rafmacalaba/datause-encoder-data huggingface dataset:
# **Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan**

Shawn Cole Martin Kanz Leora Klapper<sup>_∗_</sup> July 23, 2012

**Abstract**

This paper uses a series of experiments with commercial bank loan officers to test the effect of performance incentives on risk-assessment and lending decisions. The paper first shows that, while high-powered incentives lead to greater screening effort and more profitable lending, their power is muted by both deferred compensation and the limited liability typically enjoyed by loan officers. Second, the paper presents direct evidence that incentive contracts distort judgment and beliefs, even among trained professionals with many years of experience. Loans evaluated under more permissive incentive schemes are rated significantly less risky than the same loans evaluated under pay-for-performance.

**EPOL, FSE JEL** : D03, G21 J22, J33, L2 **Keywords** : loan officer incentives, banking, emerging markets

> _∗_ Harvard Business School; World Bank Development Economics Research Group and World Bank Development Economics Research Group, respectively. E-mails: scole@hbs.edu, mkanz@worldbank.org, and lklapper@worldbank.org. We wish to thank the cooperating financial institution for providing us with the data on loan applications used in this paper. For helpful comments and suggestions, we thank Philippe Aghion, Andreas Fuster, Victoria Ivashina, Raj Iyer, Michael Kremer, Rohini Pande, Jose Liberti, Daniel Paravisini, Enrichetta Ravina, Andrei Shleifer, Antoinette Schoar, Erik Stafford, Jeremy Stein, and conference and seminar participants at Bocconi (Carefin), University of Chicago (Advances with Field Experiments), IGIDR Mumbai, IIM Bangalore, FIRS (Minneapolis), LBS European Winter Finance Conference 2012, NBER Corporate Finance, NBER Risks of Financial Institutions, University of Virginia (Darden), and the World Bank. Samantha Bastian, Doug Randall, and Wentao Xiong provided excellent research assistance. Financial assistance from the Kauffman Foundation, the International Growth Center, and the Harvard Business School Division of Faculty Support and Research, is gratefully acknowledged.The opinions expressed do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
||(1)<br>Actual gr|(2)<br>Actual gr|(3)<br>Actual gr|(4)<br>Actual gr|(5)<br>Actual gr|(6)<br>est6|
|---|---|---|---|---|---|---|
|post|-5.166<sup>_∗∗∗_</sup>|-7.508<sup>_∗∗_</sup>|-0.911|-1.760<sup>_∗_</sup>|-2.941<sup>_∗∗_</sup>|0.559|
||(1.876)|(2.891)|(1.267)|(0.949)|(1.378)|(1.055)|
|Fixed efects|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|
|Period|Five years|Five years|Five years|Ten years|Ten years|Ten years|
|Sample|All sample|Weak institutions|Strong institutions|All sample|Weak institutions|Strong institutions|
|SE|Robust|Robust|Robust|Robust|Robust|Robust|
|Obs- Country Year)|432.00|242.00|179.00|779.00|440.00|313.00|
|R-sq|0.11|0.19|0.35|0.08|0.12|0.18|

inst ~~p~~ ol, Standard errors clustered at country level _∗ p <_ 0 _._ 10, _∗∗ p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01 Table 10: Actual annual growth rates, Polity, different event samples is lower following a discovery. However, when we examine longer comparison groups we find no evidence for an average divergence from zero, showing in Table 11. Similarly, when we divide the sample by institutional quality we find no evidence for an average long run effect.

Our findings on the growth effects of resource discoveries suggest that the conventional estimation approaches used in the literature may overlook a short-run presource curse effect. For example, Smith (2015) notes: “One possible way to define the event year is the year of discovery, but this does not make sense for a growth regression, since GDP is not directly affected by the discovery of resources, but rather their extraction”. Instead Smith focuses on estimating the resource curse effect from the year of exploitation, which can occur 8 or more years beyond the year of discovery. We find, to the contrary of Smith’s hypothesis, a systematic short-run growth under-performance following discoveries across our sample. Indeed, where comparisons are made for growth in the period prior and post the year of production starting, such estimates risk contamination by the presence of short-run presource curse effect.

