rafmacalaba/datause-encoder-data huggingface dataset:
- 17 -

ERY was recommended for a particular sector (TMailand). In a few cases the Bank recommended a uniform nominal tariff with additional time bound tariff protection for new/infant industries (Morocco, Thailand).

Two opposite concerns are expressed in some Bank studies, one of which sits uneasily with the general thrust of recommendations towards a uniform tariff. These have to do with the tariff treatment of inputs and the implications for protection of upstream and downstream industries. In a number of cases it was felt that tariff escalatin (lower tariffs on inputs relative to outputs) contributed to excessive protection to downstream industries since ERPs were rendered greater than the nominal tariff. It was felt in the case of Thailand that the development of upstream industries was retarded if input tariffs were low while in the case of Zaire the Bank study noted that the high degree of tariff escalation created incentives for uneconomic assembly type industries. Raising the tariff on inputs while lowering final good tariffs was the recommended solution.

The opposite concern and implication for tariff structure was expressed in Pakistan and Indonesia where it was felt that high tariffs on inputs saddle downstream industries with high costs, lower the ERP, and retards downstream development. The implication in this case is to lower input tariffs although in other cases it may lead to suggestions to raise final good tariffs. In the case of Bangladesh the anomaly of higher tariffs on inputs than final goods was sought to be eliminated by lowering input tariffs.

PHILIPPINES: In 1980 the Bank recommended tariff reductions to lower the average ERP while narrowing the band between high and low tariffs. The Bank recommended tar.ff reform to reduce average ERPs and to narrow the range of nominal tariffs to a band of 10-50 percent. It also urged a phased program of liberalization of QRs on non-essential consumer imports and unclassified imports.
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weighted average of the study estimates taking into consideration the sampling error and the between-study variability. Note that the within-study variability is not taken into account and only the average estimate is used in the quantification of the health benefits.

Since the analysis applies to Mexico City, articles based on Mexico City population were given double the weight of international cases because they are thought to better reflect the Mexican reality in terms of susceptibility and sociodemographic characteristics. The potential influence of long-term exposures on health, and especially in the reduction of life expectancy, could be one of the most influential end points. This is discussed in more detail in box _5._ 1. An example of mortality due to acute exposure to PM1o is presented in box 5.2.

Table 5.1 summarizes the ER functions and the background rates for health effects as they are used in the present study. The exposure response coefficients in the second and third columns of the table come from the meta-analyses described above. (For some health endpoints, a meta-analysis was not possible and the source of the estimate is a single study.) The studies used to derive each coefficient reported in table 5.1 may be found in Cesar and others (2000). In the next two sections we will include only nonoverlapping health endpoints to prevent double counting of the benefits from air pollution reduction.

# **Box 5.1 Premature Mortality Due to Long-Term Exposure to PM10**

Cross-sectional studies and cohort studies have been conducted to study the effect of long-term exposure to particles and premature mortality. Cohort studies are preferable to cross-sectional studies because cohort studies can control for other factors related to mortality such as smoking status or occupation. To date three cohort studies in the United States have followed a significant number of individuals for at least 8 and up to 17 years (Dockery and others 1993, Pope and others 1995, Abbey and others 1993). During the study period, air pollution data were gathered from local monitoring stations to estimate average pollution exposures for individuals within the study. The 1999 U.S. EPA Report to Congress on the Benefits and Costs of the Clean Air Act 1990 to 2010 and other authors prefer to use the Pope and others (1995) study, which is based on extensive evaluation of confounders, as well as a larger sample size and greater geographic coverage. This cohort study found a concentration-response coefficient of 17 percent for a 24.5 ug/m<sup>3</sup> increase of PM25, or a 6.6 percent increase for a 10 pg/iM<sup>3</sup> increase of PM2 5. For comparison with PM10 studies, this is equivalent to an increase in mortality rates of 3.84 percent for a 10 ,g/m<sup>3</sup> increase of PM10.

