rafmacalaba/datause-encoder-data huggingface dataset:
WPS4003

# **Access and Risk: Friends or Foes?**

# **Lessons from Chile**

## **Osvaldo Adasme, Giovanni Majnoni, Myriam Uribe**<sup>**1**</sup>

## **Abstract**

This paper documents the link between risk, stability, and access to credit markets in an emerging economy. Annual credit loss distributions of Chilean banks are presented for the period 1999-2005 providing, we believe, the first empirical evidence of the cyclical pattern of expected losses and unexpected losses of bank loan portfolios in emerging countries. The paper provides three main contributions to the debate on bank solvency and access to credit markets. First, it derives non parametric estimators of expected losses and unexpected losses, free from model error and, in particular, from distributional restrictions. Second, it shows how the distribution of credit losses for portfolios of retail and commercial loans is affected by the lumpiness of bank loans. Finally, it shows that the shape of credit loss distributions helps select appropriate policies to promote broader and sounder access to bank credit for the poor and the unbanked.

## JEL: G21, G28

## World Bank Policy Research Working Paper 4003, September 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Osvaldo Adasme is a Director of Supervision in the Superintendence of Banks and Financial Institutions of Chile (SBIF), Myriam Uribe is Head of the Credit Risk Unit in the same Superintendence; Giovanni Majnoni is Lead Financial Economist in the World Bank.  This work has been prepared for the “Conference on Risk Management and Governance” organized by the Central Bank of Brazil, Febraban and the World Bank in Sao Paulo, the 11 and 12 of April 2006. The authors thank Julio Acevedo, Don Chance, Tito Cordella, Andrew Powell, Dilip Ratha and Stijn Claessens for valuable discussions and contributions in the elaboration of this paper. Marco Arena provided excellent research assistance. All opinions expressed in this paper are of exclusive responsibility of the authors and do not compromise the World Bank and its Board of Directors nor the SBIF.  Contacts: Majnoni:  Gmajnoni@worldbank.org ; Adasme:  Oadasme@sbif.cl ; Uribe:  Muribe@sbif.cl .

1
fication in which population density enters non-parametrically (see tables S.6 and S.7 in the appendix). To test the robustness of our second measure of scale, the length of a work activity, we include the length variable in bins: contracts less than 20 km (base category), compared to work activities between 20 and 50 km, 50 km and 100 km, and above 100 km (see tables S.8 and S.9 in the appendix). We find that including population density nonparametrically does not affect our results, and the coefficients on flexibly entered length variable show that costs decrease with the length of the contract as we would expect. We have also estimated the model with costs per square meter instead of costs per km, using different lag structures of the WGI measures, tested whether large countries are driving the conflict results, and tested a range of alternative ways of dealing with missing data. Appendix S.2 discusses these further robustness checks in more detail.

# **Public Investment Management and Doing Business**

Having established that conflict and corruption are associated with higher costs, our main concern that prevents us from interpreting the coefficient estimates as causal are omitted variables. The fact that the inclusion of per capita GDP does not substantially alter the results is suggestive that conflict and corruption are correlated with unit costs not simply through the level of income. However, conflict and corruption might be correlated with other unobserved variables. For example, conflict states are likely to both have weak government public investment management capacity, as well as an unfriendly business environment. We therefore use information on the Public Investment Management Index and data from the Doing Business Indicators in 2007 to test whether these two dimensions capture part of the higher costs. We use variables which are underlying the Doing Business Indicators: the time it takes to start a business, obtain a construction permit, import and export, register property, and enforce a contract. The Public Investment Management Index is measured on a scale from one to four, with higher values reflecting better public investment management capacity. If our results are not affected by their inclusion, this does not imply a causal relationship, but it weakens the argument that our conflict and corruption variables are simply proxying for a weak business environment and government capacity.

