rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8305

# **Abstract**

This research note investigates the impact of phone reminders on response rates in the context of a web-based survey in an international organization, the World Bank. After randomly assigning treatment to 248 survey participants, the study finds an intention-to-treat effect of 19.86 percentage points. Given a relatively low treatment compliance rate (31 percent), the estimated average effect of treatment-on-thetreated is even larger, corresponding to an increase of 64 percentage points. Therefore, if ways can be found to increase treatment compliance, high response rates are attainable. This may lead World Bank surveyors to turn to sample surveys more often, reducing survey overload in the institution.

This paper is a product of the Independent Evaluation Group . 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://econ.worldbank.org. The author may be contacted at lodewijk. smets@gmail.com _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
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on ofi¢;10 ni un. -I m..tcj n 'LI , but at d Waye low enough to attract only those with low pre-intervention incomes. Ravallion (1989) presents a theoretical analysis of such schemes, while Ravallion (1991) surveys some of the empirical evidence, concluding that such schemes do, indeed, act as effective targeting devices. But Ravallion's analysis, and most of the other literature on selftargeting schemes, ignores the effect that they can have on intrahousehold allocation, as we shall see in Sectiot, 5.

# 4. INDICATOR TARGETING AND INTRAHOUSEHOLD ALLOCATION

The simple analysis of indicator targeting, as conducted, for example, in Kanbur (1987), is in terms of lump sum transfers to individuals with different values of an indicator variable (e.g., region or landholding). All individuals with the same indicator value are treated identically, since, _ex hypothesi,_ there is no other information to differentiate between them. Un this assumption, Kanbur (1987) derives leakage minimizing rules based on statistical information derived, for example, from sample surveys. Individual incentive effects are ignored in the simple analysis. Kanbur and Keen (1990) show how the rules are modified when labor supply is elastic, so that incentive effects matter and the intervention itself changes the distribution of pre-intervention income.

Consider now the assumptions underlying indicator targeting in light of the literature on intrahousehold allocation. Suppose the indicator variable is such that not all members of the household have
Olken, Benjamin (2006) “Do Television and Radio Destroy Social Capital? Evidence from Indonesian Villages”, BREAD Working Paper No. 130 Oster, Emily (2006) “HIV and Sexual Behavior Change: Why not Africa?”, Mimeo. University of Chicago.

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Roberts, Peter, KC Shyam and Cordula Rastogi (2006) “Rural Access Index: A Key Development Indicator” _Transport Papers_ No. 10, Transport Sector Board, The World Bank.

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Staal, S., Delgado, C., Baltenweck, I., and Kruska, R. (2003) “Spatial aspects of producer milk price formation in Kenya: a joint household-GIS approach”, _Paper presented at the 24_<sup>_th_</sup> _Conference of the International Association of Agricultural Economists_ , Berlin, August, 2000.

Staal S., Baltenweck I., Waithaka M., de Wolff T. and Njoroge L. (2002) “Location and uptake: Integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya”, _Agricultural Economics_ 27(2): 295-315.

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Turner, Andrew (2006) “Introduction to Neogeography”, _O’Reilly Short Cuts_ , December. USAID Timor-Leste (2004) “East Timor Completes the World’s First GPS-Based Census”, USAID Timor-Leste Small Grants Program Highlights, <u>http://timorleste.usaid.gov/PrintVersion/SGArchive51Print.htm</u> [Accessed December 28, 2006] VanWey, Leah, Ronald Rindfuss, Myron Gutmann, Barbara Entwisle, and Deborah Balk (2005) “Confidentiality and spatially explicit data: concerns and challenges”, _Proceedings of the National Academy of Sciences_ 102, pp.15337-15342.

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nor their affiliates, nor shifted profits. Instead, we focus on measuring the importance of tax expenditures as a share of firms’ reported net profit, holding evasion and profit-shifting opportunities fixed. We also note that Dyreng et al. (2017) show that ETRs have decreased in the same way for domestic and multinational firms, suggesting that this distinction may not be first-order.