_23_
# Table 3: Cross-Country Regressions

The dependent variable is the ratio of the number of bankruptcies to the number of firms. The regressions are estimated using ordinary least squares with robust standard errors. _LN(GDPPC)_ is the log of GDP Per Capita. _GGDP_ is the 1-year growth rate of real GDP. _D_SYSTCRISIS_ is a dummy equal to I in the case of a systemic bank crisis (Caprio and Klingebiel, 2000). _RINTEREST_ is real interest rates. _D_MKTORIENT_ is a dummy equal to I if the country is market-oriented and equal to 0 if the country is bank-oriented (Demirguc-Kunt and Levine, 1999). _TIME_ is the number of business days required for a business to become operational (Djankov et al., 2000). _SME SHARE_ is the percentage of total employment in the SME sector (Klapper and Sulla, 2002.) t-statistics are in parentheses, *, *, and *** indicate significance at 10%, 5%, and 1% respectively.

||(1)<br>**Base**<br>**Regression**|**(2)**<br>**Market**<br>**Orientation**|**_(3)_**<br>**Business**<br>**Entry**|**(4)**<br>**SMEs**|**_(5)_**<br>**With**<br>**Dummies**|
|---|---|---|---|---|---|
|_Constant_|0.8904<br>(1.53)|0.1730<br>(0.30)|2.2350***<br>(3.73)|4.1320***<br>(5.75)|-0.2300<br>(-0.45)|
|_LN(GDP_|0.0052***<br>_PC,.,)_(5.34)|0.0053***<br>(5.78)|0.0040***<br>(4.03)|0.0054***<br>(6.26)|-0.0034<br>**(-0.68)**|
|_GGDP,,_|-0.0704***<br>_(-2.59)_|-0.1003***<br>(-3.47)|-0.0994***<br>(-3.44)|-0.0206<br>(-0.75)|-0.0101<br>(-0.52)|
|_DSYSTCRISIS,-,_<br>_D_SYSTCR!SIS,,_|0.0692<br>(0.16)|0.3191<br>(0.77)|-0.2555<br>(-0.63)|0.4553<br>(1.14)|0.7054***<br>(3.05)|
|_RINTEREST,,_](-0.63)|-0.0052|-0.0087<br>(-1.07)|0.0323***<br>(2.90)|-0.0199<br>(-1.62)|0.0088<br>(1.34)|
|_D MKTOR_<br>_D_MKTORIENT_|_IENT_<br>________|~~~1.1239***<br>(4.98)||||
|_TIME_|||-0.0208***|||
|_SME_SHARE_<br>_ _<br>_|_**_**_ _ _ _ _ _ _ _ _**_** <br>_ _<br>_ _ _ _ _ _ _|_ _|_ _ _ _ _ _ _ _ _ _ _ _|-0.0576***<br>(-7.5<br>4)||
|_CountryDummies_|No|No|No|No|Yes|
|_YearDummies_|Yes|Yes|Yes|Yes|Yes|
|_Adj.R-Squared_|0.14|0.24|0.31|0.36|0.91|
|_Observations_|273|273|265|243|273|

29
they may be observed without indicating who will be studied or when this might occur. However, it is not clear that there are always practical means to communicate this to the service providers (we return to the logistics of communication below). Moreover, this may lead to strangers, often easy to recognize in a remote rural setting, receiving higher quality services than usual. Thus, the clearest ethical guideline for such types of research pertains not to the rights of individuals being observed, but the interests of the wider community which these individuals are expected to serve, since any such stealth visits should be designed in a manner that does not crowd out or otherwise reduce access for the general population.

_Further Discussion on Communications with Subjects beyond Informed Consent_ Survey respondents or trial participants are not only expected to be informed of the purpose of the study, they are generally expected to be informed of the results. Chambers (2001), among others, makes the point that surveys take the time of respondents and to a degree interviews are, therefore, extractive, even if the consent to be interviewed is voluntary.<sup>7</sup> Communication of the results as they pertain to the community then can be viewed as a minimal compensation for participation, although even the process of an interview serves a degree of communication since the interview may raise awareness of health or other concerns even in the absence of a direct intervention. The advice to share results is, however, more practical for focus groups than for broad surveys. The statistical power of surveys is determined by the interplay of the number clusters or communities included in the sample and the number of respondents in each cluster. Transport costs – which are often inputs into software used for survey design – are a key factor

> 7 However, this observation does not, by itself, imply that such data collection is unethical; taxes are also extractive yet viewed by most economists as justifiable to fund public goods even if they entail various distortions which economists seek to minimize.  The value of the tax on the respondent’s time, then, is a function of the public value of research.

