rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3.3: Protection offset share for coastal flooding for each coastal province and return period**

||||**Returnpe**|**riod inyears**|||
|---|---|---|---|---|---|---|
|**Province**|**5**|**25**|**50**|**100**|**250**|**500**|
|Quang Ninh|10|80|100|100|100|100|
|Hai Phong City|0|30|100|100|100|100|
|Thai Binh|0|10|100|100|100|100|
|Nam Dinh|0|30|100|100|100|100|
|Ninh Binh|10|50|100|100|100|100|
|Thanh Hoa|10|50|100|100|100|100|
|Nghe An|20|100|100|100|100|100|
|Ha Tinh|10|80|100|100|100|100|
|Quang Binh|10|80|100|100|100|100|
|Quang Tri|5|50|100|100|100|100|
|Thua Thien - Hue|20|100|100|100|100|100|
|Da Nang City|20|100|100|100|100|100|
|Quang Nam|20|100|100|100|100|100|
|Quang Ngai|20|100|100|100|100|100|
|Binh Dinh|20|80|100|100|100|100|
|Phu Yen|20|80|100|100|100|100|
|Khanh Hoa|20|80|100|100|100|100|
|Ninh Thuan|5|50|100|100|100|100|
|Binh Thuan|20|100|100|100|100|100|
|Ba Ria-Vung Tau|10|80|100|100|100|100|
|Ho Chi Minh City|5|50|100|100|100|100|
|Tien Giang|0|30|50|100|100|100|
|Ben Tre|20|100|100|100|100|100|
|Tra Vinh|10|80|100|100|100|100|
|Soc Trang|5|50|100|100|100|100|
|Bac Lieu|5|50|100|100|100|100|
|Ca Mau|5|50|100|100|100|100|
|Kien Giang<br>Source: Van Ledden et al. 2020.<br>Note: Each number represents the sha|5<br>re of modelled|50<br>coastal flood e|100<br>xtent that is ac|100<br>tually expected|100<br>to occur in a g|100<br>iven province|

Source: Van Ledden et al. 2020. Note: Each number represents the share of modelled coastal flood extent that is actually expected to occur in a given province for a specific return period taking into account the current state of the existing flood protection infrastructure. These offsets are designed to be used specifically with the flood maps described in section 2.1 and should not be used in combination with other flood maps or for other applications.

18
Equation 2.<sup>16</sup> A distributed lag (DL) specication is standard in the MPC literature for one-o transfers, such as in JPS and Parker et al. (2013). Although the direct projections approach is more popular in the multiplier literature, regressions on simulated data reveal that it has diculty isolating the lagged eects of one-o transfers over short horizons, whereas the DL specication uncovers the correct multipliers exactly (see Appendix 3.5). Other specications are reported in Appendix 3.3 and Appendix 3.4, including a specication in detrended levels, which produce broadly similar results.

# 3. Empirical Results

3.1. Graphical Evidence. Figure 1 provides a visual representation of the size and direction of the relationship between changes in transfers on the x- axis (permanent and temporary), and contemporaneous growth in per capita labor income and GDP (y-axis). The slope of relationship is a simple estimate of the size of the cross-region transfer multiplier. For the temporary transfers (Figure 1, Panels BD), each of the 50 dots represents the growth rate and transfer size in a dierent state in the quarter the transfer was paid. For permanent transfers (Figure 1, Panel A) there are too many Social Security increases to be plotted in this way. So instead, I group permanent transfer change residuals into 50 bins based on their size (across dierent quarters and states) and plot the average transfer size in each bin (x-axis) against the average growth rate residuals of per-capita labor income for that bin (y-axis). Residuals are calculated after removing time and state FEs.<sup>17</sup> .

In all four subplots in Figure 1 there is a positive and signicant relationship (line slope), indicating that states that received larger transfers tended to grow faster contemporaneously. Also note that the relationship between transfers and contemporaneous growth has a steeper slope for permanent than temporary transfers. For permanent transfers (Panel A), the slope is about 1.9, 16 Specically, _β_ 0∆ _tri,t/Yi,t−_ 1 in Equation 2 is replaced by<sup>�</sup><sup>_h_</sup> _j_ =0<sup>_γj_∆</sup><sup>_tri,t−j/Yi,t−j−_1,with</sup> the cumulative multiplier being the sum of the lags for a one-o transfer _Ch_ =<sup>�</sup><sup>_h_</sup> _j_ =0<sup>_γj_.</sup> 17Quarters without any Social Security increases are dropped. Figure 1, Panel A excludes bins where the normalized absolute transfer change is tiny (less than 0.015% of labor income). This does not aect the slope or t-statistic, but makes the relationship more visible. The graph with all the bins is shown in Appendix Figure 3.5.