# **3 Effective Tax Rates and Firm Size**

## **3.1 Aggregate Tax Expenditures**

We construct firm-level effective tax rates (ETRs) with the data and methods described in section 2. The last three columns of table 1 show for each country the top statutory tax rate, the average ETR for all firms (imputing a zero ETR for loss-making firms), and the average ETR for profitable firms. We use the difference between the top statutory tax rate and the firm-specific ETRs multiplied by their net profits to compute firm-level tax expenditures. By aggregating across firms, we obtain the total tax expenditures, which we express as a share of each country’s GDP. This measures the forgone revenue due to corporate tax expenditures absent behavioral responses.

Figure A.2a displays our aggregate corporate income tax expenditure estimates, ranking countries by GDP per capita. For countries in our sample tax expenditures represent 0.87% of their GDP on average. The magnitude varies across countries: Albania and Ethiopia’s CIT expenditures are close to zero, while all other countries’ tax expenditures are over 0.5% of GDP, and Costa Rica and Honduras display expenditures in excess of 1.7% of GDP. In Figure A.2b, we include for comparison data from an additional 66 countries, available in the Global Tax Expenditure Database, which collects information from official tax expenditure reports (Redonda, von Haldenwang and Aliu, 2022).<sup>9</sup> The average CIT tax expenditures in this extended sample of 79 countries are slightly lower than in our sample but still substantial at 0.63% of GDP.

Figure A.2c shows the correlation between our tax expenditure estimates and the estimates from government tax expenditure reports, for the 11 countries with overlap. The correlation is high but below one: in particular, our method yields higher estimates for African countries. Note that the methods to compute official tax expenditures differ substantially across countries: reduced

> 9Following international standards, tax expenditures should be computed yearly. In practice, developing countries do not systematically produce these reports, and computation methods vary significantly by country.

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**Chart 8: Cross-Section Regression. Scatter Plot of Growth on Uphill Flows**

<!-- Start of picture text --> KOR<br>IND<br>GRC LKA<br>MUS<br>PAN<br>CRI MLTMYSROM<br>CHLPER<br>ESPFIN TURTHAPHLIDN<br>AUS<br>TGO GTMSLVCOL USANZLISLSENNLDITAMWIPRTGBRSWECAN BRA MEX PAK<br>DNK FRA<br>SGP<br>ISR<br>HND<br>HKG MARDEUAUT JAM<br>NIC<br>JPN<br>ARG<br>PRY<br>KEN<br>JOR<br>CHE MDG<br>URY<br>-1.5 -1 -.5 0 .5 1<br>Uphill Flows Residuals<br>coef = .01146931, (robust) se = .00344606, t = 3.33<br>.03<br>.02<br>.01<br>0<br>Growth residuals<br>-.01<br>-.02<br><!-- End of picture text --> This is the partial scatterplot of the regression in column 2 of Table 1A. Controls include initial income, institutional quality, primary school enrollment, and capital stock.

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For instance, Colombia has 23 regional poverty lines, whereas Chile, Ecuador, Haiti, and Nicaragua have only one national poverty line. Finally, there are some extreme values of national poverty lines that could substantially increase the mean value of all poverty lines in LAC. For instance, Montevideo-Uruguay’s moderate poverty lines is $14.9 per person per day, which is considerably higher than the rest of the moderate poverty lines in LAC.

Since the number of subnational poverty lines varies by country and the extreme values of official poverty lines and size of the population of the subnational regions play an important role (according to Figure 3), we take into consideration these factors when estimating regional extreme and moderate poverty lines. Therefore, since the _mean_ is more sensitive to outliers, we use the _median_ of subnational urban poverty lines for some specifications to account for extreme values. In addition, we also consider other specifications that weight all poverty lines by the number of individuals residing in their corresponding urban subnational regions to account for the size of the population of each region and the number of poverty lines by countries.