20
**Table 8. Comparison of results between main and sensitivity analysis** (% change as compared to the counterfactual scenario in 2007)

|**Shock**|**Crop**|**Main Analysis**|**Sensitivity analysis**|
|---|---|---|---|
|**Biofuel**|**Corn**|9.8%|12.7%|
||**Soybean**|3.4%|3.7%|
|**Income growth**|**Corn**|15.3%|20.3%|
||**Soybean**|14.7%|16.0%|
||**Rice**|16.1%|17.2%|
||**Wheat**|21.1%|25.8%|

## _7.3.2. Price effect of shocks using inventory specification of Carter et al. [14]._

Finally, we simulate the model using the inventory demand parameters estimated in Carter et al. [14]. Results confirm the conclusions derived for the baseline scenario. The price effect now is marginally smaller for all shocks. This is because the estimates of the parameters of the inventory demand employed in the elastic scenario imply an inventory demand function that is on average more elastic compared to that suggested by parameters estimated by Carter et al.

# **8. Conclusion**

This report has focused on four major factors widely agreed to be responsible for food commodity price increases – economic growth, biofuel expansion, exchange rate fluctuations, and energy price change. The study also captures the effect of inventory adjustments. Incorporating an empirically estimated inventory demand function into the market-clearing condition shows that the impact of inventory on prices increases as the level of inventory diminishes. We find that in the absence of shocks attributable to the four factors mentioned above, in 2007 the prices of corn, soybean, rapeseed, rice, and wheat would have been 26% to 36% lower than the observed prices in that year. On the other hand, if inventory demand were to be ignored, in 2007 the prices would have been 38% to 52% lower than the observed prices in that year. Abstracting from considerations of inventory responses leads to predictions of larger price changes.

54
In Appendix Table A9, we report the differential effect by the GP president’s eligibility to run for office on within-GP resource allocation across pres- and nonpres-hamlets. Not surprisingly, this analysis lacks adequate power for us to draw key statistical inferences. We present these results as an exploratory analysis. The results provide highly suggestive evidence that the underlying inequities, as well as the equity effect of both our incentives are correlated with the GP president’s future electoral incentives. None of the _p_ -values of the _F_ -tests of these differences by eligibility are however statistically significant.

# **7 Conclusion**

Governance failures—broadly categorized as capture, corruption or clientelism—are endemic to electoral democracies, particularly in developing countries. This paper adds to a growing literature on attempts to correct such failures. It reports on the result of an experiment conducted among 198 village governments (gram panchayats or GPs) in rural Tamil Nadu in South India, which randomly assigned incentivizes to elected village presidents to improve their performance in two arms—either by increasing the public funds available to high performers, or by publicly identifying high performers with certificates awarded to them at a village ceremony.

We develop a theoretical model to show how imperfect voter information on the performance of elected representatives can result in inequities in resource allocation. The model provides insights into the trade-offs that political agents face between improving their election prospects and allocating resources equitably. The model derives testable hypotheses that show an increase in village budgets will increase resources allocated to electoral constituencies (hamlets) within a governance unit (GPs), but not change the relative resource allocation across hamlets. Non-financial incentives, on the other hand, by disseminating credible information on politician quality should redress inequities in resource allocation across hamlets. Finally, the model shows that politicians who face re-election should demonstrate more acute effects across both types of incentives.

Our results are consistent with our theoretical model. Overall, both financial and non-financial incentives had a positive effect on politician performance. Both resulted in more efficient performance —private transfers to village citizens that are under the direct control of the president sharply increased in comparison to the control group. Public goods such as village roads, streetlights, water supply, and sanitation—where the president can control the spatial allocation but not its amount—showed much more equitable allocation across hamlets in the treatment group, 23
international context, implying that important cross-border financial linkages already existed prior to the negotiation of PTAs.

