asked about their reasons for not seeking loans, the majority listed: i) not needing loans; ii) not liking to be in debt; iii) preferring to use own resources; or iv) not enough income. All these answers, accounting for over 75% of the population, suggest a general aversion towards going into debt. In other words, most people do not see borrowing as a strategy for survival or upward

> 50 We consider this as an informal credit mechanism because it falls outside the banking system. 51 An informal lender, loan-shark, pawn-broker.

57
# **5.3 Heterogeneous effects of the crisis**

In order to better understand the distributional implications of a possible crisis, Figure 18 shows descriptive statistics for Uruguayan households grouped by their poverty trajectories under the crisis scenario relative to the no crisis scenario, evaluated in year 2. We distinguish three types of households: “always poor” (those that fall below the poverty line with and without crisis), “never poor” (those above the poverty line in both scenarios) and “vulnerable to crisis” (those that would not be considered poor in the absence of crisis but are predicted to be so were the shock to hit Uruguay as simulated).<sup>11</sup>

**Figure 18: Households in the crisis scenario in year 2 (composition, residence, educational level and other characteristics)**

<!-- Start of picture text --> 90 84.5 90 60%<br>51%<br>49%<br>70 70 50% 47%<br>42%<br>40%<br>39%<br>50 50 40%<br>30 30 30%<br>11.5<br>10 3.7 10 20%<br>9%<br>-10 -4.2 -2.1 -10 10% 5% 6% 5% 2% 4%<br>0%<br>-30 -25.4 -30<br>Montevideo > 5000 < 5000 Rural<br>Vulnerable to Always poor Never poor<br>crisis Interior<br>Change in income 2014 year 2 Population (%) Vulnerable to crisis Always poor Never poor<br>45% 100<br>40% 90<br>35%<br>80<br>30%<br>25% 70<br>20% 60<br>15%<br>50<br>10%<br>5% 40<br>0% 30<br>20<br>10<br>0<br>HH Urban Members < 35 35-55 > 55<br>female<br>Age<br>Vulnerable to crisis Always poor Never poor Vulnerable to crisis Always poor Never poor<br>Income change (%) Population (%)<br>None Primary incomplete Primary complete Secondary incomplete Secondary complete Terciary incomplete Terciary complete<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011. Status is evaluated according to simulations for year 2: “Always poor” (poor with and without crisis), “never poor” (non-poor in both scenarios) and “vulnerable to crisis” (poor with crisis and non-poor without crisis)

> 11 There is, of course, a fourth group: those who would be poor in the absence of crisis but would be above the poverty line were the crisis to strike. Since there is a negligible proportion of individuals in this group, for clarity of exposition it is ignored in the figures.

17
# **Figure 7: Average loan size of MFIs over Time**

<!-- Start of picture text --> Panel A Panel B<br>Average loan size / per capita Average loan size / per<br>GNI, balanced sample capita GNI, full sample<br>1.0 1.2<br>0.9 1.1<br>0.8<br>1.0<br>0.7<br>0.6 0.9<br>0.5 0.8<br>0.4 0.7<br>0.3<br>0.6<br>0.2<br>0.1 0.5<br>0.0 0.4<br>2005 2006 2007 2008 2009 2003 2004 2005 2006 2007 2008 2009 2010<br>Africa (mean) Other regions (mean) Africa (mean) Other regions (mean)<br><!-- End of picture text -->

# 3. **Overcoming Barriers to Financial Inclusion: Branch Expansion, Field**

# **Experiments, and Technology**

Africa has made substantial progress over the past years, not only in financial depth and inclusion, but also in the underlying macroeconomic stability, with few countries presenting double-digit inflation rates. Similarly, there has been some underlying institutional progress, including in creditor rights, contract enforcement and credit information sharing. Specifically, the average cost of property registration dropped from 13 to 9 percent of property value between 2004 and 2012, while creditor rights increased from an average of 4.2 to an average of 5.6 (out of a maximum of 10) over the same period. While in 2004, fewer than half of the countries had a public or private credit registry, in 2012 70 percent of countries in the region had one. On the other hand, the average cost of contract enforcement has remained high, barely decreasing from 56 percent of the average claim in 2004 to 54 percent in 2012.<sup>x</sup> While macroeconomic management and institutional development have thus shown a certain degree of improvement, the benchmarking exercise discussed in section 2.2 suggests that these are necessary and not sufficient conditions for financial deepening in Sub-Saharan 25
The effect can be broken to two components: the decline in the stock markets of emerging markets, and the appreciation of the U.S. dollar. Indeed, as the WTI oil price declines, the dollar index appreciates in value (Table 5.2). A 10% decline in the WTI oil price leads to 0.41% increase in the U.S. dollar. We use the tradeweighted dollar index that the U.S. has against its major trade partners.<sup>16</sup> The appreciation of the U.S. dollar is usually a worrying sign to emerging markets (Shin, 2016).

