rafmacalaba/data-use-mentions-extended huggingface dataset:
school attendance of boys and girls were in the first year of the program (round 3). In this round the experimental methods detected significant program impacts for both boys and girls.

The nature of the PROGRESA sample allowed us to investigate more deeply three potential explanations for these discrepancies between the RDD and experimental methods. Specifically, we tested whether spillover effects contaminate the comparison group, whether the RDD estimator is subject to evaluation bias, and whether there are any contamination problems with the control group due to announcement effects. Although none of these tests were able to reveal any problems with the comparison group used, it did turn out that the RDD method was able to yield significant impact estimates in both post-program rounds, comparable to the experimental estimates, when we used as a comparison group children from non-eligible households in the control localities. In conclusion, it would be fair to say that the RDD approach, using only information on the outcome variables of interest, the household-specific discriminant score and the region-specific threshold values, is a valuable approach to evaluating program impacts as it has shown to generate estimates that are remarkably close to conventional experimental methods that require much richer data.

One additional key finding from our analysis is that the quality of the control group is very important. In combination, the variety of tests we conduct suggests that the reliability of the estimated program impact depends more on the integrity of the comparison group used and less on whether an experimental or quasi-experimental estimator is used to measure impact. In the case of PROGRESA, a quasi-experimental method such as the RDD would have yielded program impacts that were comparable to the "ideal" experimental impact estimates as long as the comparison group of households did not come from localities where the program was in operation. This finding has potentially critical implications since it implies that the evaluation of social programs at the national scale covering large segments of the poor may be extremely 28
1984, a law was passed expropriating the former owners‟ land (with compensation) and entitling current occupants. Some original owners accepted the governmental decision and compensation, while others challenged the decision in drawn-out lawsuits (some of which are still pending). Galiani and Schargrodsky exploit the variation in the owners‟ decision to accept or challenge the law to identify the effects of land titling on occupants‟ behavior. Although it is conceivable that owners with more favorable land quality would have been more likely to contest the appropriation, Galiani and Schargrodsky show that this was not the case and that the parcels of land for which owners contested or did not contest the appropriation were actually highly comparable and basically next to each other. For this reason, their analysis considers the land titling ”treatment” exogenous from the point of view of the squatters.

Galiani and Schargrodsky find substantial effects of the land titling program on household behaviors using data from two surveys, performed in 2003 and 2007, and focusing on 245 56
|---|---|---|---|---|---|
||Average frequency of<br>|36 LMICs|2.4|175|1|
||reporting|5 LMICs in SSA|4.5|175|1|
||Average longest time gap|36 LMICs|54.9|175|1|
|**Numerator Issues**<br>|between two data points|5 LMICs in SSA|90.4|175|42|
|(misreporting of the<br>number of people|Largest jump in<br>administratie data|36 LMICs|0.20|37.3|0|
|<br>currently vaccinated)|v<br>(percentage points)|5 LMICs in SSA|0.20|11.10|0|
||Largest jump in<br>administrative data|36 LMICs|0.11|4.19|0|
||(percentage points per day<br>since last report)|5 LMICs in SSA|0.04|1.33|0|
||Average time lag for admin<br>|22 LMICs|97.9|442|1|
||data to catch up with survey<br>estimate|4 LMICs in SSA|182.6|308|35|
||Average difference in|19 LMICs with<br>||||
|**Denominator Issues**<br>(under- or overestimation<br>of target population size)|reported vaccination rate for<br>identical number of people<br>covered:<br>WHO vs. Our World in|exact match in<br>date and number<br>of people<br>vaccinated|1.6|-2.9|5.1|
||Data|5 LMICs in SSA|
.05**|0.02|0.02|0.02|
|Family care indicators<br>|0.03*|0.01|0.02|0.01|0.01|0.01|0.03*|0.02|0.05***|0.01|0.04**|0.01|
|score(out of 17)<br>Notes. Receptive English a<br>Picture Vocabulary Scale (<br>adapted MDAT are the_z-_s<br>were age adjusted. Baseli|nd Luo voc<br>BPVS). Expr<br>cores from<br>ne and follo|abular<br>essive<br>the Ke<br>w-up|y scores ar<br>vocabular<br>nya adapt<br>were cond|e_z-_sco<br>y_z-_sco<br>ation o<br>ucted a|res, measur<br>res were me<br>f the Malaw<br>pproximatel|ed using<br>asured<br>i Develo<br>y 5 wee|separate<br>using a too<br>pmental A<br>ks apart. *|assess<br>l deve<br>ssessm<br>_p_< 0.1|ments bas<br>loped from<br>ent Tool (<br>, **_p_< 0.0|ed on t<br>the P<br>MDAT<br>5, ***|he British<br>PVT. The<br>). All_z-_sco<br> _p_< 0.01.|<br>res|
this higher disaggregation comes at the cost of additional noise.<sup>10</sup> Additionally, disaggregated GDP-growth projections are associated with higher degree of noise and fluctuation. Section 4 compares our specification to alternate models with only three sectors.

