rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2001 2002 2003<br>year of child's birth<br>no TSC by 2003 some TSC by 2003<br>TSC latrines per capita<br>.012 .78<br>.01<br>.76<br>.008<br>.74<br>.006<br>.004 .72<br>TSC latrines reported built, per capita<br>.002 fraction recognizing numbers at age six<br>.7<br><!-- End of picture text --> Figure 3: Difference-in-differences: Learning trends and TSC intensity Birth years are listed on the horizontal axis; tested years are six years later. The sample is split at the district level into those which received any reported TSC latrines within 2001, 2002, or 2003 and those which did not. Error bars report 95% confidence intervals. TSC latrines per capita reports the average number of latrines per capita built in districts where any latrines were built by 2003, in order to show the timing of TSC scale-up.

37
_Table 17: Results of Design-Based Simulation_

||Arusha,<br>|Kilimanj<br>|aro, and<br>|Tanga R<br>|egions<br>|<br>||
|---|---|---|---|---|---|---|---|
||Mean<br>Poverty|<br>ARB|Mean<br>MSE|Mean<br>CV|Corr|RMSE|Coverage<br>rate|
|Direct survey estimates||||||||
|H-T approximation|47.0|-0.031|135.4|29.9|0.673|0.212|72.1|
|EA-Clustered standard errors|47.0|-0.031|52.0|21.0|0.673|0.212|48.7|
|<br>Household-level EB model||||||||
|Benchmarked|45.7|0.028|29.9|14.0|0.828|0.112|68.0|
|Non-benchmarked|42.4|-0.046|26.0|14.0|0.803|0.083|66.6|
|Model diagnostics<br>||||||||
|Marginal R<sup>2</sup><br>||||0.324||||
|Conditional R<sup>2</sup>||||0.361||||
|Skewness of area effect||||0.13||||
|Kurtosis of area effect||||2.45||||
|Wilks-Shapiro P-value<br>of area effect||||0.59||||
|Skewness of hh error||||-0.19||||
|Kurtosis of hh error||||3.66||||
|Wilks-Shapiro P-value<br>of hh error||||0.002||||

Notes: Results reflect averages across 6000 estimates, representing 250 simulations of 24 districts. Fay-Herriot model not included due to small number of districts in sample. Diagnostic results reflect averages over 250 simulations

# VII. Conclusion

Policy makers worldwide are increasingly demanding more granular data to inform decisions. This paper examines the extent to which supplementing a synthetic sample drawn from the census with geospatial data at the subarea level in Sri Lanka and Tanzania generates more precise and accurate estimates of non-monetary poverty. The paper employs empirical best predictor models because they are more accessible to most statisticians in National Statistics Offices than hierarchal Bayesian models. In addition, empirical best models have a long history in small area estimation, are similar to other commonly used methods, and have been implemented in multiple publicly available software packages. The analysis examines non-monetary poverty in order to evaluate the estimates against census data, which sheds light on the performance of different methodological approaches and software packages.

The results are encouraging. The correlation between the small area estimates and the actual census is roughly 88 percent in both countries, and substantially greater than the analogous correlations for the direct survey estimates, which are 73 and 77 percent in Sri Lanka and Tanzania, respectively. The household-level model, compared with standard clustered survey estimates, reduces the mean squared error by about two-thirds in Sri Lanka and four-fifths in Tanzania, which is roughly equivalent to tripling 40
sri lanka country risk profile 10

|**Ris**<br>**precip**<br>|**k to ass**<br>**itation**<br>|**ets from**<br>**fooding**<br>|
|---|---|---|
|**District**|mLKR|mUS$|
|Colombo<br>|6,040|40.2|
|Rathnapura|1,230|8.19|
|Kalutara<br>|1,120|7.43|
|Gampaha|1,040|6.93|
|Puttalam|498|3.32|
|Kurunegala|363|2.42|
|Polonnaruwa|315|2.10|
|Hambantota|233|1.55|
|Trincomalee|189|1.26|
|Kegalle|151|1.00|
|Galle|101|0.67|
|Batticaloa|89|0.59|
|Mannar<br>|79|0.53|
|Anuradhapura|71|0.47|
|Kandy|64|0.43|
|Ampara|46|0.30|
|Badulla|44|0.29|
|Matale|26|0.17|
|Vavuniya|7|0.04|
|Moneragala|6|0.04|
|Mullaitivu|4|0.03|
|Nuwara Eliya|0|<0.01|
|<br>Jaffna|–|–|
|Kilinochchi|–|–|
|Matara|–|–|
|Total|11,700|78.02|