It is worth noting, when extended to the long-run which captures the effects of oil production as well as discovery, we find no evidence for a long run (unconditional) resource curse effect. As found by Warner (2015), oil production can mechanically raise the aggregate level of growth in an economy, even where the rest of the economy fails to benefit fully from the boom (for example when measured in terms of growth in non-resource GDP). Our findings would be consistent with this observation.

||(1)<br>AGrowth|(2)<br>AGrowth|(3)<br>AGrowth|
|---|---|---|---|
|post|-0.029|0.004|0.009|
||(0.921)|(0.966)|(0.870)|
|Controls|CFE YFE|CFE, YFE|CFE YFE|
|Standard errors|Robust|Robust|Robust|
|Event sample|Fifteen years|Twenty years|Full sample|
|Obs- country year|1097|1382|1946|

Actual growth rate * p¡0.10, ** p¡0.05, *** p¡0.01 Table 11: Actual annual growth, long run, different event samples 25
Turnovsky, Stephen J.; Shalit, Haim; and Schmitz, Andrew (1980), Consumer’s Surplus, Price Instability, and Consumer Welfare _. Econometrica_ , 48, 135-152.

U.S. Department of Energy (2007), Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations. U.S. Climate Change Science Program, Synethesis and Assessment Product 2.1a. Washington DC: U.S. Department of Energy.

Waugh, Frederick V. (1944), Does the Consumer Benefit from Price Instability? _Quarterly Journal of Economics_ , 58, 602-614.

World Bank (2009), _World Development Report 2010: Development and Climate Change_ . Washington DC: The World Bank.
- 2.9

-

# **RAEEBCES**

Imran, M and R C Duncan,<sup>(1988?). 'Opthal Export Taxes for Perennial Crop Erporters',</sup> mhoeo, Internatinal **Comfodltles**<sup>Market Division, WorldBank,</sup><sup>**Washington**DC.</sup> Konings, P. (1986). The State<sup>andRuralClass Formation hI hana:AComparative Analysis,</sup> KPI (distr. by Routledge and Kean Paul,<sup>London).</sup> MIrrlses. J. A. (1988), 'OptUal<sup>Comodity Price hntrventIon", _beo, WorldBank,August.</sup> Thabatabal, Hamld, (1908) 'Economc<sup>Decline, Acces to Food and Structural Adjustment In</sup> Ghana'. World Ecoromc Programeo<sup>Rosech WorkIng Paper 1O-G/WP8O,</sup> hnternatIonal Labor Organisatlon.
|||Table 2: Desc|riptive statistics|||
|---|---|---|---|---|---|
|**Obs.**<br>|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|**Storms**||||||
|_FATALITIES_|289|226.723|1232.926|0.000|14600.000|
|_AID p. c._|289|0.792|3.770|-1.022|45.062|
|_GDP p. c._|289|10082.490|10136.190|648.365|34364.500|
|_GINI_|289|43.056|9.049|25.216|64.796|
|_POP (in tsd.)_|289|217809.900|344382.700|2745.429|1271085.000|
|<br>_POPDENS_|289|230.988|279.104|1.976|1024.392|
|_OPEN_|289|49.772|30.740|12.672|218.796|
|_MAGNITUDE_|289|155.886|57.410|45.000|300.000|
|_NATHAZ - RISK_|289|3.566|3.187|0.000|7.855|
|_LATITUDE_|289|28.085|13.582|2.165|61.063|
|_ELEVATION_|289|601.594|449.689|34.259|1839.950|
|**Floods**||||||
|_FATALITIES_|1109|125.649|956.011|0.000|30000.000|
|_AID p. c._|1109|0.758|3.079|-1.023|33.328|
|_GDP p. c._|1109|7496.065|8185.186|389.963|35107.520|
|<br>_GINI_|1109|44.203|8.990|25.216|64.796|
|_POP (in tsd.)_|1109|249678.700|372010.200|2299.124|1271085.000|
|<br>_POPDENS_|1109|131.481|162.153|1.943|972.416|
|_OPEN_|1109|45.678|29.358|9.275|228.874|
|_MAGNITUDE_|1109|1.654|1.691|0.000|8.000|
|_NATHAZ - RISK_|1109|4.282|2.528|0.026|9.854|
|_LATITUDE_|1109|25.347|14.976|0.422|61.063|
|_ELEVATION_|1109|778.552|552.312|85.476|2565.382|
|**Earthquakes**||||||
|_FATALITIES_|365|381.715|2637.994|0.000|40000.000|
|_AID p. c._|365|1.552|5.562|-0.005|40.578|
|_GDP p. c._|365|5907.357|5978.683|482.528|35107.520|
|<br>_GINI_|365|44.055|8.756|25.216|64.196|
|_POP (in tsd.)_|365|45.020|25.089|10.418|214.423|
|<br>_POPDENS_|365|280543.200|420309.300|2434.262|1271085.000|
|_OPEN_|365|109.141|114.505|1.976|989.455|
|_MAGNITUDE_|365|6.159|0.881|4.000|8.000|
|_NATHAZ - RISK_|365|2.279|2.001|0.000|7.369|
|_LATITUDE_|365|25.274|14.519|0.422|53.887|
|_ELEVATION_|365|980.628|545.394|85.476|2565.382|

