rafmacalaba/datause-encoder-data huggingface dataset:
America, correlation between nowcasting and actual poverty estimates are above 70 percent under the three methods, and over 0.90 for the NDG and the QGC.

PE tends to be outpaced by the other two methods. Aggregate errors, defined as the difference between poverty nowcasts and actual poverty rates, are quite small in general. In fact, the QGC performs slightly better than the NDG method when computing regional poverty nowcasts. For instance, the LAC average errors are about 1, 3, and 4 percent under the QGC, NDG, and PE methods, respectively. Once again, the PE and the QGC are relatively more dispersed than the GND.

Results under the QGC and NDG methods are encouraging both at the country and the regional levels. Validations show a good performance of both methods. The next section presents a series of sensitivity tests to changes in the default underlying parameters.

# **6 Stress and sensitivity analysis of poverty nowcasting methods in LAC**

In this section we test the robustness of the findings to changes in the underlying parameters –i.e., past information, pass-through, and number of quantiles used when nowcasting regional and country-level poverty rates. We perform all these test on the 15 countries for which data are available on a regular basis and for LAC as a whole.

# _6.1 Sensitivity analysis of the Informational Error_

The PE and QGC validation results use information on poverty elasticity and contribution to growth from periods -2 and -1 when nowcasting poverty in moment 0. However, with few exceptions, the 15 countries used in the analysis have micro data available on a yearly basis since 2003. This allows us to test whether the time span matters. For instance, we are interested in testing whether results are improved when using poverty elasticity and quantile contribution to growth between 2011-13 instead of 2012-13 when nowcasting poverty in 2014. The objective is to study the sensitivity of the PE to changes in the length of the period used to estimate the poverty elasticity and the quantile contribution to growth.

Table 4 shows 2014 poverty nowcasts for a range of periods for QGC and PE and keeping everything else unchanged. The table shows that the methods perform similarly irrespective of the length of periods used to estimate elasticity and growth contribution. However, there are a few exceptions for both QGC and PE, which can lead to outliers like the ones observed in Figure 1. For instance, QGC nowcasts increase significantly from 7.6 percent to 55 percent in Chile when 18
a sample frame of 5,000 informal firms in Dhaka clustered in about 100 small market places for our intervention. From this sample frame, we ran a baseline survey for a random sample of 3,200 informal firms in October-November 2012. The baseline survey verified that the firms were not registered with the tax authority and collected detailed information about the business and owners’ characteristics. Prior to the implementation of our intervention in June 2014, we conducted a follow-up phone check to verify that the location, ownership, and informality status of the firms from the baseline survey were still intact. We were able to complete and have information on about 50% of the phone calls made, and out of those completed calls we had 253 firms declaring to have a valid TIN and another 147 had closed down for good. We dropped those 400 firms from our working sample as well as another 100 firms determined to be ineligible for the sample and should note that this step happened before the treatment or randomization was implemented. Through this process we obtained a resulting sample of 2,700 firms. Those firms were then randomly assigned to 3 possible groups: i. treatment, ii. control in treatment markets, and iii. control in control markets as detailed below. This two-step randomization (as in Angelucci and De Giorgi (2009)) was performed in order to be able to capture withinmarket spillover effects. Note that in our sampling design we attempted to produce a census of firms operating in a small market. It would seem possible that control firms may react to the visits to treatment firms, if for example by noticing the NBR official (although there is nothing conspicuous about our envoys) or by information sharing with treated firms. In order to define a specific market we first used local knowledge and administrative boundaries to subdivide the city of Dhaka into a total of 43 larger markets each with about 55 firms on average. As this procedure resulted in fairly large markets occupying a wide area, we then split the largest 29 markets into 2 or 3 submarkets minimizing the distance between firms within a submarket (cluster) so that we obtained markets with an average size of about 25 firms, irrespective of the sector. The overall procedure ultimately resulted in the definition of 93 markets, which allows us to detect possible spillovers as well as maintaining enough power to detect the main effects. Ultimately the algorithm used splits markets according to the existence of physical barriers with administrative location such as water-flows, major roads, and buildings (see Figure A.1 in the Appendix).