**Table 1:** Expected annual asset losses (tabulated in millions of LKR and thousands of US$) from exposure to precipitation floods in Sri Lanka. (US$1 = LKR 150). Asset loss exceedance curves were not available from the Fathom model for Jaffna, Kilinochchi, or Matara.
<!-- Start of picture text --> 800<br>» 600<br>5 e==eChina:<br>6 400<br>= exmelndia<br>F 200<br>ema World<br>0 Prirrri ri red Prrred rire ne wn<br><!-- End of picture text --> <!-- Start of picture text --> 3 2<br>&<br>3s 0.1 a -<br>.5 0 AY wav’ ne.L PEALal \KA | —=C.V.= 0.05<br>£co) -0.1 UWy v At \ Wi) —cv.-008<br>re<br>C.V.= 0.03<br>&> -0.2 | a<br>uv<br>9 -0.3<br>PPD DDD PP SF PS PS PS oF<br><!-- End of picture text -->
N. Regulatory substance- market entry index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Market Entry Regulation**|**50%**|**50%**|**100%**|**100%**|**77%**|
|**Permitting New Entrants**|50%|100%|100%|100%|90%|
|Responsible for monitoring compliance<br>with the terms of the license orpermit|||||88%|
|Authority to impose penalties for<br>violatinglicense orpermit terms|NAP||||100%|
|Penalties are formally written and<br>publiclyavailable|NAP||||80%|
|Provisions to force companies to<br>relinquish licenses or permits for<br>violation|||||100%|
|**PPA Approvals**|50%|0%|100%|NAP|57%|
|Legally required to approve all power<br>sales contracts either directlyor indirectly|||||59%|
|Approve or refuse a proposed PPA in a<br>legallyspecifiedperiod of time|NAP|NAP||NAP|60%|
|Authority over the process by which<br>utilities can select or procure power from<br>IPPs|||||50%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 69
# **Ill.** Testing for Asymmetric Effects: Methodology

The various channels through which output shocks can generate asymmetric effects have different testing implications, which in principle could be used to discriminate among them in a particular sample. For instance, an empirical test of the "credit crunch" view would imply looking at the share of collateralized credit, the amount of firms' borrowing on world capital markets (or, more specifically, the share of unhedged, short-term foreign currency liabilities incurred by firms), the behavior of asset prices (including exchange rates) during periods of economic downturns, and more generally changes in balance sheets. Examining data on the _composition_ of credit, namely, the share of lending going to small and medium-size firms, would also be important. The relevance of the "labor hoarding" hypothesis can be assessed by examining the importance of turnover costs, changes in the composition of the labor force and the behavior of productivity during recessions and upturns, and the behavior of physical investment immediately after crises. Indeed, as noted earlier, the hypothesis predicts a change in the composition of the factor mix (notably, an increase in capital and skilled labor intensity relative to unskilled labor) in the immediate aftermath of a recession or a large output contraction. Interestingly enough, Neri and Thomas (2000) found (using survey data for the past two decades) that in Brazil less-educated workers fell into poverty at a greater rate during recessions-a result consistent with the labor hoarding hypothesis.

The objective of this paper is, however, far less ambitious. As noted in the introduction, it focuses on two aspects of the debate: whether the initial cyclical position of the economy, and the magnitude of the initial drop in the level of output below trend in a downturn, matter in assessing the extent to which output shocks have an asymmetric effect on poverty. To this end, and given the focus of the present study on short-run dynamics, it proposes a standard vector autoregression (VAR) model involving a small set of stationary variables.

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16 is the number of children born in the five years prior to the data collection. In the 2000 RDHS, this corresponds to the period between May 1995 and June 2000. This time span will be compared to the period between May 1987 and June 1992, when data collection for the 1992 RDHS began. Similarly, in the pooled 1992-2005 data, the time span of interest is the ten years prior to the starting date of the 2005 RDHS, which is the period between May 1995 and February 2005. This corresponds to the period from September 1982 to June 1992 in the 1992 RDHS.

