rafmacalaba/datause-encoder-data huggingface dataset:
28 Vladimir and 26.7% in Gorodets (Fig. 2.8). Among married couples with children, 31.8% and 22.6% respectively would like to get a loan. The effect of the income level ius demonstrated in Fig. 2.9.

Fig. 2.8. Willingness to take a loan for the purchase of housing, by level of provision with housing (by quintiles based on per capita provision with total floor space; % of each quintile) <!-- Start of picture text --> 40-.<br>36<br>35<br>25<br>20<br>.5 4';$ I s:4 5<br>40 1.5<br>0.<br>Vladimir CGorodets<br><!-- End of picture text --> Note: I - lowest quintile, 5 **-** highest quintile **Source:** Vladimir and Gorodets Longitudinal Household Survey, December 1996.

**Fig. 2.9.** 15~~~~~~ Willngness **to** take a loan **4for** the purchase of t **h** uint les ous **i** ng (by **3** based on per capita income; **4'M** % of each quintile)

<!-- Start of picture text --> 40 -<br>35<br>30<br>25 22 21<br>20 1<br>1iis e a i7 13 3 13.4<br>VI2 3 4 5 1 2 3 ts4 5<br>Vladimir ~~~~~~Gorod*t<br><!-- End of picture text -->

**Note: 1- lowest quintile, 5 highest quintile Source- Vladirnir and Gorodets Longitudinal Household Survey, December** 1996.

A comparison of the categories of households planning to improve their living situation and those who would like to take a long-term loan (Fig. 2.10) shows that:

- t in Vladimir and Gorodets, there are similar trends and proportions as regards loans

- only half of the households intending to obtain housing through miarket methods are willing to take out a loan. The rest seem to rely on other sources;

- of those **on** housing waiting lists, some (about one-third) have nothing against paying to solve this **problem (provided** they are able to receive financing). The **propor-tion** of such households in Vladimir is **2.4%/, and** in Gorodets, 1%;

- it is especially important that _5.9%_ of the households in Vladimir and _5.2%_ in Gorodets, who do not see **how** they could improve their living conditions, would like to **iuse** a long-term loan to get access to the housing market.
with the highest capitalization, hence with potentially the highest aggregate untaxed rents, are not the mining companies.

_Figure 4: Ranking of South African Companies by Market Capitalization -US$ billion (Mining in Yellow)_ <!-- Start of picture text --> 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0<br>Naspers<br>FirstRand<br>Standard Bank Group<br>MTN Group<br>Capitec Bank<br>Vodacom<br>Sasol<br>Gold Fields<br>Impala Platinum<br>Sibanye-Stillwater<br>AngloGold Ashanti<br>Shoprite<br>Bid Corp<br>Sanlam<br>Nedbank<br>Discovery Limited<br>Mediclinic International<br>Pepkor<br>Remgro Limited<br>The Bidvest Group<br>Clicks Group<br>Aspen Pharmacare<br>African Rainbow Minerals<br>Exxaro Resources<br>Old Mutual<br><!-- End of picture text --> _Source:_ _<u>https://companiesmarketcap.com/</u>_ Because market capitalization confounds rent and quasi-rent, isolating the resource rent at individual mining projects can be best done by resorting to the only time when resource rent can be measured – that is, prior to investments that generates quasi-rents. This is best done by observing the market capitalizations of listed exploration companies that have no demonstrated mineral reserves or mineral resources and therefore no appreciable value added to the raw resource potential through investment. These market capitalizations approximately reflect the remaining untaxed resource rents of the projects in their portfolios.<sup>13</sup> With pure mine exploration company market capitalizations typically well below US$10 million, and with these companies usually owning multiple exploration projects in their portfolios, the remaining resource rents available for taxation at a given mineral exploration property is likely in the millions or tens of millions of dollars. Table 1 provides the value per project for selected pure exploration companies that are likely to have almost no quasi-rent capitalized into their market capitalization because 13 The value of the assets is market capitalization, plus debt, less cash holdings. For companies like this, debt and cash holdings are approximately offsetting. A stricter accounting of asset value, which we have not undertaken, would not undermine the point that project rent is minimal.

