rafmacalaba/datause-encoder-data huggingface dataset:
- 28 -

unambiguous instructions. Thus, for the past 12 years, effective formal policy guidelines for irrigation cost recovery have been nonexistent. Of course, Bank lending for irrigation has been active over the 1976-88 perlod, which leaves the clear Inference that Informal rules of thumb for designing cost recovery covenants have existed. Unfortunately, an OED review of loan conditionality concerning Irrigatlon cost recovery for this period has not yet been produced. However, the 1986 OED review of this topic covering the perlod up to 1976 disciosed that the record was not good In that earlier period. In at least two-thirds of the projects reviewed, the covenant requiring that cost recovery at least cover O&M costs had not been compiled wlth. In many cases, the covenants concerning cost recovery were so vague that It was difficult to determine whether there had been compliance or not. O&M of the Irrlgation systems concerned was considered satisfactory at audit In only about half of the projects.

Given the demonstrated Inadequacy of existing guidelines concerning Irrigation finance aind the growing concern with quality control on Bank-financed Irrigation Investments, a synthesis of the hard lessons from Bank lending experience into a better framework for irrigatlon lending Is needed. This paper suggests that six basic propositions distilled from Bank experience can serve as the basis for such a framework:

- I. Accept the diversity of cultures and institutional arrangements In borrowing countries and substitute flexibility and Ingenulty In designing feasible irrlgation Institutlons.

- li. Focus tNix dlalogue upon the physlcal sustalnability of irrigation Investmer.a and associated natural resources.

- Ill. Approach the finance of Irrigation as a policy adjustment Issue.

- Iv. Base cost recovery pollcy on an analysis of the totality of government Interventions.

- v. Assign tax policy Instruments to appropriate policy objectives.
Table 6: Heterogeneity by municipality characteristics

|Exit BF<br>IV Approach|
|---|
|**Panel A: Positive vs. negative employment growth**|
|**On average neg. shifter**<br>-0.0177<br>(0.0258|
|F-Statistic<br>13.70|
|Mean exit probability<br>0.0792|
|N<br>36,151,808|
|**On average pos. shifter**<br>0.0598***<br>(0.0221)|
|F-Statistic<br>87.63|
|Mean exit probability<br>0.1026|
|N<br>15,220,945|
|**Panel B: Informality Level Municipality**|
|**Above average informality**<br>0.0821***<br>(0.0254)|
|F-Statistic<br>15.43|
|Mean exit probability<br>0.0710|
|N<br>35,925,648|
|**Below average informality**<br>0.0603***<br>(0.0225)|
|F-Statistic<br>12.47|
|Mean exit probability<br>0.1223|
|N<br>15,402,785|
|Time-variant controls<br>✓|
|Family-fxed efects<br>✓|
|Time-fxed efects<br>✓|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 **Notes:** The table presents the results from the IV approach as specified in model (2). In Panel A municipalities are split into municipalities with on average positive and on average negative shifters. In Panel B municipalities are split into below- and above-average informality rates according to the Census of 2010. The regression controls for family- and time-fixed effects. Robust clustered standard errors at the municipality level are shown in parentheses.

the average. As shown in Panel B of Table 6 the effects are positive and significant for both types of municipalities. However, results show that the effects are stronger in municipalities with higher rates of informality indicating that in places, where formal dependent employment is less present, an extension of it causes a greater impact in terms of exits from the program.

**Family composition** Third, we split the sample into families that have just one adult living in the family vs. families with various adults. Panel A of Table 7 shows that the overall effect is driven by families with more than 1 adult. Those results seem plausible since single-headed households are commonly facing multiple constraints to benefit from formal employment growth