21
Policy Research Working Paper 10093

# **Abstract**

In Iraq, the Multi-Purpose Cash Assistance programs have been instrumental in reaching the households most affected by the conflict with the Islamic State of Iraq and the Levant in areas where the coverage of the government’s social safety net (SSN) programs remain limited. In the evolving context, however, short-term Multi-Purpose Cash Assistance programs will require eventual integration in some form with the government’s social safety net programs to continue reaching vulnerable households affected by the conflict. As an initial step, this paper proposes an analytical pseudo-proxy-means test (PPMT) tool to bridge the targeting differences between the government’s cash transfer program and the humanitarian Multi-Purpose Cash Assistance programs. Using the common proxies between the cash transfer targeting formulas of the humanitarian agencies and the government, the pseudo-proxy-means test provides each Multi-Purpose Cash Assistance beneficiary’s probability of being eligible for the government’s cash transfer program under different expansion scenarios. When applied to the existing humanitarian beneficiary database, the results of the pseudo-proxy-means test tool suggest the potential for both significant referral numbers and a sequenced referral strategy.

This paper is a product of the Poverty and Equity Global Practice. 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 lphadera@worldbank.org; dsharma5@worldbank.org; mwaipoi@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
Table 5. Financial Development (Credit/PIB), Property Rights and Poverty (Poverty gap)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Private Credit/GDP (log)|-0.115|-0.105|0.463|0.660|
||(3.15)***|(3.40)***|(1.66)*|(2.26)**|
|GDP per capita ($US, log)|-0.576|-0.595|-0.539|-0.640|
||(7.83)***|(7.22)***|(6.46)***|(8.00)***|
|Property Rights (log)|||-0.292|-0.469|
||||(1.16)|(1.83)*|
|Private Credit/GDP (log) * Property Rights (log)|||-0.181<br>(2.23)**|-0.228<br>(2.69)***|
|Inflation (log)||0.732||0.739|
|||(4.71)***||(3.89)***|
|Trade Openness (log)||0.090||0.171|
|||(0.67)||(1.38)|
|Constant|5.351|5.070|6.089|6.687|
||(11.53)***|(8.36)***|(7.22)***|(7.10)***|
|Observations|69|69|65|65|
|Number of countries|34|34|33|33|
|Chi square (Wald test)|134.01|265.23|132.64|172.40|
|Pseudo R2|0.44|0.44|0.44|0.44|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 6. Financial Development (M3/PIB), Property Rights and Poverty (Headcount poverty)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|M3/GDP (log)|-0.145|-0.166|-1.355|-1.239|
||(1.76)*|(1.82)*|(5.56)***|(5.54)***|
|GDP per capita ($US, log)|-0.450|-0.479|-0.546|-0.571|
||(8.20)***|(7.61)***|(12.65)***|(11.83)***|
|Property Rights (log)|||-0.877|-0.695|
||||(4.01)***|(3.33)***|
|M3/GDP (log) * Property Rights (log)|||0.299|0.248|
||||(4.48)***|(3.75)***|
|Inflation (log)||0.504||0.594|
|||(5.13)***||(7.28)***|
|Trade Openness (log)||-0.053||-0.026|
|||(0.61)||(0.46)|
|Constant|6.572|6.977|10.670|10.427|
||(25.82)***|(21.03)***|(13.77)***|(14.34)***|
|Observations|70|67|66|64|
|Number of countries|36|34|34|33|
|Chi square (Wald test)|166.04|165.53|272.75|423.44|
|Pseudo R2|0.39|0.46|0.44|0.48|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