The resulting 93 markets were then randomly assigned to treatment or control status and firms within treatment markets were split between treatment and control firms conditionally on 7
|**Item**|**IEA**|**IMF pre-tax**|
|---|---|---|
|Pipeline<br>transport costs|Country-specific estimates for some<br>countries, adjusted for distance|Same as IEA where IEA calculations are<br>used, not considered otherwise|
|Consumption|Natural gas used as feedstock for<br>manufacturing, power generation, or<br>district heating excluded; subsidies for<br>gas used in power generation captured<br>under electricity end-use subsidy<br>calculations|Same as IEA where IEA calculations are<br>used, all domestic consumption with the<br>exception of electricity otherwise|
|**Coal**|||
|Method|Price gap|For all 39 non-OECD countries, price-gap<br>estimations are taken from the IEA after<br>subtracting VAT; PSE for 16 countries<br>taken from OECD|
|End-user prices|Data provided by IEA’s network of country|partners and commercial databases|
|End-user price<br>data frequency|Monthly where available||
|Reference price<br>data source|Spot prices from IEA data and commercial|databases|
|Hub locations|Large number of locations. For example, th|ere are five prices in China.|
|Quality<br>adjustment|Prices already capture quality differences.||
|International<br>shipping|Costs for major routes obtained, and then|adjusted for distance.|
|Domestic<br>transportation|Match costs in individual countries with co<br>China, South Africa) and adjust for distance|sts in major markets (Indonesia, Colombia,<br>s.|
|Consumption<br>quantities<br>**Electricity**|Thermal and coking coal; coal used for dist<br>used in power generation captured under t|rict heating excluded. Subsidies for coal<br>he electricity end-use subsidy calculations|
|Method|Price gap|IEA subsidies in 37 countries, hidden cost<br>in 40 countries in SSA, MENA, and a few<br>European countries. For these 40, 2009<br>was the last year when data were<br>available.|
|Generation<br>sources|Fossil fuels only|For countries using hidden cost, all<br>sources of electricity included|
|Local tariffs|Data provided by IEA’s network of<br>country partners and commercial<br>databases|For hidden-cost calculations, tariffs from<br>utilities or country authorities|
|Reference<br>tariffs|Only fuel costs and T&D costs. T&D costs<br>are taken from OECD, US$0.015/kWh for<br>industrial consumers and US$0.04/kWh<br>for residential consumers. To avoid<br>unrealistically high reference prices,<br>costs are capped at those of a new<br>combined cycle gas turbine plant.|For hidden costs, country-specific costs of<br>generation, T&D, under-collections, and<br>losses|
|Consumption<br>quantities|<br>Consumption by end-user sectors|Consumption of all electricity for hidden<br>cost calculations.|

_Sources_ : IEA 2014b; Clements et al. 2013; OECD 2013a; IEA staff; IMF staff. _Note_ : For more information on the IEA’s methodology, see the IEA’s website at <u>www.iea.org/publications/worldenergyoutlook/resources/energysubsidies/methodologyforcalculatingsubsidies/;</u> 34
Table 1 General. Vocational and Agricultural Secondary Schooling: Comoarative Statistics

|Year|Nu|mber of Scho|ols|Nu|mber of Stu|de|nts|
|---|---|---|---|---|---|---|---|
||General|Vocational|Agricultural|General|Vocational|A|gricultural|
|1948/49|39|26|-|7,168|<br>2,002||-|
|1959/60|113|60|30|32,894|<br>10,167||5,016|
|1969/70|219|258|30|63,731|<br>49,556||7,641|
|1970/71|210|265|30|59,207|<br>53,847||7,462|
|1971/72|208|288|29|54,908|<br>60,039||7,189|
|1972/73|210|297|29|54,903|<br>63,778||6,683|
|1973/74|212|302|28|54,064|<br>64,068||6,464|
|1974/75|217|307|27|57,408|<br>64,648||5,877|
|1975/76|222|317|27|56,519|<br>65,677||5,655|
|1976/77|224|315|27|56,796|<br>66,307||5,371|
|1977/78|225|317|28|57,199|<br>66,613||5,620|
|1978/79|210|310|27|57,499|<br>67,720||5,460|
|1979/80|231|310|27|61,581|<br>70,681||5,108|
|1980/81|246|313|27|63,990|<br>73,785||4,704|
|1981/82|246|304|27|66,155|<br>76,361||4,284|
|1982/83|267|301|26|70,310|<br>76,636||4,970|
|1983/84|278|305|26|74,704|<br>79,957||4,692|
|1984/85|292|305|26|73,213|<br>84,631||4,648|
|1985/86|306|306|26|83,933|<br>89,385||5,104|
|1986/87|305|308|26|86,813|<br>91,720||4,683|

