rafmacalaba/datause-encoder-data huggingface dataset:
Table F.3: Survey over-reporting, estimated by the mean difference between survey and validation measures ( _y_<sup>∆</sup> )

|||Potenti|ally sensitive|behaviors||Exp|enditure|s|
|---|---|---|---|---|---|---|---|---|
||All (0-4)|Steal|Marijuana|Gamble|Homeless|All (0-2)|Video|Phone|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Full sample|-0.10|0.02|-0.03|-0.11|0.02|-0.27|-0.19|-0.08|
||_0.17_|_0.57_|_0.24_|**_0.00_**|_0.45_|**_0.00_**|**_0.00_**|**_0.00_**|
|Control group|-0.07|0.03|-0.02|-0.12|0.03|-0.50|-0.29|-0.22|
||_0.64_|_0.57_|_0.71_|_0.09_|_0.60_|**_0.00_**|**_0.00_**|**_0.00_**|
|Therapy only|-0.04|0.02|0.00|-0.07|0.02|-0.17|-0.13|-0.04|
||_0.80_|_0.77_|_1.00_|_0.29_|_0.77_|_0.08_|_0.07_|_0.53_|
|Cash only|-0.29|-0.02|-0.05|-0.20|-0.03|-0.23|-0.18|-0.05|
||**_0.04_**|_0.80_|_0.37_|**_0.00_**|_0.42_|**_0.03_**|**_0.03_**|_0.32_|
|Therapy + cash|0.02|0.03|-0.05|-0.03|0.06|-0.19|-0.16|-0.03|
||_0.91_|_0.57_|_0.37_|_0.66_|_0.25_|**_0.01_**|**_0.02_**|_0.48_|
|Observations|239|238|238|238|239|239|238|239|

_Notes:_ Columns 1 to 8 report the simple mean differences in the survey and validation measures for the full sample and for each treatment arm, along with p-values for as t-test of whether the mean is different from zero. We bold p-values _≤_ 0.05.

First, the results imply that the adjusted treatment effect of therapy and cash on sensitive behaviors overall is no lower than what we estimate with self-reported survey data, and may even be larger (Column 1). This holds true for each of the individual sensitive behaviors, save marijuana use. Despite the large standard errors introduced by the small validation sample, the adjusted treatment effect on all sensitive behaviors is larger and significant at the 1% level.

Meanwhile, the underreporting of gambling does not have a statistically significant association with treatment. Those who received cash alone underreported gambling to the surveyors more often than control group members, and so the measurement error in gambling is probably a combination of a general desirability bias as well as one correlated with treatments. A larger sample size would be needed to separate these more precisely.

In contrast, the slight underreporting of expenditures behaviors in the survey (seen in Table F.3 above) implies that the short term increase in survey-based expenditures due to cash could be due to measurement error correlated with treatment. The adjusted treatment effect of therapy plus cash is generally negative but not statistically significant (Column 6). We see a similar pattern with another expenditure-related item, homelessness, in Table F.4—the survey-reported decline in homelessness tends to disappear with adjustment.

# **References**

- Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. _Journal of the American Statistical Association 103_ (484), 1481–1495.

lvii
at three points in time, as described in the previous section. Hence, to maximize statistical power, we aggregate outcomes for individuals across time and create an index of lottery outcomes, following Kling, et al. (2007).<sup>17</sup> Table 4 reports regression results using both the lottery index and individual survey measures as the dependent variables. Column 1 shows that the average treatment effect is close to zero and statistically insignificant. This suggests that in the aggregate, gambling behavior did not change in the treatment group. Columns 2 to 4 report results of regression model 1 that estimates differential treatment effects between the high and low treatment groups. Column 2 shows that results vary significantly between the two groups – the low intensity group, where individuals needed fewer attempts to get two sixes, gambled significantly _more_ than the control group, while the high intensity group gambled significantly _less_ . The treatment coefficients (compared to the control group) are statistically significant at the 5% and 10% level, respectively. The difference between the high and low treatment coefficients is also significant at the 1% level as indicated by the p-value of the F-test reported in the bottom row of Table 4. Results do not change in magnitude or statistical significance when we control for additional baseline covariates in columns 3 and 4.