10
cases, we allow the coefficients to vary by sector. We find that the coefficient of withinfirm variance in technology and average level of GAP do not differ significantly between agricultural and non-agricultural firms. Importantly, including these additional controls does not affect the difference in the coefficient of average technology on productivity between agricultural and non-agricultural firms.

Table 13: Productivity and Technology Sophistication, Industry-heterogeneity Continued

||(1)|(2)|
|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|
|GBF EXT*AGRI|0.52**<br>(0.23)|0.44**<br>(0.22)|
|GBF EXT*Non-AGRI|0.54***<br>(0.07)|0.50***<br>(0.07)|
|SSBF EXT*AGRI|0.46*<br>(0.25)|0.42*<br>(0.25)|
|SSBF EXT*Non-AGRI|0.10*|0.04|
||(0.06)|(0.06)|
|Var(ABF EXT)*AGRI||0.34<br>(0.30)|
|Var(ABF EXT)*Non-AGRI||0.41***<br>(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|
|R-squared|0.48|0.49|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

Table 13 further explores the origin of the sectoral variation in the coefficient of average firm-level technology on productivity by separating GBFs and SSBFs. The main finding from this table is that the difference in the coefficient comes from SSBFs. Specifically, we find that the coefficients for the average technology level in GBFs is very similar for agricultural and non-agricultural firms but, while the coefficient on SSBF in the productivity of non-agricultural firms is essentially zero, the coefficient on agricultural firms is large and significant.

Next, we use the estimates from Table 12 and Table 13 to conduct a cross-sector development accounting exercise to assess the fraction of the sectoral gap in cross-country 40
**Table 4.2 (continued): Sample representativeness in Nigeria HFPS**

||**Nigeria(**|**GHS-Panel)**<br>|||
|---|---|---|---|---|
||**All**|**With**<br>**Contact info**|**HFPS**|**Sample**<br>_Difference†_|
||_Mean_|_Mean_|_Mean_|<br> _(3) -(2)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|
|**_Housing_**|||||
|Improved roof|0.886|0.887|0.884|0.002|
|Improved floor|0.776|0.780|0.777|-0.001|
|Improved toilet|0.602|0.603|0.609|-0.007|
|Improved water source|0.765|0.766|0.764|0.001|
|Access to electricity|0.554|0.558|0.565|-0.010|
|**_Asset ownership_**|||||
|Solar|||||
|Generator|0.246|0.247|0.237|0.009|
|TV|0.451|0.454|0.462|-0.011|
|Refrigerator|0.173|0.174|0.178|-0.005|
|Bicycle|0.157|0.157|0.151|0.006|
|Motorbike|0.306|0.305|0.310|-0.004|
|Car or other vehicle|0.096|0.097|0.094|0.002|
|Mobile Phone|0.760|0.764|0.752|0.008|
|**_Other activities_**|||||
|Owns land|0.669|0.667|0.666|0.004|
|Cropfarming|0.699|0.697|0.696|0.003|
|Owns anylivestock|0.469|0.466|0.464|0.005|
|Operates nonfarm enterprise|0.595|0.596|0.602|-0.007|
|Anymember has financial account|0.493|0.496|0.500|-0.007|
|Income from remittance(int'l & domestic)|0.319|0.321|0.308|0.011|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

# <mark>4.1.3 Non-response bias</mark>

The last source of bias to examine is for non-response. Non-response as considered here includes cases from the HFPS sample that were either (1) not successfully contacted or (2) contacted but not successfully interviewed (refused or breakoff mid-interview). Both of these sources of non-response (non-contact and non-interview) will be considered separately here.