To explore the marriage market effects of conflict on fertility, the following equation is estimated: which additionally includes a time dummy _Year_ for the survey year and an interaction term between the proxy for conflict exposure _Conflicti_ and the time dummy. The estimated coefficient _β6_ captures the impact of _actual_ conflict exposure on fertility after the genocide relative to _women with similar risk of exposure to conflict before the genocide_ .

Given that we control for the effects of conflict in addition to the usual socio-economic determinants of fertility and given that conflict may also affect these other variables, the estimated coefficient of the conflict variable can be interpreted as the ‘pure conflict’ effect. This makes our calculations conservative estimates of the total effects of conflict on fertility. For example, conflict is likely to reduce the educational attainment of children exposed to violence during their school-age (Akresh and de Walque 2008) and of girls in particular (Shemyakina 2006), hence inter alia raising their fertility.

In both (1a) and (1b), measures of each woman’s socio-economic characteristics include three age categories (young: 15-24 years, middle: 25-34 years, and old: 35-49 years), the number of sons and daughters born before the time span of interest, her education (no education, some primary education, and some secondary or higher education), a dummy variable indicating whether she is currently in a union,<sup>9</sup> a dummy variable indicating whether she has had more than one union, a continuous household wealth index (see Section 4.1), a dummy variable indicating whether she has always lived in the same community, a dummy variable indicating whether the current location is urban, mortality rates of children under five years at the district

> 9 This variable is not included in the estimation of equation (1b), given that widowhood is strongly correlated with being in union.
**Table 4. Characteristics of Five Developing Country Users of Antidumping, 1995-2004**

|**AD-imposing**<br>**country**||**Exporting country**<br>**target**|**Share of import**<br>**market in 2000 (rank)**|**Antidumping investigations**<br>**(share of total)**|**Investiga**<br>**measures**<br>**country’**|**tions resulting in**<br>**(share of target**<br>**s investigations)**|
|---|---|---|---|---|---|---|
|**Argentina**|1.|China<br>|4.4% (4)<br>|38 (22%)|33|(87%)<br>|
||2.|Brazil|25.9% (1)|33 (19%)|22|(67%)|
||3.|EU|22.9% (2)|21 (12%)|10|(48%)|
||4.|South Africa|0.3% (23)|10 (6%)|6|(60%)|
||5.|Korea|2.1% (8)|10 (6%)|8|(80%)|
|||All other|44.3%|85 (48%)|58|(68%)|
|||Total|100.0%|176|124|(70%)|
|**Brazil**|1.|China|2.3% (8)|24 (19%)|18|(75%)|
||2.|EU|25.0% (1)|18 (14%)|13|(72%)|
||3.|USA|23.2% (2)|18 (14%)|7|(39%)|
||4.|India|0.5% (25)|8 (6%)|5|(63%)|
||5.|South Africa|0.4% (27)|5 (4%)|3|(60%)|
|||All other|48.7%|56 (43%)|16|(29%)|
|||Total|100.0%|129|62|(48%)|
|**India**|1.|China|3.0% (5)|66 (19%)|59|(89%)|
||2.|EU|20.8% (1)|49 (14%)|40|(82%)|
||3.|Taiwan|1.0% (16)|28 (8%)|22|(79%)|
||4.|Korea|1.8% (11)|25 (7%)|21|(84%)|
||5.|USA|6.0% (3)|19 (5%)|15|(79%)|
|||All other|<br>67.5%|<br>164 (47%)|135|(82%)|
|||Total|100.0%|351|292|(83%)|
|**Mexico**|1.|USA|73.3% (1)|21 (28%)|16|(76%)|
||2.|China|1.7% (6)|13 (17%)|12|(92%)|
||3.|EU|8.5% (2)|7 (9%)|2|(29%)|
||4.|Russia|0.0% (41)|6 (8%)|5|(83%)|
||5.|Ukraine|0.0% (35)|5 (7%)|5|(100%)|
|||All other|<br>16.6%|<br>23 (31%)|15|(65%)|
|||Total|100.0%|75|55|(73%)|
|**Turkey**|1.|China|2.4% (5)|44 (44%)|40|(91%)|
||2.|Taiwan|1.0% (14)|11 (11%)|10|(91%)|
||3.|Thailand|0.4% (25)|8 (8%)|6|(75%)|
||4.|Korea|2.2% (7)|6 (6%)|5|(83%)|
||5.|India|0.8% (18)|6 (6%)|5|(83%)|
|||All other|93.2%|25 (25%)|19|(76%)|
|||Total|100.0%|100|85|(85%)|

Note: Antidumping data compiled from Bown (2006b). Import data from COMTRADE. *For consistency, this table only allows for one “EU” entry for each product-specific investigation, hence total number of investigations and imposed measures may differ from table 2 due to aggregation of EU member cases per investigation.