22
# Debiasing _on a Roll_ : Changing Gambling Behavior through Experiential Learning

Martin Abel<sup>_∗_</sup> Shawn Cole<sup>_†_</sup> Bilal Zia<sup>_‡§_</sup> February, 2015

### **Abstract**

This paper tests experiential learning as a debiasing tool against gambling and lottery behavior in South Africa. The study implemented a simple, interactive dice game that simulates worsening winning odds of rolling sixes as more dice are added to the game. The analysis exploits two levels of exogenous variation, first from random assignment into the debiasing game, and second from the number of rolls it takes to obtain the sixes. Treated individuals who needed above-median number of rolls to obtain simultaneous sixes are significantly less likely than the control group to gamble or play the lottery in the following year. The converse is true for individuals who needed below-median number of rolls, suggesting a perverse treatment effect among this group. The analysis also finds suggestive evidence that the debiasing affected the sensitivity to varying winning odds. Changes in entertainment utility or risk preferences cannot explain these findings, rather the results are consistent with changes in risk beliefs.

## **JEL Codes: D03, D12, D81, G02**

**Keywords: Debiasing, Experiential Learning, Behavioral Economics, Financial Education**

> _∗_ Harvard University. Email: abel@fas.harvard.edu. Corresponding author. 79 John F. Kennedy Street, Cambridge, MA 02138, United States. Phone: (207) 319 2967

> _†_ Harvard Business School and NBER. Email: scole@hbs.edu

> _‡_ World Bank. Email: bzia@worldbank.org

> _§_ Acknowledgments: This paper greatly benefited from discussions with and comments from Xavier Gine, David Laibson, David McKenzie, Matthew Rabin, Andrei Shleifer, Richard Zeckhauser, and conference participants at Yale University, the University of Cape Town, the Rotterdam Behavioral Finance Conference, and the World Bank. Megan Blair, Reinhard Schiel, and Sarah van Vliet provided excellent research assistance. Special thanks to Luke Crowley for creative contibutions to the debiasing intervention. We also thank the International Labor Organization and the World Bank for financial support. All errors and omissions are our own.

<mark>1</mark>
remedial assistance provided only by the teacher had little impact on either test scores and the inclass remedial assistance from the teaching assistant had no impact on overall English test scores. While the other treatments have persistent impacts, they are significantly less than the impacts for those participating for two years, except for the split class approach, whose persistent impacts are as large or larger than the two-year impacts.

As with the Beg et al. (2020) results for remedial education in fourth and fifth grades, the benefit-cost ratios here are generally high except in the cases where the impact on test scores is not significantly different from zero. The ratios for the split classes are the lowest when using the tests of two-year participants, but among the highest when using the persistence results. This reflects both the stability of the test score results and the fact that the persistence results cost half as much as the main results.

Duflo, Kiessel, and Lucas allow for heterogeneous effects by gender in an appendix in their paper, but do not report the coefficient for the base case (boys), only the difference between girls and boys. This difference is always positive, that is, the effect is larger for girls, and statistically significant in three out of four specifications. Further, the authors report that the coefficient for boys is mostly statistically insignificant, so the only effect seems to be for girls.

Turkson and Baffour (2020) calculate benefit-cost ratios for two of the experimental arms in the Duflo, Kiessel, and Lucas (2020) study: in-class remedial instruction without an assistant and in-class remedial instruction with an assistant. They use somewhat different assumptions, particularly regarding future labor earnings, and the overall test score improvements rather than only for English, to find benefit-cost ratios of 8.3 and 6.0, respectively, for the two interventions.

Overall, these two studies suggest that improving the quality of primary schooling through remedial teaching at the right level provides good value for money in Ghana.

# **c. Ghana School Feeding Programme**

Aurino et al. (2020) report on an expansion of the Ghana School Feeding Programme (GSFP) to previously uncovered districts. The expansion was randomized by school for two years; the control schools also received the intervention after the experimental study ended. GSFP pays caterers to provide one meal per day to students in primary and junior secondary school.