Table 4 also reports results of the same regression specification estimated separately for the three gambling outcomes that comprise the lottery index. All gambling outcomes show a consistent picture: the high treatment intensity group gambled less and the low treatment group gambled more relative to the control group. Estimated effects are large in magnitude. Individuals in the high treatment group gambled 29% less in a lottery offered after the debiasing intervention while the low treatment intensity group gambled 19% more compared to the control group in a lottery offered immediately after the intervention. It is noteworthy that the effects persist one year after the intervention – in the final follow-up, 10.7% of people in the low treatment intensity group reported playing the lottery compared to 7.4% in the control group. The equivalent figure for the high treatment intensity group was 4.7%. The p-values on the F-tests are statistically significant, hence showing that the differences between high and low intensity groups are significantly different from each other.

As discussed earlier, our main specification divides the treatment intensity into above and below median, because of differential stoppage rules. We can nevertheless examine how decisions vary when

> 17The index is created by adding values for the two indicator outcomes (midline, endline) and a standardized measure of the number of rounds played (immediate).

17
means that 175 million adults in India became account holders. But the sheer size of India's population means that there is still a long way to go before universal financial inclusion becomes a reality.

This paper updates the 2014 Global Findex indicator of account ownership using data from a new module of questions- _Measuring the Ease of Financial Access_<sup>8</sup> - added to the “Gallup 2016 India State Survey”. Our survey asked respondents if they had ever applied for an account at any financial institution and finds that 68 percent of adults—in surveyed states—have applied for an account at a financial institution at some point in their lives.

This data provides another gauge of financial inclusion and the degree to which groups such as the poor, women and youth are excluded from formal financial systems. The indicators in the survey are based on a state-level representative sample of 12,000 adults in 12 Indian states – Andhra Pradesh, Bihar, Chhattisgarh, Himachal Pradesh, Jharkhand, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh (including Telangana).<sup>9</sup> It is important to note that the survey is not nationally representative. However, it is representative for these states, which make up about 70 percent of the country's population, according to the latest government census.<sup>10</sup> The survey was carried out between January 2016 and March of 2016 by Gallup Inc. The target population is the entire civilian, non-institutionalized adult (age 15 and above) population living in these 12 states.<sup>11</sup>

# **_2.1 Account applications by individual characteristics: Inequality in gender and income_**

Survey results show differences by individual characteristics (Figure 1). Women do not have equal access to the financial system in India. The gender gap in application rates was 10 percentage points - 73 percent of men applied for an account, versus 63 percent of women (Figure 2). Madhya Pradesh lags with one of the largest gender gaps (21 percentage points). There is also an income gap, with 64 percent of poorer adults and 71 percent of richer adults

> 8 The questionnaire is shown in Appendix 1.

> 9 The states of West Bengal and Karnataka are excluded from the analysis in this paper due to data inconsistencies.

> 10 Summary statistics by state are shown in Appendix 2.

> 11 To ensure that the sample is representative of the adult population of the 12 states surveyed in India, weights based on available population demographics were used. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the post-stratification weights which corrects for age, gender, education, caste and urban/rural to correct for nonresponse error.

5
debt ceilings in the context of Fiscal Responsibility legislation, and the issuance by the Central Bank over the years of prudential regulations on the exposure of banks to the private sector have been instrumental in curtailing the rise in municipal indebtedness, despite the deterioration since early 2003 .

Figure 3. **Municipal indebtedness and primary budget balance, 1998-2004** 12-month flows, in per cent of GDP <!-- Start of picture text --> 3.0 0.3<br>2.5 0.25<br>2.0 0.2<br>1.5 0.15<br>1.0 0.1<br>0.5 0.05<br>0.0 0<br>Net debt (left axis) Primary balance (right axis)<br>Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04<br><!-- End of picture text --> _Source_ : Central Bank of Brazil.

25. In addition to these constraints on the ability of local governments to finance the provision and upgrading of infrastructure, regulatory uncertainty in some sectors has discouraged private investment.