-6-
BRUNEI

# - 2. Projections NRR-1 by 2010

|**irsPopulatio**|**DO.**<br>ratio|**Priod**|_Growth_<br>rate|Cm|CDR|**TFr**|0<br>**Nale**|**F_mmt**|**In**|1000|
|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>256|58.4|1990*94|2.75|23.6|3.3|3.25|74.3|78.3|10|13|
|1995<br>294|55.8|1995<br>-99|2.28|21.7|3.3|2.85|75.6|80.1|9|11|
|2000<br>330|51.7|2000-04|1.81|19.2|3.4|2.50|76.8|81.6|7|9|
|2005<br>361|50.2|2005-09|1.33|16.6|3.8|2.19|77.3|82.3|6|8|
|2010<br>386|47.4|2010-14|1.13|15.6|4.3|2.07|77.9|83.0|6|7|
|2015<br>408|45.5|2014-19|1.05|15.3|4.8|2.07|78.5|83.7|5|6|
|2020<br>430|46.9|2020-24|0.95|14.9|5.4|2.07|79.1|84.4|4|5|
|2025<br>451|51.3|2025-49|0.54|13.2|7.9|2.06|80.4|86.0|3|4|
|2050<br>516|63.5|2050-74|0.18|12.1|10.3|2.06|81.5|87.7|2|3|
|2075<br>540|71.6|2075-99|0.05|11.8|11.3|2.06|82.1|88.6|2|3|
|2100<br>547|73.5|2100-24|0.03|11.7|11.4|2.06|82.6|89.3|2|3|
|2125<br>550<br>2150<br>552|74.3<br>74.6|2125-49|0.01|11.6|11.5|2.06|82.8|89.6|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>85-69<br>80-84<br>55-5 9<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>R2RRERERE BBB 1 ~~5-9 Rgg<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

TFR **eO** Growth rate <!-- Start of picture text --> Country Region Income *atu<br><!-- End of picture text --> Cowmtry Region Income grou.p _Co,atry_ Region Ircome group '3
# **Table 4 Land Sale Revenues and Infrastructure Spending Mumbai & India**

|**Item**|**Amount (in Rs millions)**|
|---|---|
|MMRDA Land Auctions<br>Bandra-Kurla Complex<br>January2006|2,2950|
|Total MMRDA<br>Infrastructure Investment<br>2004-2005|5400|
|Total Infrastructure Investment<br>Mumbai Municipal Corporation<br>2004-2005|1,0440|
|Pledge for Mumbai Infrastructure Investment<br>By State of Maharashtra<br>As Part of Mumbai Strategic Development Initiative<br>2005-2006|1,0000|
|Central Government Funding<br>For Infrastructure Investment and Services to Poor<br>In 63 Major Cities, Including Mumbai,<br>National Urban Renewal Mission<br>2005-2006|5,5000|

Note: One million = 10 crore Source: Data for MMRDA infrastructure spending, Municipal Corporation infrastructure spending, and State of Maharashtra pledge: Pathak (2005). “Infrastructure” investment excludes capital spending for education and health.

MMRDA’s revenue mobilization from land sales reflects both the market reality of a real estate boom and a change in asset management strategy, away from annual rental of land to upfront sale of land properties. Similar initiatives to realize land values and invest the proceeds in infrastructure are being undertaken in other parts of urban India. The poorer state of Rajasthan, for example, announced at the beginning of 2006 that it was turning over to the Jaipur Development Authority for sale at auction land having a minimum auction price of Rs 5.07 billion (roughly US$113 million), a sum that vastly exceeds annual infrastructure investment by all levels of government in the capital region of Jaipur.