- 4 compares our specification to alternate models with only three sectors.

- 3. Non-labor income has two components. The first is the income from BISP transfers ) . The value of the cash transfers made under BISP’s flagship UCT program has gradually BBCCBBCC

- (yy

- increased over time in nominal terms. We use the yearly change in the nominal value of payouts (as announced by BISP) to simulate the evolution of households’ income from public transfers, using the reported values in HIES 2018/19 as a base.<sup>11</sup> The second component includes other non-labor incomes such as pensions and private transfers ) . For simplicity, we model its evolution by using the headline CPI. While the model is highly flexible, and NNLL

- (yy

- additional components of non-labor income can be independently modeled to simulate their net impact on poverty under different scenarios (see component (b) in equation (1)), data availability limits the level of heterogeneity that we can model.

4. To capture the impact of differential price trends across 12 COICOP components, we estimate a household-specific inflation rate (Meyer, Kishwar, & Nasir, 2024). As shown in equation (1) and (2) 1h denotes the household-specific inflation rate between the baseline period and the year we project to . As noted in the previous section, differential price trends PP tt= 0 affect households' effective inflation rates depending on the composition of their tt= 1 consumption basket. 5. Finally, we use the propensity to consume of each household 0 at baseline to transform � 0 ~~�~~ cc the predicted household income into household expenditure at , which is the measure yy to estimate the incidence of poverty using the official and
(2013) have 421 individuals for their study of China’s one child policy’s effects; Chuang and Schechter have data on between 49 and 140 households for their analyses of how shocks affect time and risk preferences, and their survey of 19 studies of risk preference stability in the literature shows a median sample size of 84; Hao et al. (2014) have 144 individuals for their study on migration and risk preferences in China; and di Tella et al. (2007) have 312 households for their study of how property rights affect pro‐market beliefs.

Third, we can ask whether large impacts would be consistent with the results we obtain. Considering first the estimates one by one, the standard errors provided in each table allow one to determine the confidence intervals for the different impacts. These confidence intervals typically do not contain the large effect sizes of many of the studies in the literature, but we cannot rule out moderate effects. For example, from Table 3, a 95% confidence interval for the impact on the 11‐point risk‐seeking scale is (‐0.27, +0.30), and for the likelihood of choosing a risky job is (‐0.14, +0.14). Any impact on risk attitudes is therefore relatively modest, and less than the 20 percentage point change seen in Cameron et al. (2013), or the 20 percentage point increase in the likelihood of choosing a risky activity for Hurricane Katrina evacuees (Eckel et al, 2009). For the impact on pro‐market beliefs, the 0.53 estimate of di Tella et al. (2007) lies right at the boundary of a 90 percent confidence interval for our LATE impact (‐0.18, 0.53). A 95 percent confidence interval for the impact on household efficiency in game A is (‐0.03, +0.09), which is less than the 16 percentage point change in the amount sent
26

**Table 6. Simulation Results – Effect of Treaty-Shopping**

|Column<br>(1)|(2)|(3)|
|---|---|---|
|Subsample<br>All countries|DTT with investment hub|No DTT with<br>investment hub|
|Diversion (in%)<br>36.00|92.71|30.20|
|FDI Gain (in%)<br>0.96|2.90|0.76|
|CIT Revenue Loss (in%)<br>5.13|14.48|4.17|
|Notes: Based on the conceptual framework and estim<br>results in percent, differentiating between three countr|ation results of Table 5, this Tab<br>y groups.|le presents simulation|

# **VI.   CONCLUSION**

We analyze the cost and benefits of concluding Double Tax Treaties (DTTs) with investment hubs on FDI and government revenues for a sample of African economies. Our results imply that these economies are likely facing a substantial reduction in revenue when signing a treaty with investment hubs. At the same time, we find no evidence substantiating expectations of a corresponding benefit in terms of additional investment generation. The value of tax treaties to attract investors thus remains uncertain at best, especially where investments tend to be aimed at a country’s natural resources or specific market.