- The private sector has traditionally been involved in service delivery in the case of urban transportation, predominantly through concessions. Regulatory uncertainty and constraints on pricing that simultaneously meets the requirements of private investors and the purchasing power of the population has deterred greater private sector involvement in other areas.

- In the case of water/sanitation, public investment has been cut back over the years without an offsetting increase in private investment. This can be attributed predominantly to a lack of clarity about which level of government is responsible for service delivery and regulation in the sector.<sup>24</sup> Investment, both public and private, has also been discouraged by the externalities associated with the provision of sewerage and water treatment services, and because water/sanitation networks are costly, investment maturities are long, and rates of return are relatively low. This is particularly the case in the metropolitan regions, which straddle municipal borders. These trends are important

> 24 . According to the 1988 Constitution, the municipalities are in charge of granting concessions for “local” public services, while the federal and state governments should guarantee efficient and well-regulated water supply and sanitation services. The 1995 Concession Law provided more guidelines for private concessions but did not clarify which level of government is in charge of water and sanitation provision. The Concession law challenged the monopolies that state companies enjoyed for 25 years, stating that the municipalities also have the right to grant concessions or enter into licensing agreements for “local” services or provide these services themselves. Prior to 1994, a decline in the inflation-adjusted value of user charges contributed to lower investment capacity. See Parlatore (2000), for more information.

19
# PAYING FOR **URBAN** SERVICES

# A **STUDY OF WATER VENDING AND WILLINGNESS** TO PAY FOR **WATER** IN **ONITSHA, NIGERIA**

# I. INTRODUCTION

1.01 For most water utilities and donor agencies the actual water supply situation in many Third World urban areas is typically something of a mystery. Where do households obtain their water? How much water do different types of households use? What do they use the water for? How much do households pay for water, if anything? What does that payment represent as a proportion of household income? How much would they be willing to pay for improved water services? Most water utilities simply do not know the answers to such basic questions. Conseq- ntly, new systems are generally planned and designed with little understanding of household water demand behavior. Instead, engineers and planners tend to rely on very simplistic assumptions about what determines per capita water use and affordability.

1.02 Unfortunately, urban water schemes often fail to achieve the goals set for the number of households to be connected to the water system, the amount of water produced, and the proportion of costs recovered--and the gap between expectations and accomplishments is often great (World Bank, 1988). '-he lack of adequate data on household water demand appears to be one of the reasons for these shortfalls.

1.03 However, it is not necessary to plan, design, and manage water systems in the dark. Relatively simple, rapid reconnaissance surveys of household water demand behavior have been devised to nrovide policyrelevant information to water utility managers in a timely fashion. This paper describes one such survey. A case study of water vending and willingness to pay which was carried out in Onitsha, Nigeria, in July and August 1987, illustrates how such studies can be conducted and what kinds of information they can provide.l

1.04 The purpoF of this particular study was to estimate the willingness of households to pay for water so that the state water authority could make a more informed decision on how much to charge its customers. Stated more simply, the general manager of the water utility

- 1/ This paper is the first of several reports based on the findiags of a research project on willingness to pay for wacer in Anambra State, Nigeria, funded by the World Bank and the USAID Water and Sanitation for Health Project (WASH). In addition to the research in Onitsha--part of which is presented in this paper--surveys were conducted in Enugu and in rural areas in collaboration with the Department of Economics, University of Nigeria, Nsukka.
<!-- Start of picture text --> i}<br>15 |<br>|<br>12<br>|<br>fo)<br>= |<br>89|<br>5<br>aI<br>>|<br>n3a ° ||<br>2<br>|<br>|<br>3|<br>|<br>|<br>|<br>0 |<br>-49 -42 -35 -28 24 “14 7 0 7 14<br>Birth year since affected by compulsory law<br>[~~ Sample average within bin —® Polynomial fit of order 4<br><!-- End of picture text -->
2 from pressures from interest groups, there could be considerable friction in the trade, and competition policy nexus. Thus the need for a coherent design, coordination if not integration and strict oversight, to avoid capture and distortions. This paper argues that there are good reasons to worry about the antidumping law and practice and that antidumping per se should be, if not eradicated, transferred to the jurisdiction of the competition policies agency and judged and ruled by the same economic criteria.