The study took place over two years beginning with the 2014–15 school year, though baseline data were collected before the 2013–14 school year. The study sample was drawn from 32
|Plot manager has primary edu|0.0581|-0.363**|-0.00541|0.0442|0.0488|-0.0537|-0.0207|0.0463|
|---|---|---|---|---|---|---|---|---|
||(0.114)|(0.167)|(0.0873)|(0.121)|(0.102)|(0.110)|(0.0875)|(0.115)|
|Intercropped|-0.652***|-0.288**|-0.263***|-0.146|-0.276***|-0.365***|-0.250***|-0.347***|
||(0.155)|(0.142)|(0.0940)|(0.155)|(0.0777)|(0.122)|(0.0879)|(0.111)|
|Soil fertility index|0.311**|-0.0517|0.182**|0.131|-0.0515|-0.245|0.0858|0.00963|
||(0.148)|(0.306)|(0.0730)|(0.115)|(0.122)|(0.175)|(0.0615)|(0.0778)|
|Constant|5.088***|5.963***|4.269***|4.919***|4.598***|5.359***|4.441***|4.408***|
||(0.458)|(0.431)|(0.326)|(0.360)|(0.402)|(0.351)|(0.243)|(0.298)|
|Observations|1,678|1,281|2,971|2,444|1,630|1,240|2,845|2,345|
|Adjusted R-squared|0.287|0.259|0.309|0.268|0.304|0.349|0.234|0.183|
|District FE|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 _Table A 5_ : Malawi - Regression of realized harvest and expected harvest on inputs, household, and plot manager characteristics.

# **MALAWI - Yield regressions: Realized Maize Harvest vs. Expected Maize Harvest**

|||**ln(Mai**<br>|**ze Yield)**<br>|||**ln(Expected**<br>|**Maize Yield)**<br>||
|---|---|---|---|---|---|---|---|---|
||IHPS19|IHPS15|IHPS13|IHPS10|IHPS19|IHPS15|IHPS13|IHPS10|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|log Plot area (ha)|-0.707*|-0.828|-0.967**|-0.998***|-0.0961|-1.007**|-0.715***|-2.053***|
||(0.386)|(0.554)|(0.385)|(0.277)|(0.413)|(0.467)|(0.257)|(0.432)|
|log Tot. labor days/ha|0.164***|0.379|0.302**|0.210***|0.123|0.124|0.334***|0.00704|
||(0.0509)|(0.264)|(0.123)|(0.0510)|(0.0769)|(0.115)|(0.0909)|(0.0765)|
|Any labor hired|0.689***|0.433|0.317**|0.191*|0.285**|0.434***|0.437***|-0.137|
||(0.171)|(0.312)|(0.125)|(0.0970)|(0.140)|(0.126)|(0.139)|(0.123)|
|log Maize seeds (kg/ha)|0.369***|-0.154|0.142**|-0.0368|0.567***|0.372*|0.297***|0.495***|
||(0.0564)|(0.237)|(0.0593)|(0.0465)|(0.0910)|(0.202)|(0.0443)|(0.0484)|
|Plot has improved maize variety|0.113|0.0669|||0.226**|0.110|||

54
(corresponding to YA in Figure 1), **an** assumption that is clearly at<sup>odds</sup> with Levine's Table 2 (derived frots Borus) showing differing training costs for these three training modes. Thus little credence can<sup>be given to the</sup> particular rates of return results presented in Levine's paper,<sup>a conclusion</sup> that is buttressed by the findings, presented earlier in the<sup>present paper,</sup> that earnings do not differ significantly across the main<sup>training modes,</sup> over seven years of post-training experience in the labor market.

Summary and Implications The earnings of 1233 !ormer trainees in Israel were monitored<sup>over</sup> a seven **year** period. Earnings functions were estimated in<sup>the form of a</sup> series of v. ltiple regressions, with annual earnings as the<sup>dependent</sup> - variable and type of training program attended vocational secondary<sup>school,</sup> traditional apprenticeship, factory-based industrial school<sup>and short</sup> - Ministry of Labor teenage course included amongst the independent variables. Generally, no significant differences in the earnings<sup>of the</sup> graduates of the different modes were found, in a series of<sup>regressions run</sup> on annual earnings data for the years 1969-1975.

These findings viewed against the relative costs of alternative training modes confirm those of an earlier study by Michael Borus relating to a much shorter post-training earnings follow-up period; they suggest that,<sup>on</sup> cost-effective grounds, the balance of training provision<sup>for youth in Israel</sup> is not e*,ficient. The vast majority of skilled workers are trained in relatively expensive vocational secondary schools which, perhaps surprisingly, offer no earnings advantages (even in the longer<sup>term) over</sup> cheaper, work-related, training programs. On cost-effective grounds, the desirability of a redirection of the training effort in Israel<sup>towards more</sup> direct job-related training, and in particular the formal apprenticeship,<sup>is</sup> indicated.