As urban land sales presently are administered in India, most of the revenue proceeds accrue to agencies of state governments, especially the states’ Urban Development Authorities. Land sales thus have not been an instrument of fiscal decentralization to the local level or a significant contributor to municipalities’ capital budgets. The large sums generated by land sales have not, in the past, been publicly accountable, and often have been treated by the states as off-budget cash hoards that can be allocated for any politically attractive purpose. The new land 16
# **Table 2. Pre-Treatment Summary Statistics**

The sample consists of all nonfinancial listed firms in the CSMAR database. The table presents variable average and standard deviation (in the bracket below) for the entire sample, as well as a subsample of firms that receive land titles in the land titling program ( _Titling firms_ ) and firms that do not ( _Non Titling firms_ ). The difference between the values for Titling firms and Non Titling firms are presented in Column (4). The statistics are calculated based on the financial reports of firms **in 2011** , the last year before the title reform was implemented in 2012. The t- statistics in brackets of column (4) are for the null hypothesis that there is no difference between _Titling firms_ and _Non-Titling firms_ . *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||Whole Sample|Titling<br>firm|Non-titling<br>firm|Difference|
|---|---|---|---|---|
||N=1250|N=43|N=1207||
|Sales (Billion RMB)|5.315|5.317|5.266|-0.055|
||(8.237)|(8.231)|(8.507)|(-0.043)|
|Total asset (Billion RMB)|8.342|8.323|8.879|0.576|
||(11.017)|(10.998)|(11.650)|(0.338)|
|Employment (Thousand)|4.576|4.569|4.786|0.206|
||(5.708)|(5.668)|(6.803)|(0.232)|
|Book leverage ratio|53.965|53.850|57.171|3.250|
||(19.503)|(19.605)|(16.261)|(1.072)|
|Book to market ratio|0.364|0.365|0.350|-0.015|
||(0.235)|(0.235)|(0.239)|(-0.424)|
|ROA|5.564|5.569|5.410|-0.147|
||(5.179)|(5.213)|(4.151)|(-0.182)|
|Cash ratio|0.580|0.585|0.453|-0.131|
||(0.539)|(0.541)|(0.470)|(-1.569)|
|Dividend (Million RMB)|56.642|55.845|78.849|23.295|
||(96.953)|(95.808)|(124.079)|(1.553)|
|% of SOE|0.688|0.687|0.721|0.034|
||(0.463)|(0.464)|(0.454)|(0.468)|
|Industry: Material|0.176|0.179|0.070|-0.111|
||(0.381)|(0.384)|(0.258)|(-1.873)|
|Industry: Manufacture|0.225|0.223|0.279|0.058|
||(0.418)|(0.416)|(0.454)|(0.895)|
|Industry: Nondurable goods|0.173|0.175|0.116|-0.059|
||(0.378)|(0.380)|(0.324)|(-1.010)|

47
<!-- Start of picture text --> 24<br>22<br>20<br>18<br>ia<br>2000 2020 2040 2060 2080 2100<br>— Hist. Ref. Per., 1995-2014 — SSP4-1.9<br>— SSP1-2.6 — SSP2-4.5<br>— SSP3-7.9 — SSP5-8.5<br><!-- End of picture text --> <!-- Start of picture text --> 1750<br>1500<br>1250<br>1000<br>750<br>500 | AA<br>2000 2020 2040 2060 2080 2100<br>— Hist. Ref. Per., 1995-2014. — SSP4-1.9<br>— SSP1-2.6 — SSP2-4.5<br>— SSP3-7.0 — SSP5-8.5<br><!-- End of picture text -->
on sector or size of the firm. Big cities tend to provide higher pecuniary benefits for criminals and also criminal activity may involve lower costs due to the low probability of arrest and higher probability of anonymity. We control for firm size using dummy variables for small and medium firms. A small firm is defined as a firm with fewer than 20 employees, while a medium firm has between 20 and 99 workers. Of course, when we examine the effect of economic growth on crime for small and medium firms, we only control for small firms using a dummy variable as we restrict the sample to only small and medium firms. We control for sector or industry by using a dummy variable for manufacturing firms. A priori it is not clear whether a manufacturing firm should have higher crime losses with respect to other sectors. We include a city size dummy variable that takes the value of 1 if the city has a population of 250,000 or greater, or is a capital city, and 0 otherwise. There is also evidence that female owned households as well as female owned firms have a positive association with higher crime rates (Glaeser and Sacerdote, 1999; Islam, 2013; Kelly 2000). We also include a dummy variable for whether a firm has at least one female owner. All these variables are from the Enterprise Survey’s data set. We omit security costs from the base estimations due to concerns of simultaneity bias between crime losses and security costs, however later in the robustness section we do add it to the base specification and we find that the base estimation results are unaffected.