The plan of the paper is as follows. The next section II, discusses generic issues related to antidumping and competition policies and the background to the discussion that follows. Section III describes antidumping in the Uruguay Round. Section IV examines antidumping practice in Latin America. Section V examines competition policies in Latin America. Section VI provides conclusions regarding the integration of antidumping and competition policies.

**Table** 1: Changes in Trade Orientation (percent)

||Trade reform|Openne|ss index||Trade flows||
|---|---|---|---|---|---|---|
|Country|period|Period|Change|Period|Export growth|Import growth|
|Argentina|**1989-91**|**1988-94**|**8.01**|**1990-94**|**_5.63_**|**31.62**|
|Bolivia|**1985-88**|**1984-93**|**3.56**|**1986-93**|**8.46**|**4.76**|
|Brazil|**_1990-93_**|**1989-94**|**5.19**|**1991-94**|**6.99**|**9.97**|
|Chile|**1988-91**|**1987-94**|**4.17**|**1989-94**|**10.42**|**12.75**|
|Colombia|**1989-91**|**1988-94**|**5.93**|**1990-94**|**10.87**|**18.29**|
|Mexico|**1987-89**|**1986-94**|**6.16**|**1988-94**|**4.10**|**19.06**|
|Peru|**1990-92**|**1989-93**|**6.98**|**1991-93**|**4.21**|**8.23**|
|**Uruguay**|**1987-92**|**1986-94**|**6.14**|**1988-94**|**7.93**|**12.83**|
|Venezuela|**1989-91**|**1988-94**|**1.69**|**1990-93**|**7.15**|**6.26**|

**Note: The change in the openness index is measured as the average of the changes in the ratio between the year prior to the initiation of the liberalization episode and 1994. The change in export and import growth is measured as the average of the changes between the year following the initiation of the liberalization episode and 1994. Source: World Bank data.**
In model 2, the main effect of conflict onset on resource-related FDI is negative, but insignificant. The coefficient of the moderator for geographic scope is positive, but also insignificant. Hence, there is no evidence that the onset of a conflict affects resource FDI, independent of the scope of the conflict. However, the onset of a war has a negative effect on resource-related FDI, which is statistically significant at the 10% level. The onset of a nationwide war decreases resource FDI flows by 84.1%, whereas that of a localized has a small and statistically insignificant (effect size=-0.103, standard error = 1.183).

In model (3), the main effect of conflict onset on non-resource-related FDI flows is negative and significant at the 1% level. The geographic scope moderator is positive and significant at the 5% level. The onset of a nationwide conflict reduces greenfield FDI flows to the non-resource sector by 80.4%, but the effect of a localized conflict is not significantly different from 0 (effect size = 0.051, standard error = 0.547). The onset of a war has a strong negative effect, but this effect is again only statistically significant if the war is nationwide. This analysis suggests that even when we consider conflict onset rather than conflict intensity, we find support for the hypotheses proposed in the theoretical section of the paper. One additional insight we obtain is that unlike conflicts, war onset has a negative effect on all types of greenfield FDI.

**Table 5 Effect of Political Conflict and War on Total FDI, Resource FDI and Non-Resource FDI**

||Dependent Variable: Lo<br>|g greenfield FDI(in USD mi<br>|llions),LSDVC Estimation<br>|
|---|---|---|---|
||Total FDI<br>(1)|Resource-related FDI<br>(2)|Non-resource-related FDI<br>(3)|
|Conflict Dummyt-1|-1.985***<br>(0.528)|-0.562<br>(0.762)|-1.632**<br>(0.522)|
|Conflict Dummy*Localizedt-1|1.572*<br>(0.689)|0.123<br>(0.995)|1.683*<br>(0.679)|
|War Dummyt-1|-2.692***<br>(0.664)|-1.842+<br>(0.957)|-2.172***<br>(0.655)|
|War Dummy*Localizedt-1|1.755+<br>(0.999)|1.739<br>(1.441)|0.954<br>(0.986)|
|Observations|707|707|707|
|No. of Countries|90|90|90|
|Economic Controls|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|

Bootstrapped standard errors are in parentheses. *** p<0.001, ** p<0.01, * p<0.05, +p<0.10 31
city’s footprint is significantly smaller than Dar es Salaam, Kigali, Addis Ababa or Nairobi. It is important note, because any small changes in land cover classification are easily magnified when charted as a percentage. For a more accurate representation, it is important to examine the land total chart in Figure 17. This chart actually shows a fairly consistent presence of irregular settlements throughout the city’s footprint.