Table 3 presents a number of specifications and the corresponding regional comparable extreme and moderate poverty lines in LAC. The first and second columns present the mean of extreme and moderate poverty lines, while the last two columns show the corresponding median values. The first two rows consider the mean and median of all subnational poverty lines shown in Figure 3, while the following two rows are based on national averages of subnational poverty lines shown in Table 2. The first and third row show the mean and median of official poverty lines weighted by the population of _subnational_ regions within each country, while the second and fourth rows show the unweighted mean and median poverty lines. The last two rows present the mean and median of the underlying poverty lines used to estimate the currently-used $2.5 and $4 per person per day poverty lines updated to 2011 PPPs using national CPIs, both unweighted and weighted by the _national_ population of each country.

Regional extreme poverty lines based on the most up-to-date official extreme poverty lines are approximately $2.8 per person per day on average, while regional moderate poverty lines are approximately $5.9 per person per day on average. However, there is variation across different specifications in the table. Extreme and moderate regional poverty lines based on mean values of the official poverty lines tend to be higher than those based on median values (approximately ten percent on average), which provides evidence of the impact that outliers have on regional averages of poverty lines. Similarly, unweighted poverty lines tend to be higher than the population12
-Teedwe-Non-tadwrs Pay Dlfferncss 11-

# **Table** III

# Rogression Coefficients of Teacher-Location Interaction Dummy Varisbles

|Dependent Variable<br>Sample<br>----------------------------------------------------------------|(1)<br>Base<br>Salaries<br>(All)|(2)<br>Base<br>Salaries<br>(Govt)|(3)<br>Total<br> <br>Salaries<br>(All)<br>__-|(4)<br>Total<br>Salaries<br>(Govt)<br>----------__|
|---|---|---|---|---|
|Teachers in Abidjan|0.1874<br>(0.97)|0.2998*<br>(1.73)|-0.0408<br>(-0.20)|0.1301<br>(0.72)|
|Teachers in Other Locations|<br>0.2071*<br>|0.3148*<br>|-0.0128<br>|0.1149<br>|
||(1.65)|(2.94)|(-0.10)|(1.03)|
|Non-Teachers in Abidjan|-0.0116<br>(-0.15)|0.1587*<br>(1.87)|-0.1013<br>(-1.22)|0.0624<br>(0.71)|
|Non-Teachers in Other Loc.|||||

T-values between parentheses.

* indicates statistically significant coefficients at<sup>0.10 level.</sup>

The sample underlying the estimation in Table 2 contains employees with **at least** six years of education. Currently, however, the minmu level of education required in Cote d'Ivoire to be a school<sup>teacher</sup> is 10 years. The teachers in the sample with less<sup>education could thus be</sup> seen as belonging to the 'old generation' of teachers.<sup>In order to see</sup> whether they represent 'abnormal' cases, we reestimated the earnings equation over the subsample of employees with at<sup>least 10 years of</sup> education. Table 4 reveals that the differential between teachers and other employees is essentially the same for that<sup>subsample as for the</sup> original subsample (the t-value for the difference between<sup>the coefficients</sup> in the two equations is below the critical value).
allows us to control for endogeneity of some choice variables. Vulnerability and resilience are defined based on whether a household is “poor”, “never poor”, “escaped poverty”, or “fell into poverty” over the three-survey waves and measured in the third wave in 2019.

We found that smallholder farmers in Zambia are more exposed and therefore vulnerable to droughts, the more prevalent climate shock rural households face. The extent of exposure differs both spatially and over time, but on balance, Zambia’s Southern and Western Provinces are most exposed to climate shocks. About three-quarters of all smallholders are vulnerable and nearly one-quarter are resilient nationally. There are important differences between Provinces, with the Eastern, Muchinga, Northern, and North-western Provinces being the most vulnerable. Our multivariate analysis suggests significant correlation between (i) climate shocks and worsened vulnerability, and (ii) between climate shocks and reduced resilience. We also found some evidence suggesting that assets and education reduce vulnerability and increase resilience among smallholder farmers in Zambia, and that femaleheaded households are more vulnerable and less resilient. Using CSA is associated with increased chances that a household will be resilient in the short-term.