22
**Table** 3: **Elasticities of Poverty Measures for Mean Income and Gini Index**

||_G_|_ini Index_|_Poz_|_arty.mwuheair&y_<br>_;(G,1,0)_|<br>_Poz)In_<br>_eltii_|_rAneIneqlzty_<br>_ty(,)_|
|---|---|---|---|---|---|---|
||_1994_|_1998_|_1994_|_1998_|_1994_|_1998_|
|Nationallevel|55.09|54.75|-1.13|-1.01|1.48|.949|
|All Urban|50.59|54.67|-3.21|-1.92|13.99|6.12|
|All**Rural**|45.86|43.77|-1.21|-1.07|.685|.31|
|**Ouest**|47.2|44.4|-1.3|-1.16|1.09|.57|
|Sud|47.16|40.89|-1.01|-1.2|.435|.419|
|Centre-Sud|39.1|45.5|-1.18|-1.01|.235|.371|
|Centre-Nord|46.2|37.1|-1.3|-1.16|.72|.12|
|Nord|45.1|46.6|-1.3|-1.32|.85|.98|
|Sud-Est|47.2|44.15|-1.18|-1.13|1.02|.43|
|Ouaga-Bobo|52.98|56.75|-1.95|-1.81|6.29|5.31|
|Autres Villes|49.33|51.32|-6.06|-.89|28.76|.47|

Sources: Authors' calculations.

Note that the absolute magnitude of poverty-elasticity for mean income is greater than unity for all economic regions but "Autres Villes", where it fell from -6% to -.89%. Hence, poverty should have declined much faster than the rate of economic growth if the growth process was not accompanied by high income inequality. These elasticities are much higher in urban areas, reflecting the scope of urban inequality and the relatively small size of the initial level of poverty. Empirical results suggest that lower poverty measures are considerably more sensitive to changes in income inequality (redistribution effects) than to changes in the mean income (growth effects), see Kakwani (1990). Rapidly rising urban poverty may therefore be driven by large income inequality.<sup>2</sup> ' However, the relatively important short-run variation in the spatial distribution of welfare cannot be explained solely by redistribution effects. Another relevant factor which might have contributed to the rapidly changing poverty map is the population dynamics effect. Nonetheless, while one may provide an updated poverty map, the population dynamics which caused internal migration to intensify during the growth period makes it difficult to determine the geographical regions which benefited most from growth on the sole basis of welfare indices. This is especially because, to a large extent, rural-to-urban migration which fueled the convergence of the traditionally rural population, largely poor to urban centers may be driven by quest for greater economic opportunities in most

> 21 There may be other reasons why increased aggregate natiortal income measured by GDP may not necessarily translate into declining poverty; these may include the nature and allocative patterns of public spending, especially if these are inherently oriented towards investment rather than consumption. Indeed, a decomposition of GDP in Burkina Faso between 1994 and 1998 shows that most of the gains achieved in the growth period was invested, which may benefit the poor in the long-run- assuming that these investments are productive and efficientbut not necessarily in the short-run. For further details on results of decomposition analysis, see Appendix A.

11
Figure 2: Results for Output per Worker <!-- Start of picture text --> Results for Labour Productivity: Y/L<br>3<br>2.5<br>2<br>1.5<br>1<br>PMGE<br>0.5<br>PMGE-IV<br>0<br>-0.5<br>-1<br>-1.5<br>-2<br>Infrastructure Measure<br>Aggregate Railways Roads Ports Air Tele- Elect-<br>Economic Tran. coms ricity<br>Infrastr.<br>Elasticity<br>GFCF FCS RAIL LOCO COACH GOODS CCAP RPASS RFRT TRDS PRDS VEHP VEHG PORTS SAA APASS TEL FTEL ELEC<br><!-- End of picture text --> 45
<mark>WPS3440</mark>

# **Market Discipline under Systemic Risk: Evidence from Bank Runs in Emerging Economies**

Eduardo Levy-Yeyati Maria Soledad Martinez Peria Sergio L. Schmukler<sup>∗</sup>

## **Abstract**

This paper shows that systemic risk exerts a significant impact on the behavior of depositors, sometimes overshadowing their responses to standard bank fundamentals. Systemic risk can affect market discipline both regardless of and through bank fundamentals. First, worsening systemic conditions can directly threaten the value of deposits via dual agency problems. Second, to the extent that banks are exposed to systemic risk, systemic shocks lead to a future deterioration of fundamentals not captured by their current values. Using data from the recent banking crises in Argentina and Uruguay, we show that market discipline is indeed quite robust once systemic risk is factored in. As the latter increases, the informational content of past fundamentals declines. These episodes also illustrate how few systemic shocks can trigger a run irrespective of ex-ante fundamentals. Overall, the evidence suggests that, in emerging economies, the notion of market discipline needs to account for systemic risk.