14
- Dell (2010). The Persistent Effects of Peru’s Mining Mita. _Econometrica_ , _78_ (6), 1863–1903. 5

- Dell, M., Lane, N., & Querubin, P. (2018). The Historical State, Local Collective Action, and Economic Development in Vietnam. _Econometrica_ , _86_ (6), 2083–2121. 5

- Dell, M., & Olken, B. (2020). The Development Effects of the Extractive Colonial Economy: The Dutch Cultivation System in Java. _Review of Economic Studies_ , _87_ (1), 164–203. 5

- Devarajan, S., Khemani, S., & Shah, S. (2009). The Politics of Partial Decentralization. In _Does decentralization enhance service delivery and poverty reduction?_ . Edward Elgar Publishing. 5, 6

- Dincecco, M., & Katz, G. (2016). State Capacity and Long-run Economic Performance. _The Economic Journal_ , _126_ (590), 189–218. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecoj.12161. 1

- Erman, A. E., Solis Uehara, C. C., & Beaudet, C. (2021). Leveling up : Impacts of performancebased grants on municipal revenue collection in Mozambique. _Policy Research working paper, World Bank_ , (9789). 1

- Faguet, J.-P. (2003). Does Decentralization Increase Government Responsiveness to Local Needs? Evidence from Bolivia. SSRN Scholarly Paper ID 373600, Social Science Research Network, Rochester, NY. 5

- Faguet, J.-P. (2014). Decentralization and Governance. _World Development_ , _53_ , 2–13. 5

- Fergusson, L., Molina, C. A., & Robinson, J. (2020). The Weak State Trap. SSRN Scholarly Paper ID 3651454, Social Science Research Network, Rochester, NY. 1, 4

- Ferraz, C., Finan, F., & Martinez-Bravo, M. (2020). Political Power, Elite Control, and LongRun Development: Evidence from Brazil. Tech. Rep. w27456, National Bureau of Economic Research, Cambridge, MA. 6

- Fukuyama, F. (2004). The Imperative of State-Building. _Journal of Democracy_ , _15_ , 17–31. 6, 28

- Gennaioli, N., & Rainer, I. (2007). The Modern Impact of Precolonial Centralization in Africa. _Journal of Economic Growth_ , _12_ (3), 185–234. Publisher: Springer. 1, 5

- Gibson, J., Olivia, S., Boe-Gibson, G., & Li, C. (2021). Which night lights data should we use in economics, and where? _Journal of Development Economics_ , _149_ , 102602. 19

- Government of the United Republic of Tanzania (2013). A Study on Local Government Authorities’ Own Source Revenues. 12

- Hanson, J. K., & Sigman, R. (2019). State Capacity and World Bank Project Success. _unpublished working paper_ . 1

31
education provision (and there is limited mobility) this in turn should aect the likelihood of a private school existing in a village.

# 2.2 Data

We employ three data sources: (a) a complete census of private schools carried out by the Federal Bureau of Statistics in 2000; (b) administrative data on the location and date of construction of public schools in the Punjab province available from the province's Educational Management and Information Systems (EMIS 2001) augmented with the National Educational Census (NEC 2005); and (c) data on village-level demographics and educational proles from the 1998 and the 1981 population censuses of Punjab, which provide both baseline and contemporaneous information on village-level characteristics.

We restrict our analysis to rural areas in the province of Punjab, the largest province in the country which hosts 60 percent of the population, two-thirds of whom live in rural areas.<sup>7</sup> Since the EMIS and the other datasets do not employ a common village coding scheme, we had to match villages in the dierent databases on the basis of their names. Using a combination of a phonetic algorithm and manual post-match, we were able to match over 90 percent of the villages across databases (23,064 of the 25,266 unique Punjabi villages in the 1981 census).<sup>8</sup> In our nal estimation sample, we restrict attention to villages that did not receive a girls' or boys' secondary school prior to 1981 and did not have such secondary schools in their neighboring villages. This reduces our sample to 9,333 villages, but aords two

> 7Not all data sets (e.g., EMIS, 1981 Census) were readily accessible for other provinces, and urban areas could not be matched at the granular level necessary to exploit the cross-sectional variation in private school location and GSS presence that we utilize in the paper.

