rafmacalaba/datause-encoder-data huggingface dataset:
**Figures 4(a)-(d):  Global Capital Flows, Over Time and From Different Perspectives**

<!-- Start of picture text --> (a) Countries' International Financial Position, (b) World Trade and Gross Capital Inflows ,<br>1980-2009, Ratio of World GDP Share of World GDP<br>4<br>35%<br>3.5<br>30% World<br>3 Trade<br>25% (% of<br>2.5 GDP)<br>20%<br>2<br>15%<br>1.5<br>Global<br>10%<br>1 cross-<br>border<br>0.5 5% inflows<br>0 0%<br>Source: OECD Sources: OECD, IMF, WDI<br>25% (c) Gross Inflows , Share of World GDP, (d) Advanced Economies, Gross Capital Flows,<br>By Country Aggregates % of World GDP<br>20% 20<br>Advanced Economies<br>15 Inflows (liabilities)<br>Emerging and developing<br>15% 10<br>economies<br>5<br>10% 0<br>-5<br>5% -10<br>-15<br>Outflows (liabilities)<br>0%<br>-20<br>-5% Sources: IMF andWDI<br>Sources: IMF, WDI, IMF(2011d)<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> The 2008 crisis was preceded by massive increases in global capital flows. These flows fluctuated between 2 and 6 percent of world GDP during the 1980s and the first half of the 1990s, but rose sharply to almost 15 percent of world GDP before the 2008-9 crisis. In 2007, these capital flows totaled nearly $8 trillion, more than three times their aggregate level in 1995. By comparison, major global payment imbalances have been much smaller, in the range of 0.5 to 1 percent of world GDP. For 21
The price and the income effect is the main channel through which crisis impact upon households. Evidence from the Asian economic crisis in the late 1990s as well as from Africa consistently shows that as prices increase, households first reduce consumption of more expensive food items, typically animal source foods (meat, poultry, eggs, fish, and milk), and fruit and vegetables that are good sources of high-quality nutrients. This is followed by a reduction of the size and frequency of meals (Fuere et al, 2000, Bloem et al, 2005, Thorne-Lyman et al, 2010, Koumou, 2008, Block et al, 2004). World Food Programme’s household-level food security assessments conducted in 2008 in a number of countries around the world found similar evidence as well as reductions in health care visits or health expenditures, increased school dropouts, and sale of assets (Sanogo, 2009).

Individual country case studies have looked at the impact of crises on nutrition in: Indonesia, Jamaica, South Africa and Cameroon. Over the last twenty years, another strand of country studies has emerged, which has been solely focused on Russia. Using official Goskomstat for 1991 and 1992, Cornia (1994) concludes that there is only a small correlation between household resources (measured by income or expenditure) and caloric intake. Zohoori et al (2001), Popkin et al (1996), Vella (1997) and Dore et al (2003) use RLMS data to examine caloric intake and other measures of nutrition. These studies report little variation in aggregate levels of nutrition intake and nutritional status over time.

However, the absence of a robust link between economic crises and nutrition in the papers above might be a result of the methodological and data shortcomings. Indeed, as some of the authors point out, the nexus between the two variables is a complex one, thus involving various coping strategies that are usually not captured by some of the extant surveys. Moreover, it may well take a few years for a clear pattern to emerge – something that is almost impossible to do when a cross-section dataset or a two-year longitudinal data is used for analysis. Indeed, Vella (1997) and Popkin et al (1996) both use two year longitudinal datasets. Furthermore, as pointed by Popkin et al (1996), it is possible that some of their results are driven by differences in samples. In particular, it seems paradoxical that, in 1994, elderly people were poorer than before, spent a smaller proportion of their income on food, and yet had not lost weight. Finally, almost all of the papers above caution that their findings are preliminary and that further study of the crisis/nutrition nexus is needed in order to shed further light.