**Figure 6: Market Share of Foreign Financial Institutions in the Domestic Financial Systems of Chile, Colombia and Costa Rica at the Time of FTA Negotiations**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Chile (2002) Colombia (2005) Costa Rica (2005)<br>Banks Securities Firms Insurance Companies Pension Funds<br><!-- End of picture text --> _Source: Own analysis based on de la Cruz and Stephanou (2006), Echandi (2006) and Saez (2006). Note: Percentages are based on total assets for_ **_banks_** _(credit institutions in the case of Colombia), number of foreign firms registered in the domestic stock market (Costa Rica) and total trading volumes (Chile and Colombia) for_ **_securities firms_** _, total premiums (Chile) and life/non-life assets (Colombia and Costa Rica) for_ **_insurance companies_** _, and total mandatory pension assets under management for_ **_pension funds_** _(the Costa Rica figure is an estimate). The foreign market share in the securities sector is likely underestimated since many securities activities – such as trading – are actually provided (offshore and on-shore) by banks._

**Figure 7: International Financial Integration of Chile, Colombia and Costa Rica (1997-2004)**

<!-- Start of picture text --> 250%<br>200%<br>150%<br>100%<br>50%<br>0%<br>1997 1998 1999 2000 2001 2002 2003 2004<br>Chile Colombia Costa Rica<br>% of GDP<br><!-- End of picture text --> _Source: Own analysis based on data from Lane P.R. and Milesi-Ferretti G.M. (March 2006). Note: International financial integration is measured as the gross stock of a country’s foreign assets and liabilities to GDP._ 37
# **Water, Sanitation and Children’s Health**

# **Evidence from 172 DHS Surveys**

**Isabel Günther and Günther Fink**<sup>*****</sup>

> *  Isabel Günther (the corresponding author): ETH Zürich. Voltastrasse 24, 8092 Zürich, Switzerland,. E-mail: <u>isabel.guenther@nadel.ethz.ch; Günther Fink: Harvard School of Public Health. E-mail: gfink@hsph.harvard.edu.</u> The authors thank two anonymous referees for very valuable comments on a previous version of the paper and the World Banks Knowledge for Change Program for funding this research. The views and findings in this paper are those of the authors and not necessarily those of the World Bank, its Executive Board, or member country governments.
2000 and 12,240 households from 612 psus in 2010. The clustered sampling used in both surveys means that households living in a psu are close to each other and can be assumed to live in the same community or neighborhood.

To provide a sense of how far/close households in a psu live, we brie‡y discuss the sample design in relation to population density in Bangladesh. For the 2010 survey, the psus were selected randomly from the integrated multi-purpose survey (IMPS) framework developed by the Bangladesh Bureau of Statistics. The IMPS identi...ed 1,000 psus using the 2010 population census as the frame. The psu borders are de...ned to be contiguous census enumeration blocks (usually about 2) and consisted of 200 households. At the ...rst-stage of sampling, psus are selected randomly from the IMPS sample of 1,000 psus. At the second stage, 20 households are selected randomly for each psu. The sample design in 2000 is slightly di¤erent. At the ...rst stage of sampling, psus are selected randomly using population weights from the 2000 population census. At the second stage, 20 households were drawn for each psus in rural and non-metropolitan urban areas and only 10 households were drawn from each psu in metropolitan areas.

The population density in Bangladesh is the highest among all non-city state countries in the world. According to the 2010 census, the population density in Bangladesh is 1,015 per square kilometer (sq. km). If all of world’s current population were squeezed into USA, the density would be 670/sq km, or into Australia, the density would be 835/sq. km (Streat...eld and Karar, 2008). The density is smaller in rural areas, about 764/sq km. With an average household size of 4.4, this implies that each rural psu in 2010 has on average an area of little over 1 sq km. The population density in urban areas (8940/sq km) is many times higher than that in rural areas. On average, more than 2,000 households live in an area of 1 sq km in urban areas. Thus our psus in urban areas cover perhaps no more than a city block. The population density in 2000 was about 880/sqkm, implying that geographical areas of 2000 HIES psus were slightly larger than that of 2010 psus. Yet, in both survey years, households in a psu lived in close proximity to each other.