> 8We also augment the public schooling data from the EMIS with more recent data from the 2005 National Educational Census. We are able to match some more villages using the NEC, but cannot use this as the primary data source because of insucient information on the upgradation of schools from primary to secondary. Specically, there is a chance in the NEC data that we incorrectly assign a village to have received a GSS by 2000 when it only had a primary school that was upgraded to a secondary school after 2000. Since our empirical strategy examines the relationship between pre-existing girls secondary schools and private schools (as of 2000, the date of the private school census), the correct thing would be to classify such a village as not having a GSS.

9
||||w4 and w5, UGA<br>wave 4 and above|
|---|---|---|---|
|_twi_|Potential wetness<br>index||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_nutrient_availability_<br>_nutrient_retention_<br>_rooting_conditions_<br>_oxygen_availability_<br>_excess_salts_<br>_toxicity_<br>_workability_|Nutrient<br>Availability<br>Nutrient Retention<br>Rooting<br>conditions<br>Oxygen<br>availability<br>Excess salts<br>Toxicity<br>Workability||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_soil_fertility_index_|<br>Soil fertility index|PCA of soil variables above||
|_plot_slope_|Plot Slope<br>(percent)||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_plot_dist_household_|Plot Distance in<br>(KMs) to HH||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above<br>Only present in<br>ETH, MWI.|

Supplementary Table 4. Directory of variables in the household-level dataset

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_hh_size_|Household<br>size|||
|_hh_shock_|Was the<br>household<br>negatively<br>impacted by a<br>shock over the<br>past 12<br>months?||MALI: 3 year recall period.<br>TZA: 2 year recall period in w4, w5.|

36
of interest are continuous, such as prices, incomes, and rainfall, and so allowing more bins with narrower intervals can provide more precision in estimating subjective means. These visual aid methods typically provide a histogram of the subjective distribution. As with the percent chance methods, further assumptions are needed to calculate many of the moments of interest. A common approach is to use the midpoints of intervals to discretize the continuous variable, and then use the elicited probabilities along with these midpoints to calculate moments of interest.<sup>5</sup> The support of the distribution over which the respondent should answer can either be predetermined or elicited directly from the respondent. Because eliciting the support usually involves some real time calculations, enumerators using paper and pencil methods are less likely to make mistakes with a pre-determined support. In addition, if the whole distribution needs to be compared across respondents, then a pre-determined (common) support is required. However, if the likelihood of the event is very different across respondents, then a pre-determined support may jeopardize accuracy if the intervals are too coarse. In the end, pilot testing will be necessary to refine the support so that no probability mass is allocated to the extremes of the support and that the grid of the support is fine enough so that the probability mass is distributed across several grid points.

If the event is discrete, then the support can easily be pre-determined. For example, Giné, Townsend and Vickery (2008) elicit the probability that the monsoon will start in a given 15-day period and so the support they use contains all 15-day periods spanning two months prior to the normal onset and three months afterwards. No respondent gave mass to the first or last period in the support, again suggesting that they were not constrained by it.

An example of a pre-determined support with a continuous outcome comes from the panel data on Sri Lankan microenterprises, collected by de Mel et al. (2008). Microenterprise owners were first asked how much they expected profits to be in the following two months, and based on their answer they were then given a grid with between 17 and 29 profit range bins. The bins were wider at the end points and narrower in the middle to cover a wide range of answers

> 5 For example, Hill (2007, p.40) writes “For each farmer the beans were split into seven 100 shilling intervals from 100 to 800. A common lower and upper limit was placed on the data. The class mark for each of these 100 shilling 7

> classes was taken as the midpoint of the class. The mean was calculated as ∑ _f i_<sup>_x_</sup> _i_ where is _xi_ the given class mark _i_ = 1 for class _i_ and _fi_ is the probability the price would fall into this class.

- 9 -
first group as “lottery winners” and the second group as “lottery losers”. Lottery losers were taken off the roster of households that could be activated for BDH transfers. As is shown below, however, a substantial fraction of these households nonetheless received BDH transfers, an issue we address in our estimates of program impact.