6
but for designing interventions aimed at changing gambling behavior. In this paper, we address this important area of research by studying the impact and discerning mechanisms of a gambling debias experiment in a sample of 840 poor households in the Eastern Cape and KwaZulu-Natal province of South Africa. This location choice was deliberate – the national lottery in South Africa is very popular especially among the poor, is widely advertised, and has extremely low odds associated with the jackpot prize.

Our debiasing tool was an experiential dice game based on the fundamentals of a four-stage learning cycle model common in psychology (Zull 2002). We started by asking participants to roll a six-sided die until they obtained a six. We then handed them two dice and asked them to roll till they obtained two sixes, which on average took them much longer. This iteration offered them a concrete experience on assessing probabilities. Moreover, depending on how fast participants were able to roll two sixes, they could reflect and update their beliefs about winning odds. We then informed them that the chance of winning the South African national lottery jackpot was smaller than them rolling _nine_ dice with all nine showing up sixes.

Experiential learning methods have the potential to be more effective than standard lectures for several reasons. First, experiential debiasing techniques tend to be simpler and more intuitive and thus do not tax cognitive resources like memory or attention (Bell et al. 1988). Our debiasing game conveyed the concept of probability by having participants experience rolling one die versus two dice, without the feeling of sitting through a math lesson. Second, experiential learning designs can be more novel and thus more memorable, while still building on preexisting knowledge (Heath et al. 1998). Our debiasing game provided participants a useful and powerful benchmark to assess the likelihood (or lack of chance) of being able to simultaneously roll sixes on nine dice, having just experienced the difficultly of rolling just two sixes. Third, experiential learning can leverage cognitive biases to induce desired behavioral change, a strategy referred to as ‘counter-biasing’ (Jolls and Sunstein 2006). Likewise, experiential learning caters to the the human tendency to put too much weight on single events in the process of belief updating (Griffin and Tversky 1992, Rabin 2002), a phenomenon that Kahneman (1998) dubbed the ‘ _power of the particular_ ’. Hence, in our intervention the dice rolling experience may have become the basis of a new availability heuristic.

Our research design features two levels of exogenous variation. First, half the study sample was randomly assigned to receive the debias treatment at the end of a baseline survey on financial access, 3
In our model the dependent variable is the extent of RER undervaluation when it takes place otherwise _0_ when the RER is in equilibrium or overvalued.

The dependent variable is the absolute value of the undervaluation beyond a certain threshold, and _0_ otherwise. We test whether policy variables have an influence on the extent of real undervaluation of the local currency. The negative coefficient in the dependent variable means that the smaller a lag in the misalignment the larger magnitude of undervaluation in the local currency. This model is used when the response is continuous but possibly censored with the dependent variables assuming discrete values. Although these values are unknown, we can still identify whether those values are greater than some threshold values. We want to investigate whether the RER undervaluations greater than some thresholds such as 5, 10, 20 and 25 percent. Hence, our dependent variable is as: This implies that _X_ reflects the magnitude of the deviation of RER below its equilibrium level beyond a certain threshold _k_ . We measure the size of the undervaluation when it is greater than a threshold _k_ and explain whether our explanatory variables affect the size of the undervaluation beyond a certain threshold. In short, our Tobit analysis examines the effects of macroeconomic policies on the magnitude of RER undervaluations.

# **4. Empirical Assessment**

This section discusses the findings from the limited dependent variable analysis on the linkages between economic policies and the likelihood (of sustaining) and magnitude of RER under-valuations.

## **4.1. Policy Analysis of RER Undervaluations:** **_Probit_ and** **_Tobit_ Models**

We examine the linkages between policy actions, the likelihood of sustaining under-valuations and the extent to which policy can affect the magnitude of the undervaluation —these relationships are evaluated using _Probit_ and _Tobit_ models, respectively. Some researchers argue that some countries ( _e.g._ China and Argentina) 15
Table 2: Migration flows, in absolute numbers, by year

||Low-skilled||
|---|---|---|
|year|2001|2011|
|male migrants within-district|3,728 (3,952)|4,419 (5,375)|
|male migrants out-of-district|4,834 (4,935)|5,022 (4,684)|
||High-skilled||
|year|2001|2011|
|male migrants within-district|4,712 (5,557)|7,586 (11,158)|
|male migrants out-of-district|7,712 (6,762)|10,289 (9,341)|

Note: In this table we present the trends in district-level migration flows, in absolute numbers, from 2001 to 2011. The numbers presented are the average (and standard deviation in brackets) number of out-migrants across all districts. We only include male migrants between the ages of 20 and 64 who migrated in the past five years, but distinguish between low-skilled (less than 10 years of education) and high-skilled (more than 10 years of education) at the top and bottom panel, respectively.