17
Hence, all the estimates for fractions of measurement error should probably be treated as lower bounds.

Table 2 presents the IV estimates of the fraction of the variance that is due to measurement error. These results will be discussed in more detail in the subsequent sections. Nevertheless, the table already shows that measurement error is responsible for a large fraction, typically 30%-60%, of cross-sectional variance. Measurement error accounts for an even larger share, generally around 55%-80%, of the variance of income and expenditure shocks. Measurement error seems to be about as important for consumption as for income and is generally higher in Russia than in Poland.<sup>6</sup>

**Table 2: Instrumental Variable Estimates of the Fraction Measurement Error**

||Russia 19|94-199|8|Poland 19|93-1996|
|---|---|---|---|---|---|
|Fraction measurement error in:|Consumption|Inc|ome|Consumption|Income|
|Logs, Period 1|0.447 (0.073)|0.564|(0.064)|0.346 (0.023)|0.402(0.035)|
|Logs, Period 2|0.531_(0.055)_|0.676|(0.055)|0.386 (0.024)|0.363(0.036)|
|Logs, Period 3|0.517 (0.049)|0.561|(0.057)|0.401 (0.022)|0.332(0.039)|
|Logs,Period4|0.320 (0.056)|0.618|(0.043)|0.381 (0.021)|0.285(0.033)|
|Log difference, period 2-1|0.751 (0.098)|0.797|(0.094)|0.682 (0.051)|0.534(0.084)|
|Log difference, period 3-2|0.746 (0.413)|0.869|(0.181)|0.710 (0.039)|0.546(0.073)|
|Log difference, period 4-3|0.779 (0.111)|0.699|(0.190)|0.728 (0.046)|0.42(0.94)|

Note: All measures are for equivalent adults. The equivalence scale is household size to the power of 0.75 (e=0.75). Data consist of balanced panels, 1993-1996 for Poland and 1994-1998 for Russia.

# 4. **Underlying Inequality**

It is common to measure inequality in living standards by inequality in income or expenditure across individuals in a given month. However, income or expenditure in a given month is only an imprecise measure of the living standard of a household. Inco:me or expenditure could be misreported or could reflect transitory events. Whenever measured income (or expenditure) is an imperfect indicator of true living standards, the inequality in measured incomes or expenditures will exceed the inequality in underlyi:ng

> 6 We find the same pattern of measurement in food expenditure, though measurement error in food expenditure tends to be higher than that in total expenditure. See appendix table Al for details.

15
captures whether a drought occurred in cluster _c_ during the growing months leading up to the harvest season ( _t_ ) that coincided with the child’s birth.<sup>21</sup> _Dc_ ( _t_ +1) is similar except it refers to the year after the birth year. _Wi_ is a vector of four dummy variables indicating whether the child was born in a household in one of the top four wealth quintiles (the lowest wealth quintile is the contrast variable). The month of birth x cluster fixed effect is captured by _θcm_ . _α_ is the constant. The error is clustered at the cluster level.

Wealth quintiles are relative measures and cannot be compared across countries. However, after controlling for cluster fixed effects, they may be used to estimate the relationbetween wealth status and the likelihood of for households in the same ship stunting cluster. Two points are worth noting. First, as expected, after controlling for geographic and spatial factors, differences in wealth are associated with differences in child anthropometric measures. When compared with the poorest quintile, every other quintile is significantly less likely to be stunted. Second, drought impacts are comparable to a household falling from the second wealthiest quintile to the poorest quintile. On average (for the five countries taken together), for stunting outcomes, the impact of a drought during a child’s birth season (10 percentage points) is similar to the difference between being born to a household in the poorest quintile instead of the second wealthiest quintile (8 percentage points).

While these numbers are large, three important caveats need to be kept in mind. First, there is limited variation in wealth status within a cluster. Second, the wealth quintiles are constructed (c.f. Rutstein (2015)) in a manner that suggests that this DHS wealth index is probably a very rough approximation of our idea of household wealth — in particular, no attempt is made to account for differences in the quality and price of the underlying assets, especially the two most important rural assets (land and livestock). Third, these impacts are averages across countries. However, what we would like to stress is that the magnitude of the impact of a drought on stunting is substantial, especially given that droughts (based on our definition) occur more than a fifth of the time in all five countries.

Table 3, which reports results from Equation 2 below, uses a more complete set of controls, including maternal biological factors _Mi_ — which include the mother’s height, weight and age and the birth order and the birth interval —- as well as all the assets ( _Ai_ ) that are used to construct wealth indices, instead of the quintiles of the wealth index.

> 21As discussed above, this varies by province, we use the cluster here to make the notation simpler.

12