Table 4.3a presents the comparison of the weighted mean characteristics for the HFPS sample (columns 1, 6, 11, and 16), sample of households successfully contacted in the HFPS (columns 2, 7, 12, and 17), and the sample of households successfully interviewed in the HFPS (columns 4, 9, 14, and 19) for the four 19
**Table 3:** Effect on consumption and poverty

||(1)<br>lg(cons)|(2)<br>lg(non-food)|(3)<br>lg(food)|(4)<br>Poor|(5)<br>Food Poor|
|---|---|---|---|---|---|
|NDVI Z-score (2018)|0.052<sup>_∗∗∗_</sup>|0.140<sup>_∗∗∗_</sup>|0.014|-0.058<sup>_∗∗∗_</sup>|-0.019|
||(0.014)|(0.020)|(0.018)|(0.013)|(0.014)|
|R-squared|0.438|0.586|0.294|0.208|0.183|
|Temperature Z-score (2018)|-0.050<sup>_∗∗_</sup>|-0.143<sup>_∗∗∗_</sup>|-0.001|0.051<sup>_∗∗∗_</sup>|-0.001|
||(0.020)|(0.030)|(0.026)|(0.019)|(0.018)|
|R-squared|0.423|0.450|0.284|0.198|0.179|
|Mean Dep var (transformed)|7.751|6.186|7.229|0.47|0.52|
|Mean Dep var|2571|694|1495|0.47|0.52|
|Observations|13930|13930|13930|13930|13930|
|HH and Shura Controls|✓|✓|✓|✓|✓|
|District FE|✓|✓|✓|✓|✓|

_Note_ : This table reports the coefficient on the z-score of the weather indicators in equation 3). For each weather indicator (NDVI and temperature), we run three separate regressions. Controls include (i) household head’s gender, age, age-squared, and indicator for whether the head is literate, (ii) indicator variables for rural household location, land ownership, and livelihood ownership, access to piped water, electricity and use of gas/electricity as cooking fuel, and (iii) indicators for the presence of a primary school in the community, distance to the nearest primary health care facility, and distance to the nearest hospital in the community. Dummies for quarter in which the interview was conducted are also included. District fixed effects are included. Robust standard errors reported in parenthesis are clustered at the cluster level. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

31
This acts against this assumption that a better road access entails more job opportunities. However, one should note that the labor variable under consideration here only partly proxies for employment opportunities, since the reference group encompasses both unemployed and other inactive household-heads. Irrespective of this issue, another potential explanation can be found in the very particular shape of employment in Africa. In Africa in general and thus in Cameroon the issue of activity and employment lies in a lack of sufficient revenues and underemployment rather than a conventional unemployment status. A large part of the employment is in fact corresponding to informal or agriculture activities with an average little number of worked hours or low productivity activities and therefore procures small revenues. In South Saharan Africa about 30% of the population is actually under-employed (Africa development indicators, 2008-09). Finally a large part of the activity category consists in agriculture activities<sup>19</sup> on which isolation has a positive and significant impact as we will see in the Table 4.

> 19 As a robustness test, we performed the same estimates on the rural sub-sample. Our results broadly hold: the only exception is the non significant impact of road access on the probability of being active. See Tables 6 to 8 in Appendix 2.

20
and to determine the principles of the school. This has resulted in a large number of non-public schools financed by the state. Moreover, parents can typically choose among several schools. Parents have access to a variety of schools, access is not selective, all schools are equally publicly financed, there is ease of entry of providers into the market, and information flows. Most children in the Netherlands attend privately-managed schools and the. Private schools are not for profit and usually managed by a foundation or church.

Studies of the effectiveness of school choice programs suffer from an inability to control for selection and are usually not able to attribute outcomes to the type of school attended. Since most schools in the Netherlands are private and we know that religion is a key factor associated with parental choice of school, we use this fact to create an instrument by which to measure the effectiveness of private schools in the Netherlands.