49
|_PFUt _|Price of food in urban region|
|---|---|
|_PSRi_,_t _<br>_PSUt _|Price of services in rural region<br>Price of services in urban region|
|_Rt_<br>_RWSRi_,_t_<br>_RWSUt_|Marginal product of capital in manufacturing<br>Real wage in rural services net of food<br>Real wage in urban services net of food|
|_Si_,_t_|Soil productivity factor|
|_SRi_,_t_<br>_SUt _|Rural services output<br>Urban services output|
|_TLOSSi_,_t _|Transport losses|
|_VCCi_,_t_|Value added of cash crop production in region|
|_VCCTOTt _|Value added of total cash crop production|
|_VFi_,_t _|Value of food production in region|
|_VFTOTt _|Total value of food production|
|_VSRi_,_t _|Value of rural service production|
|_VSRTOTt _|Total value of rural service production|
|_VSUt _|Value of urban service production|
|_WCCi_,_t_|Cash crop wage|
|_WGRi_,_t_|Rural government salary|
|_WGUt_|Urban government salary|
|_WMANt_|Manufacturing wage|
|_WSRi_,_t_|Rural service wage|
|_WSUt_|Urban service wage|
|_YFARMi_,_t_|Real farm income|
|_YRi_,_t_|Rural income in region|
|_YRTOTt_|Total rural income|

|**Free variable**|**s:**|
|---|---|
|_CASHODAt_|Cash ODA (non-agricultural budget support)|
|_CODAt _|Cumulative cash ODA|
|_DISYRUi_,_t_|Rural income in excess of minimum consumption (_cmin_)|
|_DISYURt_|Urban income in excess of minimum consumption (_cmin_)|
|_FKAMPt_|Food need in Kampala|
|_GNPWFt_|GNP with food|
|_IMPGp_,_t_|Import content in public service sector expenditures|
|_IMPUCt_|Urban consumer good imports|
|_IMPRCi_,_t_|Rural consumer good imports|
|_IMPUIt_|Urban investment good imports|
|_IMPRIi_,_t_|Rural investment good imports|
|_MIGRATEi_,_t_|Rural to Urban migration from region_i_|
|_NETINi_,_t_|Nutrient flow|
|_NUTSUMi_,_t_|Indexed accumulation of nutrient flows|

52
<!-- Start of picture text --> o Asia o Europe/Wddle EasVNorth Africa Latin Arrerica/Caribbean X OECD x Sub-Saharan Africa<br>13 -<br>12 -- ----- ,<br>10 . __ _ _. . i af | _<br>9<br>7 -- . _--_-_-X-__-__ - -_-_-_<br>6-<br>6 7 8 9 10<br><!-- End of picture text -->

Figure 2. Mincerian Retums and mean years of schooling <!-- Start of picture text --> 30- - E Primary O Secondary 0 Higher<br>25-<br>21.3<br>20 8.8<br>15- ~ ~ ~ 1<br>363 2,996 22,530<br>Per Capita Income (0<br><!-- End of picture text --> Figure 3. Social retums to investment in education by income level 16
# **Benchmarking Costs of Financial Intermediation around the World**

Pietro Calice and Nan Zhou<sup>†</sup> JEL Classification Numbers: G21, G28 Keywords: Net Interest Margins, Costs of Financial Intermediation, Banks Authors email addresses: pcalice@worldbank.org; nzhou@worldbank.org

> † World Bank Group. We are indebted to Augusto de la Torre, Erik Feyen, Alfonso Garcia Mora, Davide Mare, Zafer Mustafaoglu and Martin Raiser for their comments on earlier versions of this paper. We would also like to thank all participants to the seminar held on May 1<sup>st</sup> , 2018 at the World Bank for their valuable suggestions. Errors and omissions are only ours.
Infrastructure service delivery in these areas is clearly well below what would be expected on average for a country in Lesotho’s income group. It is also lower than in many other world regions.