Because of the criteria for selection into the BDH evaluation, households in the study sample are poorer than other households in Ecuador. Table 1 reports the means and standard deviations for selected characteristics of households in the study sample at baseline, for all households in the country, and for all households in the parishes included in this study. The samples for these calculations are limited to households with children ages 6-17. National averages are based on the 1998/99 _Encuesta de Condiciones de Vida_ (ECV), a multi-purpose household survey, and averages for the parishes in the study sample are based on the 2001 Population Census. Table 1 shows that households in the sample have more members and fewer rooms than other households, are less likely to have access to piped water or a toilet, and are more likely to have a dirt floor in their home. Mean years of schooling of household heads in the study sample are more than two-and-a-half years lower than those of other households. These patterns are apparent both in comparison with other households in these same parishes, as well as in comparison with national averages. There are no clear differences in school attainment among children—perhaps, because of greater coverage of schooling among younger cohorts.

# _C. Data_

The main sources of data used in this paper are the baseline and follow-up surveys designed for the BDH evaluation. Both surveys were carried out by an independent firm that had no association with the BDH program, the _Pontificia Universidad Católica del Ecuador._ The baseline survey was collected between June and August 2003, and the follow-up survey was collected between January and March 2005.

The survey instrument included a roster of household members and, _inter alia_ , information on the level of schooling attained, marital status, and languages spoken by all adults; school enrollment, grade progression, and work, both paid and unpaid, of all children ages 6-17; an extensive module on household expenditures, which closely followed the structure of the 1998/99 Ecuador ECV; and a module on dwelling conditions, ownership of durable goods, and access to public services. We aggregated expenditures into a consumption aggregate, 6
Table 38

|Dependent variable: Fisca|l adjustme|nt when nee|ded based|on Escolan|o et al.(20|14)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|-0.279**|-0.264*|-0.300*|-0.324*|-0.172|1.029|-1.088*|-0.707|
|structural as % GDP|(0.139)|(0.154)|(0.161)|(0.177)|(0.282)|(0.692)|(0.568)|(0.497)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.957||-0.168|1.374|3.592|1.757|0.078|
|||(8.219)||(8.383)|(9.042)|(9.841)|(9.446)|(8.447)|
|Inflation (t-1)||19.399*||25.158**|24.763*|29.131**|20.231|25.211**|
|||(11.390)||(12.745)|(12.844)|(14.276)|(13.535)|(12.840)|
|U.S. Interest rate||-0.940***||-1.090***|-1.125***|-1.220***|-1.327***|-1.183***|
|||(0.351)||(0.423)|(0.433)|(0.450)|(0.478)|(0.449)|
|**Political factors**|||||||||
|Gini|||0.021|0.010|0.028|0.412|0.010|-0.007|
||||(0.125)|(0.250)|(0.294)|(0.338)|(0.325)|(0.219)|
|Rule of law|||-1.132|1.229|1.216|1.359|-0.528|1.509|
||||(0.741)|(1.109)|(1.115)|(1.171)|(1.660)|(1.184)|
|Margin of Majority|||3.731|3.076|3.489|6.204|5.454|-3.350|
||||(3.460)|(3.909)|(4.070)|(4.703)|(4.608)|(8.392)|
|Election's year|||-0.675|-0.414|-0.336|-0.282|-0.253|-0.391|
||||(0.526)|(0.562)|(0.572)|(0.591)|(0.578)|(0.569)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.240<br>(0.376)||||
|Gini * Deviation||||||-0.032*<br>(0.018)|||
|Rule of law * Deviation|||||||0.176<br>(0.112)||
|Majority * Deviation||||||||0.606<br>(0.723)|
|Observations|106|105|98|97|97|97|97|97|
|Number of ifscode|20|20|18|18|18|18|18|18|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in parent<br>*** p<0.01, ** p<0.05, *|0.0598<br>heses<br>p<0.1|0.224|0.149|0.272|0.277|0.315|0.300|0.280|

- 67 -
a 3.4% increase in haven deposits corresponds to around 0.075% of GDP (i.e. 2.2%*3.4%) and the implied average leakage rate is approximately 7.5% (i.e. 0.075%/1%). A simple simulation that weighs countries by their historical share of aid disbursements and accounts for crosscountry differences in the ratio of haven deposits to GDP, implies a leakage rate of around 5% in aggregate disbursements. These modest leakage rates represent a lower bound in the sense that they only include aid diverted to foreign accounts and not money spent on real estate, luxury goods etcetera.