30
program placement was random and impacts common across all units then one would not be concerned, although heterogeneous impacts cloud the picture, even in an experiment.<sup>9</sup> In nonexperimental evaluations, biases can be expected even under homogeneous impacts.

However, the literature on policy or program evaluation does not contain (to my knowledge) even a single example in which this type of baseline recall has been tested against conventional survey data collected at both the baseline and post-intervention.

This paper tries to help fill this gap in our knowledge. The paper reports on an experiment that was designed to test the idea of using retrospective data as a substitute for baseline data from a contemporaneous survey. After collecting baseline and post-intervention data for treatment and comparison units to allow estimation of a standard double-difference (DD), a series of recall questions were asked on how various dimensions of welfare had changed since the time the project was introduced. This allows what I will call the ―shoestring double difference‖ (SDD) estimator. More precisely, the two estimators of mean impacts are:

> Here  _Yi_  _Yi_ 1  _Yi_ 0 is the measured change in consumption between the baseline (date 0) and post-intervention surveys (date 1) for the respondent in household _i_ , where each respondent is assigned to either the treatment (the set _T_ ) or comparison group ( _C_ ), and<sup>_R_</sup> _i_<sup>denotes the</sup> subjective recall of the change in living standards over the same period.

Two versions of the SDD estimator are studied here: SDD1: This assumes that no baseline data are available. Only an ex-post survey can be done. Thus no adjustments are made for selection bias based on contemporaneously observed pre-intervention differences that might influence subsequent trajectories.

> 9 For example, suppose that the true impacts of a project are greater for poor people, for whom recall is less reliable. (There is supportive evidence for this conjecture in Das et al., 2011, who found health-status recall to be worse for poor people, using data for India.) Then we will obtain a biased estimate of the difference in impacts between poor and non-poor people even with randomized assignment of the intervention.

5
each grid cell for the period 2001–2022 using the Google Earth Engine code editor.<sup>5</sup> The measure of forest loss is expressed in hectares per square kilometer (ha/km<sup>2</sup> ).

3. _Land use type._ The MODIS Land Cover Type dataset offers comprehensive global land cover information with yearly intervals ranging from 2001 to 2020 (Friedl et al. 2010).<sup>6</sup> To classify land use, the MODIS Land Cover Type dataset employs various supervised classification methodologies. These methodologies include the use of the International Geosphere-Biosphere Programme (IGBP), University of Maryland Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT). The area in square kilometers and the proportion of different land cover types were computed within each grid cell from 2000 to 2020. The land cover types of interest were urban-built areas, croplands, water bodies, dense-open forests, and grasslands.<sup>7</sup>

# **III.B.2 Remote Sensing Outcomes**

We estimate the effects of the refugee camps on two primary outcomes: night light density and deforestation. To confirm the consistency of the results, we also assess the impact of refugee camps on type of land use, which includes dense-open forest, grassland (typically used for agriculture or pasture), and cropped land. Descriptive statistics for these variables are presented in Table 1. The table also illustrates the years for which each of the outcomes is available. The study employs all the years for which the outcomes were available according to the source.

Moreover, the spatial distribution of the two outcomes we study before and after the Rohingya’s arrival in 2017 is illustrated in Figures 5 to 6. These maps illustrate a dramatic improvement in night light density but mixed changes in deforestation. The maps for the geographical distribution of land use by type are also illustrated in Figures 7, 8, and 9. The maps of dense-open forest show a clear reduction around camps after the Rohingya’s arrival, but changes in area covered by grass and crops are less evident.

> 5In the case of missing values, they were imputed with 0. This process is reliable due to the high spatial resolution of the images, which is approximately 30.92 meters.