These infrastructure shortfalls are likely to adversely affect not only the welfare of the poor but also the cost competitiveness and growth prospects of a range of economic sectors (e.g., tourism, trade) that depend critically on a stable and competitive supply of basic infrastructure service. As such, they are indicative of the infrastructure sectors and performance dimensions that need improvement if Lesotho is to catch up with competitive performance of its peer group of countries. Within the SACU sub-region, Lesotho’s infrastructure shortfalls are also likely to constrain its regional economic integration in several areas (e.g., Southern Africa power pool, road network) that are important for long-term economic prosperity. Finally, this simple, international comparison of Lesotho’s infrastructure performance should, of course, be combined with more in-depth, sector analyses for a more complete picture of the overall infrastructure performance. Taken together, such analyses could provide policymakers in Lesotho a useful guide to the areas of infrastructure performance requiring urgent attention.

# **References**

Aschauer, David.A. (1989). “Is public expenditure productive?” _Journal of Monetary Economics_ , 23:177-200.

Bogetić, Željko and Johannes W. Fedderke (2005a). “Infrastructure Benchmarking of South Africa’s Infrastructure Performance,” _Journal of Development Perspectives_ (South Africa), Vol.1, No.2, 2005, forthcoming.

Bogetić, Željko and Johannes W. Fedderke (2005b). “International Benchmarking of Infrastructure Performance in the SACU countries,” The World Bank, AFTP1, draft paper, November 2005.

De Barros, Gillberto (2005). Sector note to the Lesotho CAS on telecom and electricity sectors. World Bank, AFTPS.

Easterly, W. and Servén, L. (2002). _The Limits of Stabilization: Infrastructure, Public Deficits and Growth in Latin America_ , Stanford University Press and the World Bank.

Esfahni, H.S., and Ramirez, M.T., 2003, “Institutions, Infrastructure and Economic growth,” _Journal of Development Economics_ , 70, 443-477.

Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” _The World Bank Policy Research Paper No. 3643_ , The World Bank, Infrastructure Network, Office of the Vice President (June), Washington, D.C.

Leipziger, Danny (2001). “The Unfinished Agenda: Why is Latin America Lagging Behind?” _Finance & Development_ , International Monetary Fund, vol. 38, 38-41.

U.K. Department of Transport website: <u>http://www.dft.gov.uk/stellent/groups/dft_control/documents/homepage/dft_home_page.hcsp. In</u> particular, see TRACS surveys of the principal roads network.

World Bank (1994). _World Development Report 1994: Infrastructure for Development_ . New York: Oxford University Press.

11
take the simulated total benefits generated by Fonden and compute the mean and the standard deviation.

The second row of panel C reports the average total benefit from the simulations divided by the total cost. Rows three and four report the total benefit one standard deviation below and above the mean divided by the total cost. All in all, while our ratios vary too broadly to clearly pin down the effect of Fonden, they are of a reasonable magnitude. The average benefit cost ratio, across specifications, lies in the 1.52 (with a probability of 0.66 of being greater than one) to 2.89 (with a probability of 0.8 of being greater than one) range, and is therefore consistent with previous estimates of fiscal multipliers. These cost-benefit ratios might, however, be too conservative as they account for the overall expenditures of Fonden while assuming that the benefit of the program is only accrued in the year following the disaster.