We therefore conclude that most smallholder farmers in Zambia are exposed to climate shocks and are vulnerable, and that climate shocks are associated with vulnerability and resilience. The current use of CSA is associated with improved resilience among smallholder farmers. We draw two main implications from these findings: First, based on the positive association between CSA and resilience, there is an urgent need to invest in CSA research and development to scale out and scale up context-specific CSA practices. In the context of CSA as adaptation options, this implies a need for timely delivery of climate information services, perhaps through innovative digital platforms.

Second, given the significance of climate shocks on resilience and vulnerability, there is need for more investment in risk mitigation strategies, such as weather indexed insurance and targeted social cash transfers and how to make these work effectively for smallholder farmers. This will enable farmers to access coverage against climate shocks that make their households vulnerable and reduce their resilience. While there have been attempts to implement insurance programs in Zambia, it is not clear if they have been successful or how to make such programs work for smallholders. Other important complementary elements include facilitating asset accumulation and education that can increase resilience.

# About the authors

Ngoma <u>(h.ngoma@cgiar.org) was Research Fellow and Lead, Climate Change and Natural</u> Resources Management at the Indaba Agricultural Policy Research Institute, Lusaka, Zambia when this work was done; and is now Agricultural Economist at the Southern Africa Regional Office, International Maize and Wheat Improvement Center (CIMMYT).

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term health gains; among these, Alderman _et al._ (2006) found that de-worming brings about weight improvements in pre-school-age children in Uganda, in a district that borders the PSDP study area around Lake Victoria. Children in the Uganda study were between 1 and 7 years old, but the study did not disaggregate effects by age; however, the study by Stoltzfus _et al._ (2004) in Zanzibar did. They show that children who were treated when less than 30 months old gained the most. Within this young cohort, incidence of mild wasting<sup>10</sup> was cut nearly in half, from 36% in the control group to 18% in the treated group; older children did not improve nearly as much. The authors took note of this surprising aspect of their results: “The benefits thus occurred in the age group at highest risk for anemia and growth retardation, but in the age group with the lowest intensity of helminth infections.” The literature thus lays the groundwork for the present study. The simple question I ask is whether children who were infants or not yet born at the time of school-based deworming in Kenya received spillover benefits from the original PSDP intervention, by experiencing early childhood in a low-worm-infection environment. They were at the right age for longterm impacts to be large; recent literature suggests that worm infections are important even in the first year outside the womb; and the PSDP study showed that children who were simply near the dewormed schools also benefited from spillovers.

# **3 Data collection, 2009-2010**

In 2009 and 2010, a field team in Kenya collected height, weight, and migration data from more than 20,000 children at all of the deworming project schools in Samia and Bunyala districts of Kenya’s Western Province.<sup>11</sup> For a subset of just over 2,400 children, the team also conducted detailed cognitive assessments. Children from the same age cohorts were included during both data collection years: in 2009, this meant including every child between the ages of 8 and 14; in 2010, it meant every child between the ages of 9 and 15.

These age cohorts were chosen both because they were still enrolled in primary school at the time of data collection, and because of how these cohorts align with the original

> 10 _Mild wasting:_ having weight-for-height worse than one standard deviation below average, _WHZ < −_ 1 11 Here, we follow the original Primary School Deworming Program school lists, and pursued those that were not flooded (causing a temporary program disruption) at the time of the intervention.

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# sV. T5 MOST AND THU LEAST EFFECTXVE SCHOOLS AND CLASSES IN ZJC MATHiMATICS AND ENGLISH

Having **chosen** what **we** feel are the best models describing between school differences ln ZJC Mathematics and English achievemnt, it is now possLble to use the random parameters or residuals estimated for each school to detect outliers and to rank the schools in their 'adjusted' performance.