> 6It has a spatial resolution of 500 meters, meaning that each pixel in the dataset represents an area of 500 square meters on the Earth’s surface.

> 7We conducted this process using the Google Earth Engine code editor.

14
The somewhat weak relationship between resources and outcomes has been associated with deficiencies in the incentive structure of school and health systems. Indeed, most service delivery systems in developing countries present frontline providers with a set of incentives that negate the impact of pure resource-based policies. Therefore, while resources alone appear to have a somewhat limited impact on the quality of education (and health) in developing countries, coupling them with changes in incentives can have large and significant impacts on provider effort and the type and knowledge of staff at the frontline service units, which in turn positively affect outcomes. As noted by Duflo, Dupas, and Kremer (2009), the fact that budgets have not kept pace with enrollment, leading to large pupil-teacher ratios, over-stretched physical infrastructure, and insufficient number of textbooks, etc, is problematic, however increasing the level of resources might not help make up for the quality deficit in education and health without taking providers’ incentives into account. In other words, the literature suggests that increasing provider effort and knowledge is essential to improve outcomes.

In sum, the literature points to the importance of provider behavior and knowledge in the delivery of health and education. However, conditional on service providers exerting effort and being motivated, it is clear that the provision of physical resources and infrastructure – especially in health – also has important effects on the quality of service delivery.

We therefore propose three sets of indicators : The first attempts to measure availability of key infrastructure and inputs at the frontline service provider level. The second attempts to measure effort and knowledge to deliver quality services at the frontline level. The third attempts to proxy for effort, broadly defined, higher up in the service delivery chain. Providing countries with detailed and comparable data on these important dimensions of service delivery is one of the main innovations of the DSI.<sup>5</sup> In addition, we want indicators/measures that are (i) quantitative (to avoid problems of perception biases that limit both cross-country and over time comparisons)<sup>6</sup> , (ii) ordinal in nature (to allow within and cross-country comparisons); (iii) robust (in the sense that the methodology used to construct the indicators can be verified and replicated); (iv) actionable; and (v) cost effective.

> 5 The suggested DSI set in education and health is based partially on an initial list of 50 PETS and QSDS indicators devised part of the project “Harmonization of Public Expenditure Tracking Surveys (PETS) and Quantitative Service delivery Surveys (QSDS) at the World Bank. This initial list, which covers a wide range of factors characterizing public expenditure and service delivery in the social sectors, was streamlined using the DSI criteria and conceptual framework (see Gauthier, 2008).

> 6 See for instance Olken (2009).
sample size for the models in this paper – the number of respondents who gave an answer for each of the variables we use in the online-only voter models – is 18,235<sup>20</sup> . In the following we present the results obtained from the fully completed surveys from RS.

The overall response rate is 18 percent<sup>21</sup> This is a satisfactory ‘first contact’ response rate for both traditional (e.g. telephone) and online surveys (Cook, Heath, and Thompson 2000, Yeager et al. 2011, Pew 2012). We can analyze the location of each survey response using the respondent’s IP-address.<sup>22</sup> From the State capital of Porto Alegre there are 6,010 responses, and other cities with large number of respondents include: Pelotas (n=1,251), Caxias do Sul (n=1,122), and Santa Maria (n=1,098). Despite the reasonable response rate, concerns about non-response biases are still present: if respondents are missing at random, this is not a major concern, but it is also possible that there are systematic biases in non-response. It is therefore important to keep in mind that the results of this study are generated using a sample that may not be fully representative of the population of online voters. At the same time, however, we benefit from a very large sample drawn from across the entire state (Figure 1), and, we can infer a number of things by analyzing response patterns at the municipal level.

> 20 This reduced sample is partially the result of high non-response on the question of income, a notoriously sensitive question. To test whether this non-response affects our models, we reran the models without income both on the restricted sample of 18,235 that we use in the paper and a larger sample of 19,696 respondents who provided complete data except for income. Exactly the same set of parameters remained significant and in the same direction across both models and there were no large changes in effect size between the two. As a result, we conclude that item non-response is not driving the results in this paper.

> 21 The response rate is calculated based on the number of respondents compared with the total number of online voters.

> 22 Accuracy rates for naming the city using an IP address vary between 50%-80%, see http://whatismyipaddress.com/geolocation-accuracy, accessed May 19, 2014.

12