15
severe allergic responses in exposed populations (Zahm, Ward and Blair, 1997).<sup>1</sup> Many pesticides used in Bangladesh are banned or restricted under international agreements (NOVIB, 1993; SUNS, 1998; SOS-arsenic.net, 2004). In addition, several studies have shown that inadequate product labeling and farmers’ lack of information have led to widespread overuse or misuse of dangerous pesticides. Substantial anecdotal evidence suggests that pesticide poisonings and ecological damage have become common in Bangladesh (Ramaswamy, 1992)

# **2.2  IPM in Bangladesh**

Bangladesh’s IPM activities began with rice in 1981, and the FAO played a strong catalytic role with government officials and the donor community. The program provided capacity-building for the Department of Agricultural Extension (DAE), introduced Farmer Field Schools, and trained representatives of local NGO’s. Subsequently, the government and NGO’s initiated several IPM projects for rice and vegetables with donor funds.<sup>2</sup> At present, the Plant Protection Wing of the DAE is responsible for the implementation of IPM activities (FAO, 2001).

# **3.  Survey Data**

The research reported in this paper is based on a large survey of Bangladeshi farmers, carried out by the World Bank in the summer of 2003. We used structured questionnaires to collect information on conventional and IPM farming techniques, pesticide use and practices, applicator precautions and damage-averting behavior, health effects and environmental impacts.<sup>3</sup> To provide greater depth, we also interviewed 139 randomly-selected rice farmers who currently use IPM.

> 1 See Appendix I for details.  The FAO study has found particularly high shares for carbamates and organophosphates, which pose the health hazards noted above.

- 2 To date, major IPM programs in Bangladesh have included the DAE-UNDP/FAO IPM Project (BGD/95/003); DAE-DANIDA Strengthening Plant Protection Services (SPPS) Project; Command Area Development Project (CAD); CARE-New Options for Pest Management; CARE-Integrated Rice and Fish Project (INTERFISH); AID-Comilla’s Integrated Pest Management Project; USAID-funded IPM Collaborative Research Support Program; and FAO’s Regional Cotton Project

- 3 The survey was designed and supervised by the World Bank team, and conducted by the Development Policy Group in Bangladesh.  To minimize reporting bias, the survey was conducted under the agreement that the team would not reveal the identity of the farms surveyed or the respondents who participated.

4
values of a targeting variable) can take several discrete values, the ROC curves will consist of a series of linear segments corresponding to these discrete values. The greater the area under an ROC curve and the closer it is to the left-hand side vertical and top horizontal axes, the greater is the efficacy of a diagnostic test. The closer a ROC curve is to the 45-degree line, the weaker is its efficacy.