We are able to show that private schools have a positive and significant impact on school outcomes using the OECD’s Program for International Student Assessment (PISA) 2006 data for the Netherlands. Only when we property account for selection do we show a positive and significant impact. In fact, OLS estimates show a negligible or insignificant effect of private schools. The IV results show that private school attendance is associated with higher test scores. The true effect of private schools is higher than in the base (OLS) equation. The effect size is higher and the private school variable becomes positive and significant in the case of science as well. Private school size effects in math, reading and science achievement are 0.17, 0.28 and 0.18, all significant. Given PISA’s scaling, this is close to 0.2 of a standard deviation in the case of math and science, and almost 0.3 of a standard deviation in reading. In other words, these are large effect size effects, indicating that school choice contributes to achievement in Netherlands. The reasons for an impact, despite the almost equal raw scores in achievement between public 23
# _Table 7_ . Season-Specific Potential Production Gains at Frontier Attainment

|||MAPS|Suitabilit|y Class|W|ealth Terc|ile|Househ|old Head|
|---|---|---|---|---|---|---|---|---|---|
||Total|S1|S2|S3|1|2|3|Male|Female|
|Plot Level Means||||||||||
|Plot Area|0.146|0.182|0.144|0.121|0.125|0.161|0.152|0.151|0.126|
|Observed Yield|1068|1614|1015|828|1005|1046|1153|1091|981|
|MAPS-Based Potential Yield|1804|3009|1714|1315|1749|1822|1919|1860|1714|
|Production Gain if Attaining Productivity Frontiers|:|||||||||
|MAPS Class-Specific Frontier (kg)|108|254|101|59|93|125|116|116|92|
|_(USD)*_|_46_|_110_|_44_|_25_|_40_|_54_|_50_|_50_|_40_|
|MAPS S1 Frontier (kg)|283|254|287|263|250|317|282|290|256|
|_(USD)*_|_123_|_110_|_124_|_114_|_108_|_137_|_122_|_125_|_111_|
|GAEZ High-Input Frontier for S1 Class (kg)|1050|1210|1043|897|906|1164|1080|1084|918|
|_(USD)*_|_454_|_523_|_451_|_388_|_392_|_503_|_467_|_469_|_397_|
|Household Level Means||||||||||
|Total Household Maize Area<sup>†</sup>(Ha)|0.283|0.283|0.286|0.264|0.238|0.297|0.314|0.296|0.232|
|Production Gain if Attaining Productivity Frontiers|:|||||||||
|MAPS Class-Specific Frontier (kg)|208|394|200|129|177|230|241|228|170|
|_(USD)*_|_90_|_171_|_87_|_56_|_77_|_99_|_104_|_99_|_73_|
|MAPS S1 Frontier (kg)|549|394|571|576|477|582|583|568|470|
|_(USD)*_|_237_|_171_|_247_|_249_|_206_|_252_|_252_|_246_|_203_|
|GAEZ High-Input Frontier for S1 Class (kg)|2035|1880|2075|1962|1729|2140|2233|2124|1686|
|_(USD)*_|_880_|_813_|_897_|_848_|_747_|_925_|_965_|_918_|_729_|
|N|840|106|634|100|280|280|280|667|173|

Notes: † = Total household maize area estimated via multiple imputation founded on self-reported plot area estimates and other observable characteristics; * Monetary values estimated based on a FEWSNET price of UGX 1450 per kilogram of maize grain and converted at an exchange rate of 1 USD : 3354 UGX.

25
of the provider. These were most likely staff mapped to the clinic who are not licensed to provider care, but do so when the doctor is absent.

The discrepancy between who we sampled and who we actually saw does not affect interpretation of our results in Panels A and B of Tables 3-5 but it does in Panels C. Panels A and B present results without provider controls, therefore, whether or not we have background data on the provider is not relevant. Including the small fraction of observations where we saw unintended and unknown providers, the public-private difference here should be interpreted as the difference random visits to providers’ clinics than providers. In Panels C, we present results including provider controls. Here, for 27 interactions where we saw providers we did not sample but mapped (and conducted provider census), we use their background information. The 18 observations where we do not know the provider at all are dropped from the estimation sample.