12
|6|Stock cost|0.11|0.13|110.00|
|---|---|---|---|---|
|7|**CIF Border price (tax and port handling fees**<br>**included)**|**6.26**|**7.04**|**5111.72**|
|8|CT (Domestic Consumption Tax)|3.76|2.42|182.40|
|9|VAT (Value Added Tax)|1.00|0.95|529.41|
|10|Duty Credit|0.02|0.01|2.92|
|11|**Acquisition price, including taxes**|**11.05**|**10.42**|**5826.45**|
|12|Fees and distribution margins|0.38|0.28|90.00|
|13|**Subtotal 1**|**11.42**|**10.70**|**5916.45**|
|14|**Subtotal 2 (excluding VAT)**|**10.42**|**9.76**|**5387.03**|
|15|Equalization|0.88|0.11|0.00|
|16|VAT (Value Added Tax)|1.13|0.99|538.70|
|17|**Price adjustment account (Unit Subsidy)**|**-0.85**|**-2.67**|**-849.11**|
|18|**Wholesale prices including VAT**|**11.59**|**8.19**|**5076.63**|
|19|Premium for evaporation losses|0.06|0.04||
|20|Correction for thermal changes in inventories|0.02|0.02||
|21|Retail margin|0.32|0.26||
|22|**Retail price to the public (excluding VAT)**|10.85|7.52||
|23|**VAT (Value Added Tax)**|1.17|1.02||
|24|**Retailprice to thepublic(regulated by thegovt.)**|**12.02**|**8.54**||

**_Source: Caisse de Compensation, CDC_**

# **<u>Table 2- Example of the Selling Price Structure of LPG (MAD/Kg)</u>**

|**Items**|**Container> 5Kg**|**Container<= 5Kg**|
|---|---|---|
|**Acquisition price, taxes not included**|**8,299.3**|**8,299.3**|
|Domestic consumption tax|46.0|46.0|
|VAT|834.5|834.5|
|Duty credit|3.6|3.6|
|**Price for filling stations**|**9,183.4**|**9,183.4**|
|Losses of filling process|183.7|183.7|
|Filling margin and costs|318.0|318.0|
|Special premium for inventory financing|30.0|30.0|
|Bulk transport provision|50.0|50.0|
|Capping of bottles|20.0|50.0|
|VAT|60.2|63.2|
|**Sale price to distribution companies**|**9,845.3**|**9,878.3**|
|Costs and margins of distribution companies|538.0|604.0|
|Costs and margin for stocking|387.5|450.0|
|Deduct VAT|894.7|897.7|
|**Subtotal excluding VAT**|**9,876.1**|**10,034.6**|
|VAT (max)|987.6|1,003.5|
|Compensation fund balance|-7,726.2|-7,954.7|
|**Wholesale prices VAT included**|**3,137.5**|**3,083.3**|
|Retailers margin|195.8|250.0|
|**Retail Price**|**3,333.3**|**3,333.3**|

**_Source: Caisse de Compensation, CDC_**

5
<!-- Start of picture text --> United States<br>16<br>a 12 i teem — RecessionEmployees<br>o ae. na —— Elsewhere Classified<br>= ‘i — Goods and Services<br>8 — Interests<br>2 _ —— Social Benefits<br>—— Capital<br>4<br>———<br>0<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2019<br>Year<br><!-- End of picture text -->
# **References**

<mark>Aghion P, Griffith R. (2005).</mark> _<mark>Competition and Growth</mark>_ <mark>. Zeuthen Lectures, MIT Press.</mark> Aghion, Philippe, Ufuk Akcigit, Antonin Bergeaud, Richard Blundell, and David Hemous (2019). “Innovation and Top Income Inequality.” _Review of Economic Studies_ 86:1-45.

Alesina, Alberto and Roberto Perotti (1996). “Income Distribution, Political Instability, and Investment.” _European Economic Review_ . 40 (6), 1203–1228.

Alstadsæter, Annette, Niels Johannesen, Gabriel Zucman, 2018. “Who owns the wealth in tax havens? Macro evidence and implications for global inequality”, _Journal of Public Economics_ , Volume 162, 2018, Pages 89-100.

Andersen, Jorgen Juel, Niels Johannesen, David Dreyer Lassen, and Elena Paltseva (2017). “Petro Rents, Political Institutions, and Hidden Wealth: Evidence from Offshore Bank Accounts.” _Journal of the European Economic Association_ 15(4): 818-860.

Arezki, Rabah and Markus Bruckner (2012). “Commodity Windfalls, Democracy and External Debt.” _The Economic Journal_ 122 (561): 848-866.