To our knowledge, the only previous use of ROC analysis for analyzing the impact of poverty targeting is by Wodon (1997) using household survey data from Bangladesh. As Wodon points out, unlike conventional statistical hypothesis tests ROC analysis can take account of continuous as well as categorical targeting variables. However, like conventional hypothesis tests, ROC analysis can only be employed for dichotomous outcome variables (so that it can be used for the conventional poverty headcount but not for higher-order poverty measures such as the poverty-gap and squared poverty gap).

Figure 4 shows an example of two pairs of ROC curves drawn using data from 1998 VLSS. Since the curve for the index of radio and television ownership in rural areas lies everywhere above and to the left of the curve for the education level completed by the household head, Panel (a) shows that use of the television and radio ownership variables unambiguously dominates that for education of the household head as a targeting variable. Note that the ROC for the index of radio and television ownership has four linear segments corresponding to the four values of the index, while the ROC curve for the head's education has six segments corresponding to the six educational levels a household head may complete. Panel (b) shows the contrasting situation in which the ROC for quintiles of land area and the number of children per household cross, in which case neither variable unambiguously dominants the other from a targeting perspective.'<sup>7</sup> Of course, it will also usually be the case that some combination (linear or otherwise) of the two variables will further improve the efficacy of a test.

> households as non-poor) while I minus the specificity of a test is the same as the probability of a Type II error (incorrectly classifying a non-poor household as poor). In many respects this is akin to describing whether "a glass is half-empty or half-full", in that both are simply different methods of presenting the same data.

> 17 This is rather similar to the problems encountered in making unambiguous comparisons of inequality when the Lorenz curves cross or in making comparisons of inequality when cumulative income distribution curves cross.

3 1
**Table 2b.  Sector Value Added in Tanzania  (Percentage unless otherwise indicated)**

||<br>La|<br>bor|||<br>G|<br>DP|
|---|---|---|---|---|---|---|
||Skilled<br>labor|Unskilled<br>labor|Capital|Natural<br>Resources|USD in<br>billion|% of total|
|**Business Services**|||||||
|Air Transport|10.3|44.3|45.5|0.0|0.0|0.1|
|Communication|15.5|22.6|61.9|0.0|0.1|0.8|
|Insurance|24.9|36.5|38.5|0.0|0.0|0.1|
|Business Services nec|0.0|74.3|25.7|0.0|1.0|6.7|
|Financial Services nec|22.6|33.1|44.3|0.0|0.0|0.0|
|Transport nec|11.0|47.7|41.3|0.0|0.2|1.1|
|Water Transport|8.2|35.3|56.5|0.0|0.1|0.8|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|4.4|23.9|71.7|0.0|0.6|4.5|
|Energy and Minerals|0.2|1.5|88.8|9.4|0.3|2.4|
|Food Products|8.3|43.5|48.2|0.0|1.1|7.5|
|Petroleum and Coal Products|0.0|0.0|0.0|0.0|0.0|0.0|
|Other Manufacturing|2.7|14.1|83.2|0.0|0.1|0.8|
|Textile and Apparel|6.8|45.6|47.6|0.0|0.1|1.0|
|Wood and Paper Products|3.4|23.3|73.3|0.0|0.1|0.4|
|**CRTS**|||||||
|Agriculture and Forestry|0.5|60.4|38.1|1.0|4.6|32.2|
|Other Services|32.3|46.5|21.2|0.0|2.9|20.3|
|Trade|10.6|45.6|43.8|0.0|2.6|18.0|
|Utilities|6.9|12.9|80.2|0.0|0.5|3.3|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

41
those who were reported as not having started childbearing had indeed not started. The accuracy rates were similar across groups.

Of course childbearing is an imperfect proxy for risky sexual behavior. While information on childbearing by girls is likely to be relatively good, information on whether boys have had children is likely not as good. Abortion and anal sex could both reduce the correlation between the incidence of childbearing and the risk of HIV infection. Neither is easy to collect data on, especially as abortion is illegal in Kenya.