# **5.2 The dynamic impact of Fonden on local economic activity**

As previously mentioned, we turn to month frequency night lights data to document the dynamic impact of Fonden on local economic activity. Figures 3a to 3d plot the mean change in log night lights relative to rainfall millimeters (mm) to the threshold, at four key points in the post disaster period. Specifically, the four outcome variables are the night lights log difference between the average 12 months before the disaster and the average at 3, 5, 15, and 20 months after the disaster. The figures suggest that the impact of Fonden can be broadly characterized in three phases. In the very short run, while funds and reconstruction efforts are being put into place, we find no difference between those municipalities just above and below the threshold. Between 5 and 15 months after the disaster, we see a clear jump in brightness at the threshold. At its peak, 15 months, moving from just below to just above the threshold increases brightness by roughly 0.1 log points. From 15 to 19 months, municipalities at the left of the threshold begin to catch up to those at the right of the threshold. At 20 months there seems to be no difference between municipalities with and without access to Fonden.

In order to provide a detailed account of the dynamic impact of Fonden, figures 4a to 4c plot the coefficients and 90% confidence intervals derived from estimating equations 1 to 3. Each plotted coefficient corresponds to a separate OLS regression where the dependent variable is the night lights log difference between the average 12 months before the disaster and the average at various points in the post disaster period. We begin with a post-disaster average at 3 months, and then we incrementally increase this average by a month for up to 30 months.

In order to ensure that our estimates of the impact of Fonden at different points in time are comparable, we use the following procedure. First, following Calonico et al. (2014b), we calculate the optimal bandwidth for each of our dependent variables, that is, the change in local economic activity at different points in the post-disaster period. Second, using the bandwidths derived in 11
- 39 -

3.7 Table 3.3 shows a proposed stumpage fee system developed for Rwanda in which varying fees are charged according to distance from the processor to the final market.

Table 3.3: RWANDA - PROPOSED FEE FOR CHARCOAL WOOD TO SUPPLY KIGALI a/ (Units FRw b/)

|A.ea|Distance<br>from<br>Kigali<br>Kms|Charge<br>per<br>Store<br>c/ d/<br>FRw|Equivalen<br>per m3<br>FRw|t Stumpage<br>Fee<br>standing<br>e/<br>(USS)|
|---|---|---|---|---|
|Around Klgall|0-25|1000|1540|(20)|
|Kibungo, Gitarama, Byumba|<br>25-75|800|1230|(16)|
|Gisony. Ruh6ngerl,<br>Kibuy|e||||
|Butare,<br>Kivumu|75-150|600|925|(12)|
|Regions<br>In Northeast,|||||
|Southwest,<br>East|150 plus|400|615|(8)|

- 2/ At present there Is a fixed price of FRw 400 per (standing) store or FRw 500/stere at the roadside. However, In practice charcoal producers often pay less In some areas, sometimes as low as FRw 200/stere (standing).

- b/ Current exchange rate (June 1988) USSI = 77.37 c/ Store - Stacked cubic meter which should contain between 0.60 m3 and 0.65 .3 solid vol ume.

- d/ This Is the price before the trees are felled and stacked. For the equivalent roadside price (that Is after felling, cross-cutting and stacking) add another FRw 100.

- e/ Assuming an average stacking percentage of 65.

Source: Robert van der Plas, World Bank Office Memorandum of March 1, 1988.

3.8 If this proposed fee system is adopted for Rwanda, the rates shown in the table may have to be adjusted because of resistance by the charcoal producers. Also, the additional fee charged for saving on haulage works out at between FRw 26 (US 33 cents) and PRw 36 (US 47 cents) per tonne-km, which is very high. A difference of PRw 50 to FRw 80 per stere (12 to 13 cents per t/km) between each of the four areas may be more realistic.

3.9 A plan to rationalize fuelwood supply, proposed for Niger, illmstrates the application of a "second best"' but workable firewood fee. Fuelwood is the major source of energy in Niger and is used by 98 percent of the households. Although complete data are not available, it is widely accepted that the rate Qf consumption of fuelwood greatly exceeds the rate of natural regeneration in many environmentally vulnerable areas. Especially, the concentrated and intense overexpoitation of the natural forest cover around the urban areas has led to