Further analysis is needed to assess the large magnitudes of treaty related base erosion and profit shifting in Sub-Saharan Africa identified in our analysis. While micro-level firm level information on MNEs remains very limited in commercial databases for many developing countries, including most economies in Sub-Saharan Africa (Platform for Collaboration on Tax 2017), future research drawing on micro level administrative information, including Country-by-Country reports of large MNEs, could potentially help fill this gap To assess the robustness of our findings and evaluate their potential underlying drivers, we examine the role
effects are therefore estimated with post-program single differences between treated and control children.<sup>21</sup> Matching methods are used in the comparison of outcomes to adjust for potential biases due to nonrandom targeting and selection into the program. The standard underlying assumption for this approach is that matching on the propensity score (i.e. the estimated probability of participation in the program) eliminates any bias generated by pre-treatment differences between the two groups as long as there are no differences in unobservable variables that jointly influence program participation and the outcomes under analysis. The availability in the baseline survey of relevant pretreatment information to model program targeting and participation, in principle, makes the application of the matching methodology suitable for the evaluation of FA.

Although careful procedures were followed in the early evaluation to select comparable control areas, a comparison of baseline characteristics between treated and control children in our sample using standardized t-tests and normalized differences, presented in Table 1, reveal a number of differences that are statistical significant. In order to balance the distribution of covariates between the two groups and assess the sensitivity of the results, we matched children on the basis of three different model specifications to predict the probability of treatment. Table 2 presents the group of variables included in the three models used to predict participation into the program. The first model (Model 1) includes standard individual and household pre-treatment socioeconomic and demographic characteristics such as age and order of the child, dummy variables for married and participating in the labor force head of the household, age, education and gender of household head, urban location, the number of children in the household ages 7 to 11 and 12 to 18, and monthly expenditures. The second model (Model 2) extends Model 1 by adding a number of municipality-level covariates that proxy for measures of educational supply and demand at baseline (for instance, pupil/teacher ratio and access to schools). In addition, the third model (Model 3) includes all but the health variables used by Attanasio et al
a large amount of data, ranging from surveys of beneficiaries and households to bespoke monitoring and information systems that track resources and aid. An open question is whether projects that also have a research arm—perhaps asking the simple question of whether project resources reached the intended beneficiaries—should be subject to further IRB clearance when the project itself is not.

Third, it has also been pointed out that the presence of an internal review board would hardly suffice when a researcher conducts a study in one of its 188 member countries and the internal board’s view is not in accord with local boards. Therefore, although having an internal board might facilitate reviews by host country panels, this would not supersede the need for local clearance. On the other hand, not every country has a board that is functional and diligent. Thus, local review may also not be sufficient. Whether one should privilege the views of the local board of the World Bank’s internal board in these cases remains an open question. One option is that the union of the recommendations of both a local board (if any) and that of an external board might be prudent.

By highlighting these three issues we do not, by any means, wish to argue for World Bank exceptionalism when it comes to ensuring that research is carried out in an ethical manner consistent with the goals of the institution. Quite the opposite. <mark>The WB should be equally ethical in its research, but given the vast experience it has accumulated, it should actually be a leader in addressing the issues that arise in the contexts it works in . W</mark> e feel that the World Bank is actually in a unique position to lead in the creation of an institutional review board that can move beyond the Eurocentric bio-medical model that is currently in use. The process of creating such a review board could bring up many questions that researchers working in low-income countries 28
<br>0.040|<br>0.035|<br>0.017|
|Yearly probability of falling to<br>below poverty line|0.238|<br>0.154|<br>0.137|<br>0.015|
|Average vulnerability for three<br>annual shocks|0.577|<br>0.395|<br>0.358|<br>0.043|
|Average vulnerability three years<br>ahead|0.406|<br>0.367|<br>0.358|<br>0.233|
|Vulnerability poverty line<br>[VPL(0.5,3,PL)]|10.967|<br>10.815|<br>10.629|<br>10.477|
|Headcount vulnerable rate|58.91%|<br>47.49%|<br>42.10%|<br>30.18%/0|
|Ratio of vulnerable to poor|2.95|2.37|<br>2.11-|<br>1.51|

The results with the usual standard deviations, which would ignore measurement error, show very high levels of vulnerability, 59 percent in MiniSusenas and 42 percent in 1 00 Village Survey. In MiniPSusenas, the estimated yearly standard deviation of changes in log expenditures is 0.617, while the mean of 9 All empirical vulnerability calculations using both Mini-Susenas and 100 Village Survey data sets, with the results presented in tables 2 and 5, are based on May 1997 prices.

14
line. BPL households are APL energy and fixed charges if their consumption exceeds 50 kWh of consumption during a billing cycle.

The IBT schedule seeks to provide a minimum amount of electricity at an affordable cost to low-income/low-consumption households. IBTs also enable a utility to achieve its revenue goals, by deriving a larger share of its revenue from high-income and high-consumption households. From the perspective of balancing affordability and revenue sufficiency goals, designing an efficient IBT schedule amounts to separating households into distinct groups based on their willingness and ability to pay for electricity and using observable household characteristics to optimally setting consumption blocks at an appropriate marginal price.