The scatterplots of the standardized school level residuals against the predicted values of the ZJC grades averaged by school enable one to identify any outliers, or exceptLonal schools, in the final **models** Graph 1 is such a scatterplot for Mathematics, and outside the clustering in the middle range of values for the standardized school level rosiduals, one can see four schools above, say the 3.0 mark, and four schools below, may, the -1.5 mark. Table 4.1 identifies these oight schools and lists their standardized school level residuals as well **as** their actual and predicted grades on the 1989 ZJC Mathematlcs. SimLlarly, for EnglILh, Graph 2 depicts the four positive outliers above, say, the 1.5 mark and the three negative outliers from about the -1.5 mark and below. Once more these schools **are** identlfied in Table 4.1.

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**Table 4: Effect** **<u>of computerization and stamp duty reduction on transaction volume</u>**

||**To**<br>|**tal **<br>|**Sa**<br>|**les**<br>|**Mor**<br>|**tgages**<br>|
|---|---|---|---|---|---|---|
|Comp.|0.059|0.021|0.021|0.022|0.093|0.097|
||[0.035]|[0.038]|[0.035]|[0.037]|[0.096]|[0.096]|
|Stampduty Reduction|0.512|0.496|0.531|0.518|0.173|0.198|
||[0.048]**|[0.053]**|[0.051]**|[0.057]**|[0.115]|[0.125]|
|Comp.* Urban Share||0.38||0.052||0.709|
|||[0.081]**||[0.086]||[0.215]**|
|Stampduty Red*Urban||0.018||0.051||0.219|
|||[0.078]||[0.094]||[0.208]|
|Observations|2,587|2,587|2,587|2,587|2,587|2,587|
|R-squared|0.82|0.82|0.89|0.89|0.58|0.58|

_Note:_ All regressions include year and SRO fixed effects. Robust standard errors clustered at the SRO level. * significant at 5%; ** significant at 1%.

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the base case. It falls by 15-17.5% under the first two policy packages involving the BRT but falls much less under the third package involving the ring road.

The second measure is that popularized by the Texas Transportation Institute commonly known as the TTI index. It measures congestion as the ratio of actual travel time to free-flow travel time. We take free-flow travel time to be 100 km/hr. (62 miles per hour). The base value of this measure is in the 7.4-10.6 range, but it improves by 44-50% under the BRT (packages 1 and 2) and by only 6-13% under the ring road (package 3).

The third measure is the aggregate congestion externality, favored by economists. It is the total monetary value of the congestion delays caused by all traffic in LBP per year per consumer in the model. In the base year this amounts to 283,399 LBP per consumer per year (approximately 189 $ US). The aggregate amount is equivalent to 2.11% of the Beirut region’s gross product and 3.96% of the per consumer income. Under the policy packages that include the BRT the aggregate congestion externality falls by about 53% but by only 5% under Policy package 3.

**TABLE 9:** Improvement in congestion measures relative to Base

||**Base**|**Policy package 1**|**Policy package 2**|**Policy package 3**|
|---|---|---|---|---|
||||**% changes from ba**|**se**|
|**FLOW TO CAPACITY R**|**ATIO**||||
|**MB-MB**|10.02|-17.78|-17.45|-1.72|
|**MB-GB**|9.04|-16.19|-16.14|-3.26|
|**GB-MB**|9.14|-17.15|-16.86|-2.11|
|**GB-GB**|9.31|-15.51|-15.47|-3.75|
|**TTI INDEX FOR ROAD T**<br>|**RAFFIC**<br>||||
|**MB-MB**|10.63|-49.51|-48.80|-5.87|
|**MB-GB**|7.42|-46.01|-45.89|-10.93|
|**GB-MB**|7.72|-48.13|-47.50|-7.19|
|**GB-GB**|8.23|-44.48|-44.38|-12.49|
|<br>**CONGESTION EXTERN**|**ALITY**||||
|**Aggregate congestion**<br>**externality (in billion**<br>**LBP/year)**|408.83|-53.82|-52.88|-5.70|
|**Congestion externality**<br>**per consumer**<br>**(LBP/year)**|283,399|-53.82|-52.88|-5.70|
|**Congestion externality as**<br>**a percent of Beirut gross**<br>**product**|2.11|-54.61|-53.91|-5.07|

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