The teacher training program could potentially change some students’ behavior in ways that increase the probability of childbearing without increasing the probability of HIV/AIDS infection. For example if, following the training, girls are convinced of the importance of marriage and being faithful, they might be more likely to marry, and to have a child. If their partner is faithful as well, they may be less likely to get HIV/AIDS. For this reason, we also collect data on whether those exposed to the program are more likely to be married. We are also in the process of conducting home visits to obtain more information on the father of the child.

Whatever its limitations, data on childbearing is likely to be more reliable than self-reported sexual behavior data. Ultimately, however, the best measure of the effect of the program would be biomarkers – tests for HIV infection or other STDs. Collecting biomarker data was difficult to do while students were still minors. We hope to be able to conduct such a study in the future.

15
years, 10-19, 20-29, 30-39 and 40+ respectively. This implies that the two youngest cohorts would be the ones to be most affected by any changes in the society in general and the educational system in particular in the post-transition period. What we see from the figures is indeed a very interesting pattern of a clear increase in “distance” from the perfect mobility matrix for the two youngest cohorts in all three countries. In Georgia, this is part of a steady monotonic decline across all cohorts, while for both Armenia and Azerbaijan, the decline for the youngest two cohorts follows an improvement in the 55-64 group (for Armenia) and the 45-54 group (for Azerbaijan).

Figure 14: “Distance” from perfect mobility, by age cohorts <!-- Start of picture text --> Armenia Azerbaijan Georgia<br>Age 25-34 Age 35-44 Age 45-54<br>Age 55-64 Age 65+<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source_ : Tiwari et al (2017) Interpreting this finding requires a slightly deeper look at what kind of mobility this measure is capturing. As discussed earlier, in most other analyses of intergenerational educational mobility, the methodology that is often used is the regression of years of education of the child against that of the parent.

The coefficient of interest is � , which is the measure of the intergenerational persistence of educational attainment. An alternative measure is simply the correlation between the two (say, ��. Incidentally, the relationship between the two can be written as follows: 31
Table 5: The 2012 reform effect on Part-time work

||Logit|Logit|Logit|Logit|Probit|OLS|
|---|---|---|---|---|---|---|
|Mom(six-year-old) x Post2012|0.0114|0.00624|0.00377|0.00379|0.00494|0.0116|
||(0.0129)|(0.00679)|(0.00493)|(0.00499)|(0.00556)|(0.00746)|
|Mean (Dep. Var.)|.052|.052|.052|.052|.052|.052|
|St. Dv. (Dep. Var.)|.223|.223|.223|.223|.223|.223|
|Demographic controls|No|Yes|Yes|Yes|Yes|Yes|
|Year fxed efects|No|No|Yes|Yes|Yes|Yes|
|State fxed efects|No|No|Yes|Yes|Yes|Yes|
|Region by year unemployment rate|No|No|No|Yes|Yes|Yes|
|N|15920|15920|15920|15920|15920|15920|

Standard errors in parentheses Source: EU-LSF (2010-2019). Standard errors are clustered. The sample consists of employed mothers of children between 3 and 4 or 6 years old.

Table 6: The 2012 reform effect on Number of hours per week usually worked in main job

||OLS|OLS|OLS|OLS|
|---|---|---|---|---|
|Mom(six-year-old) x Post2012|1.099|0.625|0.772|0.771|
||(0.543)|(0.578)|(0.610)|(0.615)|
|Mean (Dep. Var)|35.229|35.229|35.229|35.229|
|St. Dv. (Dep. Var.)|13.287|13.287|13.287|13.287|
|Demographic controls|No|Yes|Yes|Yes|
|Year fxed efects|No|No|Yes|Yes|
|State fxed efects|No|No|Yes|Yes|
|Region by year unemployment rate|No|No|No|Yes|
|N|15920|15920|15920|15920|

Standard errors in parentheses Source: EU-LSF (2010-2019). Standard errors are clustered. The sample consists of employed mothers of children between 3 and 4 or 6 years old.

20