Table 5.2 Dollar index (broad)

|VARIABLES<br>∆log (Index)|∆log (Index)|
|---|---|
|∆_Log( Oil Price)_<br>-0.0406**|-0.0414**|
|(0.0200)|(0.0176)|
|Lags<br>No|Yes|
|Constant<br>0.000236**|0.000258**|
|(0.000111)|(0.000115)|
|Observations<br>689|674|
|R-squared<br>0.133|0.148|
|Robust standard errors in parent|heses|
|*** p<0.01, ** p<0.05, * p<0|.1|

Focusing the impact of oil price fluctuations on oil exporters, Table 5.3 examines the impacts of oil price fluctuations on the dollar-denominated MSCI Gulf States Index **.** The impact here reflects the reactions of oil-exporters’ stock markets to changes in oil prices. Quite surprisingly, a lower WTI oil price hurts the dollar value of the Gulf States’ stock markets but the impact is statistically insignificant: every 10% decline in WTI oil price, MSCI Gulf State index drops by 0.55%. The magnitude is relatively small and not statistically different to zero. Conventional

> 16 <mark>Euro Area; Canada; Japan; Mexico; China; United Kingdom; Taiwan, China; Republic of Korea; Singapore; Hong Kong SAR, China’ Malaysia; Brazil; Switzerland; Thailand; Philippines; Australia; Indonesia; India; Israel; Saudi Arabia; Russian Federation; Sweden; Argentina; República Bolivariana de Venezuela; Chile; and Colombia.</mark> Data are from the Federal Reserve.

24
# Secondary Impacts of Water Transfers

As discussed in the context of market failure, efficient resource use requires that externalities be accounted for in decisions to transfer water. Simply stated, this means that any change in water allocation cannot have uncompensated effects on other water users. However, this stipulation does not take into consideration thiat there may be other parties, who are not water users, who are adversely impacted. These "secondary impacts" tend to be on people who supply water users and the community at large (Howe and Easter, 1971).

Including impacts on suppliers and community tax base is relatively simple. Theoretically, these impacts are negligible if full employment exists and resources are completely mobile. Under these conditions, losses in the water selling area are offset by gains in the purchasing area. However, some unemployment, and imperfect resource mobility is the rule, rather than the exception. Moreover case, the distribution of economic activity is of concern to local officials, regardless of aggregate impacts (Nunn,1985; Fort Collins Coloradoan, 1987). Input-output analysis can be used to estimate secondary impacts (Young, 1984). A study of the California water bank shows significant and differential secondary impacts by crop type and by region (Macaulay, 1991).

Beyond economically identifiable third party impacts, water transfers are often objected to on more general ethical grounds (Brown, McDonald, Tysseling and DuMars, 1982). Around the world, transfers are often resisted because they may engender "speculation", "capitalist accumulation"(Maass, 1990) and "water hogging". There is also evidence that community members perceive a loss in opportunity for growth, a loss of culture and control over the future (Ingram, 1990) and an unfounded emphasis on individual over community values (Nunn,1990) when water is transferred permanently out of a locality.

# Administrative Systems for Transfer

Certainly, market systems for water transfers are the exception, rather than the rule, throughout the world. Many countries, because of inexperience with market systems, recognition of the problems outlined above or simply because of historical, cu'tural and political circumstances, have adopted administrative systems that reallocate water. The following paragraphs describe some alternative administrative approaches that have been taken, along with some discussion about their strengths and weaknesses.

18
Policy Research Working Paper 9903

# **Abstract**

Evidence of public expenditure on infrastructure is extremely sparse. Little is known about the trends and patterns of infrastructure expenditure, and there is no real basis for assessing the adequacy and efficiency of infrastructure spending. Drawing on the World Bank’s novel BOOST database, this paper provides a first relatively disaggregated picture of infrastructure spending trends and patterns for a large sample of more than 70 developing countries covering 2010–18, drilling down into expenditure by sector for roads as well as electricity, and distinguishing operating from capital expenditure. Complementary sources of data are tapped to allow comparison between expenditure patterns on and off budget. The study finds that on-budget expenditure on infrastructure has been low both in absolute terms (1 percent of gross domestic product) and relative terms (5 percent of total public spending), as well as declining over time. Overall, infrastructure spending declined by about one-third over 2010–18 (with the road sector bearing the brunt of the decrease), and now lies well below estimates of the required levels, except in a handful of cases. There is evidence that low-income countries, despite lower spending envelopes, attach greater priority to public investment and infrastructure spending than their middle-income counterparts. Econometric analysis suggests that infrastructure spending in low- and middle-income countries has been historically procyclical, although to a lesser degree than total expenditure. In the transport sector, road funds are shown to play a substantial role in funding road maintenance, appearing to improve the adequacy of funding, while attenuating pronounced capital biases in road sector spending, but there is little evidence of efficiency improvements over time.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at vfoster@worldbank.org, arana1@worldbank.org and ngorgulu@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