At the country level we control for the number of police per 100,000 population. The variable is obtained from the United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems. The variable is typically lagged by 1 year to reduce potential simultaneity bias between crime losses and police presence. Due to data constraints, for some countries the variable is lagged by more than one year. The specific details of the number of lags are available 10
the other hand, parents may be more willing to follow the advice of a (lay) CHW if they also receive the subsidy, for example because the link with the CSCom strengthens the CHW’s credibility.

On the acute care side, there is a similar possibility of an interaction effect with the CHW through the behavior of parents. First, in mild cases of diarrhea, the caretaker should monitor hydration and may provide ORT as necessary. Rehydration solution can be prepared at home using salt, sugar, and water, and the recipe is taught by the CHW. Positive effects on curative care may therefore arise because the CHW can nudge the parents to be proactive and provide knowledge to deal with mild cases of illness.

One of the main activities of the CHW in the realm of acute care, however, is that they directly monitor the child’s health during the biweekly visits and in the process teach parents to spot symptoms. The IMCI algorithms are designed to discern signs of serious illness and ensure that a child in need of evaluation sees a formal care provider. We may think of this as the CHW providing information about the child’s health status that is aimed at improving the targeting of formal care. For a child with diarrhea, a visit to a formal health care provider is recommended in more serious cases, such as symptoms for five days or more, dehydration, or blood in the stool.

As argued in Sautmann et al. [2020], the effect of information provision can be ambiguous, because the child’s caretakers will in general use new information to align care seeking with their own preferences, rather than those of the policy maker (here represented by the CHW – care-seeking recommendations). Information on its own can improve targeting e.g., by – alerting parents who are not aware how ill their child is but it may also have no or even a negative effect: simply put, the parent might learn to interpret symptoms but use their knowledge to postpone a doctor visit as long as possible. This friction may arise in particular due to high (private) costs of formal care to the parents, vs. the (social) benefits to the child or to others. This provides a strong case for CHW and subsidies acting as complements: the subsidy helps align the preferences of parents and policy maker by reducing private cost, 14
# 6 Robustness Checks

I do several robustness checks to test the main specication (Table 3). Column 2 uses an estimate of imported incidence without weighting the mobility data to be representative of the full population. This assumes that the only movement in the country is the movement in the Sonatel data, which would be an underestimate. Weighting the data represents an upper bound for the level of movement. The actual movement that occurs, and therefore, imported incidence, is then somewhere in between these lower and upper bounds. Thus the real eect should also lie between these upper and lower bounds of 2.1 and 1.7. In addition to weighting the movement, Column 3 scales both imported incidence and total incidence by health post utilization at the region level. The results are similar and not signicantly dierent from the results in the main specication (Column 1). Utilization is relatively uniform throughout the country; therefore, incorporating utilization does not signicantly change the results. I rerun the specication using Conley standard errors that account for spatial autocorrelation and serial autocorrelation over time (Column 4) (Hsiang 2010).<sup>29</sup> The results remain signicant.

In Column 5, I use net imported incidence, subtracting expected infected travelers leaving the health post catchment area. Both the coecients on imported and lagged imported do not change signicantly and they remain signicant. Results from additional robustness checks are available in Appendix B. These include (1) using cases rather than incidence; (2) adjusting the population that incidence is based on by the number of people entering and leaving the area each month; (3) loosening the assumption that the susceptible population is equal to 1 and scaling lagged incidence by a calculated susceptible population; and (4) restricting the movements included in calculating imported incidence by removing any movement where the SIM card had no calls or texts for more than two weeks before or after.

One limitation of the mobile phone data is that it only includes mobility within Senegal and cannot incorporate international migration. Immigrants coming in from high malaria

> 29I use a 30km cuto for the spatial correlation and two lags for the autocorrelation.

36