## **JEL classification codes:** F30, F41, G14, G21, G28

**Keywords:** market discipline, idiosyncratic risk, systemic risk, emerging markets, depositor behavior, bank run, banking crises

> ∗ Eduardo Levy-Yeyati is professor at the Universidad Torcuato Di Tella. Maria Soledad Martinez Peria and Sergio Schmukler are both senior economists in the Development Research Group of the World Bank. For useful comments and suggestions we thank David Llewellyn, Roberto Rigobón, and participants at the BIS and Chicago Federal Reserve Bank sponsored conference on “Market Discipline: The Evidence across Industries and Countries”, the BIS and Journal of Financial Intermediation conference on “Accounting, Transparency and Bank Stability”, and the 2004 Latin American Econometric Society annual conference. We are grateful to Juan Miguel Crivelli, Federico Droller, Marina Halac, and, particularly, Juan Carlos Gozzi Valdez for outstanding research assistance. We are grateful to Francisco Gismondi, Claudio Irigoyen, Luciana Ríos-Benso, and Hernán Rodriguez, from the Central Bank of Argentina, and José Antonio Licandro, Jorge Polgar, and Martín Vallcorba, from the Central Bank of Uruguay, for their help with the data and the understanding of the banking crisis in each country. The views expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors or the countries they represent..

E-mail addresses: ely@utdt.edu, <u>mmartinezperia@worldbank.org, and sschmukler@worldbank.org</u>
The final sample was split evenly between male and female respondents. Across all three governorates, 30% of female respondents in the sample were working (by design), and 70% of male respondents were working. Of male respondents, 29% had working female relatives (either wife, sister, or daughter), and 71% had non-working female relatives. The sample included 828 households where both a male and a female respondent was interviewed. Nearly 83% of the respondents were interviewed in pairs- father and daughter (7%), brother and sister (17%), or spouses (76%) living in the same household- resulting in 1,179 unique households altogether. Table 3 presents a detailed breakdown of the key demographic information of the survey sample, separated by gender and work status of respondents (for women) or respondent’s female counterpart (for men).

**Table 3. Demographic Information**

|**Variables**|**All**|**W**|**omen**|**M**|**en**|
|---|---|---|---|---|---|
|N|2007|1|061|9|46|
|Gender||5|3%|4|7%|
|Working||3|0%|7|0%|
||**All**|**Working**<br>**Women**|**Non-working**<br>**Women**|**Men with**<br>**working**<br>**counterpart**|**Men with**<br>**non-working**<br>**counterpart**|
|N|2007|315|746|275|671|
|Location||||||
|_Amman_|57%|67%|55%|65%|52%|
|_Mafraq_|13%|13%|13%|13%|14%|
|_Zarqa_|29%|20%|32%|21%|34%|
|Married (%)|81%|63%|84%|73%|89%|
|% w/ child’s age<6|42%|37%|47%|35%|42%|
|Age (years)|39.4|34.7|36.0|42.0|44.3|
||(12.34)|(8.95)|(9.92)|(14.48)|(13.17)|
|Years of education|11.90|13.72|11.32|12.87|11.29|
||(3.52)|(3.25)|(3.10)|(3.66)|(3.63)|
|Avg. no. of|1.94|1.57|2.17|1.51|2.02|
|children|(1.77)|(1.67)|(1.79)|(1.67)|(1.79)|
|Avg. HH income|540|717|448|769|463|
|(JD/month)|(499)|(731)|(316)|(733)|(329)|

_*Standard deviation in brackets_ There are notable demographic differences between working and non-working women, especially when it comes to marital status and children. Across all groups, working women have the highest level of education on average (13.7 years), while men with non-working counterparts have the lowest (11.3 years). While the difference is small, it is worth noting that the educational attainment of working women surpasses that of working men. Non-working women have levels of education similar to their male counterparts. Sixty-three percent of working women in the sample are married compared to 84% of non-working women. Working women also tend to have lower number of children on average (1.57 compared to 2.17 for non-working women), with fewer having children below the age of six (37% compared to 47%). Similar trends are also observed among male respondents with working and non-working female counterparts. Male respondents in the sample are older on average (42-44 years) compared to women (around 35 years) because our sample includes father-daughter.