<!-- Start of picture text --> Figure 16 – Land Cover Classification for Dakar<br>Source: Author’s classification of high resolution imagery c 2015<br><!-- End of picture text --> 26
the treatment variable (including with bandwidths of 30, 60, 90, 120, 150 and 180 days). A second data issue is that since the ITN campaign started in the southern portion of the country, taking the better part of a year to cover the whole country, there is, by construction, a higher percentage of “ _post_ ” observations in southern and southern highlands zones. Therefore the main estimates are presented both with and without zone fixed effects.

Figure 1 demonstrates that the 2010 universal coverage campaign dramatically increased ITN ownership. Individuals surveyed after the campaign occurred in their community have dramatically higher rates of net ownership and use and are much more likely to report that a member of their household received a free net (or free ITN) than those interviewed before the campaign occurred.

I can identify the causal impact of the bed net distribution campaign under the assumption that respondents just before or just after the campaign are comparable in expectation. However, it is reasonable to think that individuals surveyed quite a long time before or long after the campaign might be different. To address such concerns, I only compare treated cohorts surveyed shortly before the campaign to control cohorts surveyed just after. Our main analysis focuses on a “bandwidth” of 90 days on either side of the campaign period, but results are highly robust to windows of as little as 30 days on either side of the campaign. Given the plausibly exogenous timing of the campaign’s arrival in a given district combined with the survey’s randomized timing, this allows us to recover causal estimates.

# **Estimation Strategies**

In this section, I provide estimates of the effect of bed net distribution on the main outcome variable of interest: approval ratings of local government officials and political leaders. I first present results graphically, and then with reduced form and instrumental variables specifications. Figure 2 shows the graphical representation of the impact of ITN distribution on approval ratings for 5 political leaders: village executive officer, village chairman, ward executive officer, ward chairman, and Panel Survey and ITN campaign, which are discussed further below.

14
Harttgen (2009) model implemented on the household survey. The predicted rates and the rankings by department produced by both methods are comparable, providing confidence in the robustness of the small area application to vulnerability (figure 2).

**Figure 2. Vulnerability estimates of survey implementation using two methods, by department**

<!-- Start of picture text --> 1<br>Tambacounda<br>0.9 Kaffrine<br>Kolda Sedhiou<br>0.8<br>0.7 Saint-Louis Louga Fatick Matam<br>0.6 Diourbel<br>0.5 Thies Ziguinchor Kedougou<br>Koalack<br>0.4<br>0.3<br>0.2<br>Dakar<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Vulnerability rate (%)<br>Survey Günther and Harttgen method<br>Vulnerability rate (%)<br>Poverty Map estimates<br><!-- End of picture text --> _Sources:_ Calculations using EHCVM (Enquête Harmonisée sur le Conditions de Vie des Ménages 2018–2019; Harmonized Survey on Household Living Standards 2018–2019), Living Standards Measurement Study, World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/4292; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-general-de-la-population-et-de-lhabitat-delagriculture-et-de-lelevage.

_Note:_ A household is considered vulnerable if it exhibits a probability of falling into poverty above 29 percent in one year (50 percent in two years). This applies to both census- and survey-based estimations.

Table 3 illustrates the predicted variance component of the survey and census data. In the household survey, the data included 540 of the 551 communes in Senegal, representing about 98 percent of the population. Table 3, columns 1 and 2 show that the mean and overall distribution of the predicted variances in the Günther and Harttgen (2009) approach and the small area estimation approach are similar across these communes. Table 3, column 3 shows the variance in the small area estimations imputed into the census, only including those communes sampled in the household survey, while column 4 presents the estimates for communes not sampled in the household survey. In general, the variance shown in table 3, column 4 is slightly lower, but is aligned well with the estimates in the overall sample even if their estimates do not include area effects.

16