IBTs are not unique to India and the estimation of residential electricity demand under IBT schedules has been studied extensively in developed country settings. Reiss and White (2001) use a representative sample of California households, and summarize how the structure of electricity demand varies across customers.<sup>6</sup> The model is then used to analyze the effect of tariff changes on changes in consumption and the share total monthly expenditure a household spends on electricity.

McRae (2015) is one of the few papers that conducts a similar exercise in a developing country setting,<sup>7</sup> by building an asset ownership model using Colombian census data and pairing it with a utility’s administrative billing data, to show that government subsidies for electricity programs disincentivizes greater investments in electricity infrastructure and ensnares poorer households in a low-level subsidy trap. McRae (2015) uses the demand estimation under non-linear pricing econometric modeling framework developed by Hanemann (1984)<sup>8</sup> to recover the parameters of household-level preference functions.

More recently, Wolak (2016) applies an enriched version of this modeling framework to water utility customers in California and uses it to find price schedules that ‘’optimally’’ balance the revenue and conservation goals of water utilities.<sup>9</sup> Households choose their consumption level to maximize a utility function which depends on their demographic
already account for inflation changes.

**Table 1 SEDLAC Country Data Used in the Simulations**

|**Country**|**Baseline**<br>**Survey**|**Simulated**<br>**Years**|**Informality Definition**|
|---|---|---|---|
|Argentina|2016|2017 – 2020|- Salaried workers who do not receive work-related<br>pension insurance.<br>- Non-salaried workers without complete tertiary<br>education.|
|Bolivia|2016|2017 – 2020|Workers who do not receive work-related pension<br>insurance.|
|Brazil|2016|2017 – 2020|- Salaried workers without the work-registry book<br>("carteira").<br>- Non-salaried workers who do not contribute to the social<br>security system.|

> 19 Available at <u>https://www.worldbank.org/en/publication/macro-poverty-outlook.</u>

> 20 SEDLAC is a database of harmonized socio-economic statistics constructed from Latin American and Caribbean (LAC) household surveys. The SEDLAC database and project were jointly developed and are jointly maintained by CEDLAS (Universidad Nacional de La Plata) and The World Bank’s LAC Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice. SEDLAC includes information from over 300 household surveys carried out primarily in 18 LAC countries for which a comparable income aggregate (for welfare analysis) can be created: Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay.

> 21 Specifically, the growth rates between the baseline and simulated year are calculated using actual data for participation, informality, employment levels, and earnings by labor sector, inputs which otherwise would be projected
foreign investment based on income tax incentives. In particular, forward linkages are significantly stronger when foreign investors received tax incentives in the form of exemptions on value-added taxes.

# **F.  Robustness Tests**

Since our dependent variable is firm-level productivity and the focus of the analysis is on how sector-level foreign investment affects domestic firm productivity, endogeneity is less likely to be an issue. It is difficult to make a case that firm-level productivity affects sector-level foreign investment, particularly upstream and downstream foreign investment. To the extent that foreign ownership could be attracted to sectors where suppliers or users are more productive, this is accounted for by the use of firm-level fixed effects. However, some critics might argue that foreign investors are drawn to sectors where they expect higher productivity growth in the future. To address this unlikely but nevertheless potential source of endogeneity, we apply instrumental variables (IV) techniques. We use future tariffs (tariffs at time t+1) as instruments. For instance, _lnTariff_ (at time t+1) is used to instrument _Horizontal_ ; _lnTariff_backward_ (at time t+1) is used to instrument _Backward_ ; and _lnTariff_forward_ (at time t+1) is used to instrument _Forward_ . Since our tariff data is from 1998-2007, we lose one year of observations when we apply the future tariffs as instruments. All identification tests show that the equations are exactly identified.

The results are reported in Table A-4. The point estimates are magnified for backward linkages, confirming the importance of the linkages between domestic suppliers and foreign-owned buyers of their inputs. However, the coefficients for non-SOE domestic enterprises on both forward and horizontal linkages become negative and statistically significant. The negative and significant coefficient on the horizontal variable confirms previous work by Aitken and Harrison (1999) and others suggesting that foreign firms in the same sector act as competitors for domestic enterprises. The switch in sign for the coefficient on horizontal FDI calls 26
monetary policy, with a direct effect of changes in nominal interest rates, and an indirect effect through relative prices induced by the effect of changes in the nominal interest rate on the exchange rate.

The targets are considered to be met whenever the observed accumulated inflation during the period January-December of each year (measured on the basis of the variations in the IPCA index) falls within the band. In case the target is missed, the central Bank Governor must issue an open letter to the Finance Minister explaining the causes of the breach, the measures work is only a recent phenomenon; see Schaechter, Stone and Zelmer (1999, p. 12).

4 5In countries like Korea, for instance, the central bank is now required by law to set a price stability target every year in consultation with the government.

45