10
capital depreciates. Population and productivity growth are exogenous drivers of the model‟s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to growth of the population aged 15-64 years. Productivity growth is modeled as exogenous and factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model.

# **2.2 Data**

Like in any CGE model, the main data needed are in two folds: (i) social accounting matrix (SAM) and (ii) elasticity parameters. In this section, we briefly introduce the data used for the study. For more detailed information, please refer to Timilsina et al. (2010).

# **2.2.1 The Social Accounting Matrix**

For SAM, the model uses the GTAP database (Narayanan and Walmsley, 2008). However, the database has been substantially updated for the purpose of this study. First, we have introduced corn as a separate sector/commodity, whereas it was included in “other cereal grains” in the GTAP database. The splitting process was implemented through a program called Splitcom (Horridge, 2008). A significant amount of data was required to perform the split, including information on production, consumption and trade flows for corn and other cereal grains. Detailed documentation on the splitting process and the data required to execute the sectoral split is available upon request to the authors. Second, the GTAP database does not have biofuel sectors, so we specified sectors for ethanol and biodiesel. Moreover, we introduced three subsectors for ethanol: corn-based ethanol; sugar-based ethanol (i.e., ethanol produced from sugar cane and sugar beet) and other grains-based ethanol (i.e., ethanol produced from wheat and other cereal grains). We also added three biodiesel sub-sectors for biodiesel produced from oilseeds, soybeans and palm oil<sup>3</sup> .

> 3 Interested readers could request unpublished documentation describing all the steps needed in introducing these critical sectors into the GTAP database from the authors.

11
# **2.2 A cautionary note: issues regarding the reliability of poverty estimates for geographical targeting in Azerbaijan**

As discussed above, the imputation of the welfare estimates in the census relies upon parameters derived from the 2002 Household Budget Survey. The new sample design that was fully implemented in the 2002 survey has several characteristics which have the potential to affect the reliability of spatially disaggregated estimates of welfare. Amongst the most important of these are issues related to the choice of Primary Sampling Units (PSU) and the allocation of the households to a specific interview area of fieldwork.

The sample design of the HBS of Azerbaijan selects 84 PSU from master sampling frame of 484 PSU, each of which are based on areas with a population size of about 15,000. Taking such large areas as a PSU has the potential of jeopardising the selection of a random sample of the population which forms the basis for the design of a nationally representative survey, and can potentially affect the reliability of the spatially disaggregated estimates of welfare.

Furthermore, with the redesigning of the new survey, the SSC has increased the number of interviewers from 64 to 84. Each interviewer has been assigned one of the 84 selected PSU for the survey data collection. This has the potential of creating an ‘interviewer effect’ in the collection of the data as each interviewer will tend to interpret the questionnaire and collect the data in his or her own ‘personal’ way. As each interviewer works exclusively in one location, it is not possible to separate out the effect of the interviewer from the impact of the location itself. Unfortunately, it is not possible to estimate the size of the error due to the interviewer effect and thus to statistically account for this specific sort of error associated with the disaggregated estimates.

In the light of these shortcomings, it is suggested to interpret results with caution and not to exclusively rely on poverty maps for geographical targeting. Rather, they should be used with caution and together with other targeting approaches. We will graphically present the results at 8
which Nelson and Plosser (1982) called _trend stationary._ Here, p, is a deterministic linear trend plus a constant driftf a,2 > 0, then y, is an ARMA (2, 2).

If the cycle ', is just an AR(2), and C = O(constant slope), then y, is an ARIMA (2, ', 2). However in this case as well as in the previous ones, it is an ARIMA mode! with. restrictions on the parameters. An ARIMA (2, 1, 2) has _5_ parameter to be estimated while the basic structural model, with y2 _=_ **o2** = 0 has only 4.

# 3. TRENDS AND CYCLES IN THE AGGREGATE COMMODrTY PRICE INDEX

# 3.1 ANALYSIS OF THE CORRELOGRAMS.

Both the logarithm of the commodity p..ce index (CPI) deflated by the US Manufacturing price index (USMPI) and the log of CPI deflated by the UN Manufacturing unit value index (UNMUV) visibly show some decline over the whole period 1980-1986, particularly the latter. However, they show a great deal of randomness, too. There are peaks in the 1910's, in the '20's, the '50's, and the '70s (Figures IA).