# **A.4 Mapping of Providers: Dual Practice Sample**

We obtained a list of all Primary Health Centers (PHCs) and Community Health Centers (CHCs) from the Ministry of Health of Madhya Pradesh. Excluding PHCs/CHCs which were mapped as part of the representative sample, we mapped 200 more facilities in this round. Of these 200 facilities, 40 did not have a MBBS provider posted (see Appendix Table A.2). In the remaining 160 PHCs/CHCs we located 216 providers (some providers were mapped to multiple facilities). Our field team then undertook detailed field work to find out if these providers operated private practices and if yes, to locate their private practices. We found thatwere able to locate a private practice for 132 of the 216 providers (61.1 percent) operated a private practice (this is the sample we call the “dual practice sample”.). After the mapping, we administered the provider census to all providers. To the extent possible, the census was administered in the private clinic of the provider.

# **A.5 Sampling of Providers: Dual Practice Sample**

We sampled one provider from every PHC/CHC with preference for a dual practice provider. Often a provider is posted to multiple public facilities, and in cases where there were no additional providers in these facilities, we randomly sampled the provider from one of the multiple facilities they were posted to. With this sampling strategy, we sampled from 143 of the 160 facilities we could have sampled from. Of the 143 providers, 94 operated private practices (65.7 percent, see Table A.2).

43
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- De Mel, S., McKenzie, D., and Woodruff, C. (2009). Are women more credit constrained? experimental evidence on gender and microenterprise returns. _American Economic Journal: Applied Economics_ , 1(3):1–32.

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26
and A2); three global circulation models (IPSL-CM4, MIROC3.2, ECHO-G)<sup>10</sup> ; and three annual subsidence rates for land in the lower Ganges Delta (2, 5 and 9 mm/year).<sup>11</sup> Figure 4 illustrates the scale of potential change by combining our 2012 salinity map with the maximum change result for 2050 (AR4 A2 scenario, IPSL global circulation model, 9 mm/year subsidence in the lower Ganges Delta). Two patterns are particularly striking in Figure 4. First, higher salinity spreads northward and eastward as the sea level rises, riverine flows change and subsidence continues in the lower Ganges Delta. Second, the transition from 2012 to 2050 is most pronounced in Koyra and northern Shyamnagar.

Figure 4 illustrates the scale of potential change by combining our 2012 salinity map with the maximum change result for 2050 (AR4 A2 scenario, IPSL global circulation model, 9 mm/year subsidence in the lower Ganges Delta).

10 Model implementing institutions are as follows: IPSL-CM4: Institut Pierre Simon Laplace, France; MIROC3.2: Center for Climate System Research, University of Tokyo,

- National Institute for Environmental Studies, Japan,

- Frontier Research Center for Global Change, Japan;

- ECHO-G: Meteorological Institute of the University of Bonn, Germany,

- Model and Data Group, Max Planck Institute for Meteorology, Hamburg, Germany, Korea Meteorological Administration.

In their salinity modeling exercise, Dasgupta et al. (2015a) started with the temperature and rainfall projections for the territory of Bangladesh from 9 GCMs. Models were then ranked from low to high based on their median projections. Finally, three GCMs (IPS-CM4L, MIROC3.2, and ECHO-G) with the highest, intermediate and lowest ranks and the corresponding projections were used to capture the uncertain extent of climate change.

11 The Ganges Delta in Bangladesh is still in an active, dynamic state. Therefore, it is critical to include projection of land subsidence of the lower Bengal delta (the Ganges Delta in Bangladesh) in simulating future climate scenarios. Physical impacts of relative mean sea-level rise are caused by a combination of sea-level rise scenarios associated with global warming and vertical land movement (subsidence or accretion). At present there is an intense controversy in Bangladesh regarding the estimates and projections of land subsidence in the coastal region (see Dasgupta et al. 2015). In light of the widely varying estimates, the hydrological modeling for our analysis was run for three alternative scenarios of land subsidence: 2 mm per year, 5 mm per year, and 9 mm per year.

13
<!-- Start of picture text --> 0.08<br>noOi<€SD<br>€<br>3°joy<br>§ 0.06<br>S&S<br>=<br><<br>&<br>@<br>€3<br>E 004<br>fo}<br>a<br>Q<br>3<br>Q<br>aaoa<br>0.02<br>2014 2015 2016 2017 2018 2019<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br>- 5<br>8<br>5<br>0)<br>0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000<br>Number of IDP Households<br><!-- End of picture text -->