Arezki, Rabah and T. Gylfason (2013). “Resource Rents, Democracy, Corruption and Conflict: Evidence from Sub-Saharan Africa” _Journal of African Economies_ 22(4): 552-569.

Arezki, R. , Loungani P. , van der Ploeg, R. and Venables T. , (2014). Understanding International Commodity Price Fluctuations, Journal of International Money and Finance, Vol 42, April, pp. 1-8.

Assouad, Lydia, Lucas Chancel, and Marc Morgan (2018). “Extreme Inequality: Evidence from Brazil, India, the Middle East, and South Africa.” _American Economic Review, Papers and Proceedings_ 108:119123.

Atkinson, Anthony B., Thomas Piketty and Emmanuel Saez (2011). “Top Incomes in the Long Run of history.” _Journal of Economic Literature_ 49(1): 3-71.

Bagchi S., Svejnar J., Bischoff K. (2016). “Does Wealth Distribution and the Source of Wealth Matter for Economic Growth? Inherited v. Uninherited Billionaire Wealth and Billionaires’ Political Connections.” In: Basu K., Stiglitz J.E. (eds) Inequality and Growth: Patterns and Policy. International Economics Association. Palgrave Macmillan, London.

Banerjee, Abhijit and Thomas Piketty (2005). “Top Indian Incomes, 1922-2000.” _World Bank Economic Review_ 19(1):1-20.

Bartels, Larry M. (2008). Unequal Democracy: The Political Economy of the New Gilded Age. Princeton University Press.

Bivens, Josh and Lawrence Mishel (2013). “The Pay of Corporative Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes.” _Journal of Economic Perspectives_ 27(3): 57-78.

Borge, Lars-Erik, Pernille Parmer, and Ragnar Torvik (2015). “Local Natural Resource Curse?” _Journal of Public Economics_ 131:101-114.

11
As noted above, two factors might contribute to the excellent predictive performance of the partial registry method relative to the direct predictions from the household survey. The first is that the geospatial model is trained to a measure of village welfare that is far more precisely estimated than the one used in the sample. The welfare measure is more precisely estimated both because it is based on data from a much large number households from the census extract, and because it is a predicted welfare measure that largely eliminates classical measurement error. More precise training data improves the ability of machine learning to construct a predictive model, by reducing the risk that particular predictive variables will be fit to random noise in the training data, and by improving the accuracy of the cross-validation procedure used to select models.

A second potential reason for the better performance of the partial registry method is that it uses predictions derived from the census model, which is also used as the benchmark measure of welfare. While this is defensible on the grounds that it improves the accuracy of the predictions, using the same values for prediction and evaluation will overstate measured performance. To address this, as a robustness check we consider the accuracy of the geospatial model predictions for all villages, instead of using the partial registry predictions where available as well as out-ofsample predictions. In this case, the rank correlation falls from 0.75 to 0.73 and the AUC coefficient falls from 0.89 to 0.78. However, this still greatly exceeds the performance of the RWI, the second-best method, which has a rank correlation of 0.20 and an AUC of 0.60 (see Figure 4).

**Figure 4.** Rank correlations, AUC, and R-squared coefficient for the partial registry method.

<!-- Start of picture text -->
1.00<br>0.89<br> 0.90 0.84<br>0.78<br> 0.80 0.73  0.72  0.75<br> 0.70<br>0.5732<br> 0.60 0.53750.5207<br> 0.50<br> 0.40<br> 0.30<br> 0.20<br> 0.10<br> -<br>Rank correlation AUC R-squared<br>Full sample predictions Out of sample predictions Out-of-sample plus training  values<br><!-- End of picture text -->

The RWI, although the second-most accurate method for predicting the benchmark welfare, is far less accurate than the partial registry results, and only explains 4 percent of the variation in our measure of average village predicted welfare. This is probably because the RWI is based on a measure of household wealth instead of consumption or predictive consumption. Wealth may not be as accurate at distinguishing the welfare levels of villages within 10 poor districts. Moreover, the wealth index reflects the full distribution of households, whereas the benchmark welfare measure only pertains to the bottom half.

17