While the leakage estimates reported above are averages for those countries with annual aid from the World Bank above 2% of GDP, we show that leakage rates exhibit a strong gradient in aid-dependence, both within this sample and beyond. On the one hand, lowering the threshold to 1% of GDP (sample of 46 countries), we cannot reject the null hypothesis of no leakage. This suggests that the average leakage rate across all aid-receiving countries is much smaller than the estimates obtained from the main sample, which account for less than 10% of all World Bank aid. On the other hand, raising the threshold to 3% of GDP (sample of 7 countries), we find a higher leakage rate of around 15%. This pattern is consistent with existing findings that the countries attracting the most aid are not only among the least developed but also among the worst governed (Alesina and Weder, 2002) and that very high levels of aid might foster corruption and institutional erosion (Knack, 2000; Djankov et al., 2008).

While the comprehensive deposit dataset employed in the main analysis is restricted and subject to confidentiality requirements, we also study publicly available series recently released by the BIS. This allows us to study deposits in some individual havens: we find that bank accounts in Switzerland and Luxembourg contribute significantly to the correlation between aid disbursements and haven deposits whereas accounts in Belgium and Jersey do not. The public series also allow us to extend the sample period to more recent years where financial transparency has improved significantly. We find similar point estimates for the periods before and after 2009 suggesting that the relationship between aid and hidden wealth is unchanged. However, since our leakage estimates for short subperiods are imprecise, we cannot rule out that financial transparency has curbed diversion of aid.

The paper contributes to the understanding of aid effectiveness by empirically identifying and quantifying a mechanism that may render aid ineffective: elite capture. In doing so, we contribute to literatures on the distributional effects of aid (Bjørnskov, 2010); hidden wealth and its origins (Zucman, 2013); and capital flight (Johannesen and Pirttill ̈a, 2016). Our results and empirical approach are most closely related to the finding that petroleum rents are partly 4
Haddad, Lawrence, John Hoddinott, and Harold Alderman, eds. 1997. _Intrahousehold resource allocation in developing countries: models, methods, andpolicy._ Baltimore: Johns Hopkins University Press.

King, Elizabeth M. and Anne M. Hill, 1993. _Women s education in developing countries: barriers, benefits, and policies._ Baltimore: John Hopkins University Press.

Lavy, Victor. 1997. "School supply constraints and children's educational outcomes in rural Ghana," _Journal of Development Economics,_ 51: [291]-314.

Mingat, Alain, and Jee-Peng Tan, 1999. "Analyzing Problems in the Structure of Student Flow," mimeo. Institut de Recherche sur l'Economie de l'Education, Universite de Bourgogne and Human Development Network, The World Bank.

Montgomery, Mark, Kathleen Burke, Edmundo Paredes, and Salman Zaidi, 1997. "Measuring Living Standards with DHS Data: Any Reason to Worry?" mimeo, Research Division, The Population Council. New York, NY.

Nehru, Vikram, Eric Swanson and Ashutosh Dubey, 1993. "New Database on Human Capital Stock in Developing and Industrial Countries: Sources, Methodology, and Results," Journal of Development Economics, 46:379-401 Oxfam International, 1999. _Education Now._ Oxfam International: London.

Pitt, Mark, 1995. "Women's Schooling, the Selectivity of Fertility, and Child Mortality in SubSaharan Africa" LSMS working paper, 0253-4517; no. 119. The World Bank, Washington.

Rutstein, Shea, 1999. "Health Nutrition, and Population Fact Sheets." Macro International. Calverton, MD. Mimeo.

Schultz, T. Paul, 1993. "Investments in the Schooling and Health of Women and Men," _The Journal of Human Resources_ 28(4):689-734.

Stecklov, Guy, Antoine Bommier, and Ties Boerma, 1999. "Trends in Equity in Child Survival in Developing Countries: A Illustrative Example using Ugandan Data." MEASURE _Evaluation_ Project/Carolina Population Center, INED. Mimeo.

Wagstaff, Adam and N. Watanbe, 1999. "Inequalities in child malnutrition in the developing world," Mimeo. DECRG, The World Bank. Washington, DC.

World Bank, 1999. _World Development Indicators, 1999._ World Bank, Washington, DC.

41