In what follows, we will focus on log (CPW/UNMUV), hereafter LPV, on the ground that it appears as a better candidate for a real world commodity price index than log (CPVIUSMPI). Although the United States has certainly played a central role in the international trade in commodities during the whole century, a world-tradeweighted price index seems more representative. Mvloreover, since Cuddington and Urzda made the same choice, we would like to have a reference for an appropriate comparison of the results. 1 / The correlogram of LPV decays rather slowly, and at lag 14 it is not significantly different from zero. However, for longer lags, there is a substantial negative autocorrelation (see Figures 2A and 2B). Individual values are not

- 1/ The possibility of greater bias in the manufactured goods index than in the primary commodities index due to quality changes has to be acknowledged, which means a downwards bias in the deflated series.

9
tance than is often observed in the real world (Weibull, 1976). The most commonly used modified form is a negative exponential model such as: where I, _e_ is the potential accessibility indicator for location i based on the negative exponential distance decay function, most other parameters are defined as before, and the parameter a is the distance to the point of inflection of the negative exponential function.

There are several options for developing accessibility indicators depending on the choice of distance variables used in the computation. These include: (a) indicators based on Euclidean distance; (b) indicators incorporating topography; (c) indicators incorporating the availability of transport networks; and (d) indicators incorporating the quality of transport networks. In this analysis, we use network distance as the basis of the inverse weighting parameter and to incorporate information on the quality of different transportation links. Feasible travel speed and thus travel times will vary depending on each type of network link. A place located near a national highway will be more accessible than one on a rural, secondary road. The choice of the friction parameter of the access measure will therefore strongly influence the shape of the catchment area for a given point-i.e., the area that can be reached within a given travel time. This, in turn, determines the size of potential market demand as measured by the population within the catchment area.

We use the accessibility index developed in Lall et. al (forthcoming) as the market access indicator in this analysis. Their accessibility index describes market access using information on the Indian road network system and the location and population of urban centers (ML Infomap 1998). The urban centers database includes latitude and longitude coordinates and 1991 population for 3,752 cities with a total population of about 217 million. The digital transport network 14
Table A7: Heterogeneous effects of payment delays on durable spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|A<br>Assets|bove median:<br>Patience|
|1-day delay<br>8-day delay|-254.340<br>(1059.041)<br>430.667<br>(1242.231)|1951.916<br>(1361.749)<br>1912.118<br>(1608.583)|496.637<br>(1273.910)<br>-773.758<br>(1101.532)|440.985<br>(1223.070)<br>1481.456<br>(1368.852)|-776.129<br>(1406.079)<br>394.681<br>(1668.041)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|6507.55|4572.04|6861.69|5256.23|6107.70|
|R-squared|0.137|0.278|0.214|0.152|0.167|
|P-value: 1-day=8-day|0.569|0.979|0.262|0.494|0.495|
|Panel B:|Female|No previous|Far from|B|elow median:|
||HH head|bank use|branch|Assets|Patience|
|1-day delay|2015.864<br>(1591.954)|120.694<br>(1191.955)|1211.731<br>(1263.819)|419.958<br>(1367.684)|798.280<br>(1189.733)|
|8-day delay|-602.216<br>(1469.580)|-512.217<br>(1196.297)|1614.169<br>(1472.043)|-1077.400<br>(1320.772)|-563.747<br>(1257.438)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Immediate group|4796.40|6594.30|5079.37|6644.18|5825.00|
|R-squared|0.144|0.089|0.119|0.106|0.083|
|P-value: 1-day=8-day|0.059|0.598|0.787|0.227|0.211|
|P-value: equal efect of 1-day vs 0-day|0.211|0.288|0.680|0.990|0.372|
|P-value: equal efect of 8-day vs 0-day|0.573|0.202|0.179|0.161|0.631|
|P-value: equal efect of 1-day vs 8-day|0.058|0.744|0.354|0.177|0.191|

The top panel includes respondents from female-headed households and those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values in the bottom three rows are from tests of equality of the 1-day delay coefficients, the 8-day delays, and the differences between 1-day and 8-day delay coefficients, respectively, in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 49