rafmacalaba/datause-encoder-data huggingface dataset:
At the subarea level, the satellite indicators explain an impressive amount of the variation in average non-monetary welfare. In sub-area level regressions, predictor variables explain 73 percent of the variation in average non-monetary welfare in Tanzania and 59 percent in Sri Lanka. The subarea model R<sup>2</sup> of 0.73 percent for Tanzania is substantially higher than the estimates presented in Jean et al (2016), which find that features derived from satellite data explain 58 percent of the DHS asset index in Tanzania. The stronger predictive power of the model for Tanzania likely results from the use of a richer welfare index as the dependent variable, as well as the use of interpretable features derived from imagery such as building footprints, rather than features optimized to predict night-time lights.<sup>24</sup> Meanwhile, the R<sup>2</sup> of 59 percent for Sri Lanka is remarkably similar to estimates of the explanatory power of monetary poverty reported in Engstrom et al (2017).<sup>25</sup> _Table 6: Post-lasso model of normalized household per capita consumption_

|**Sri Lanka**||**Tanzania**||
|---|---|---|---|
|Variable|Coefficie<br>nt|Variable|Coefficient|
|**Sub-area variables**||**Sub-area variables**||
|1990 built-uparea|-0.30|Sum of nighttime lights|0.03*|
|2014 built-uparea|1.82***|Meanpopulation(GHSL)|0.01|
|Standard deviation of rain|0.00|Minimum agglomeration index|-0.01*|
|Rain Z score, Q2|0.05*|Mean std. dev. of size of 5 nearest<br>buildings|-0.01|
|Rain Z-score squared, Q4|-0.45|Sum of mean size of 5 nearest buildings|<br>0.30***|
|Fourier Transform mean,scale 7|0.08***|Number of buildings within 100 m|0.05**|
|Line Support Region mean,Scale 7|0.47**|% area never built-up,1975-2015(GHSL)|-0.25|
|||% of areas built up1990 to 2000(GHSL)|0.13|
|**Area variables**||% of areas built up1975 to 1990(GHSL)|1.47***|
|1990 built-uparea|2.47***|% of area built-up (GUF)|0.04|
|Standard deviation of rain|1.06***|Precipitation in 2014|0.00|
|Rain Z score squared, Q3|-0.49|% Humid tropical rainforest (Kloppen<br>classification)|5.49***|
|Rain Z-score squared, Q4|-0.46|Standard deviation of NDVI|56.67***|
|Gabor standard deviation,scale 5|-0.83***|||
|Histogram of Ordered Gradients<br>standard deviation,Scale 5|-1.74***|**Area variables**||
|Line Support Region mean,Scale 7|-0.69**|Mean of nighttime lights|0.15|
|Structural feature sets mean,scale 7|1.00***|Minimum of nighttime lights|0.48|
|||Maximum economist costs of drought|0.00|

24 Ayush et al (2020) also finds that the use of interpretable features instead of features derived from transfer learning increased predictive power by 31 percent in Uganda, which is consistent with the stronger performance seen in these models.

25 Engstrom et al (2017) averages a model-based estimate of poverty derived from the census over large numbers of census households in each village, which makes it similar in practice to a non-monetary measure of poverty.

21
- Hanley, N., R. E. Wright, and W. L. Adamovicz (1998), Using Choice Experiments to Value the Environment. _Environmental and Resource Economics_ , 11, 413-428.

- Holmes, T. P., and W. L. Adamowicz (2003), Attributed-Based Methods. In P. A. Champ, K. J. Boyle, and T. C. Brown, eds., _A Primer on Nonmarket Valuation_ . Boston: Kluwer Academic Publishers.

- Horton, B., G. Colarullo, I. Bateman, and C. Peres (2003), “Evaluating Non-User Willingness to Pay for a Large-Scale Conservation Programme in Amazonia: a UK/Italian Contingent Valuation Study.” _Environmental Conservation,_ 30, 139-146.

- Hufschmidt, M. M., D. E. James, A. D. Meister, B. T. Bower and J. A. Dixon (1983), _Environment, Natural Systems, and Development: An Economic Valuation Guide_ . Baltimore: Johns Hopkins University Press.

- Kramer, R.A. and D.E. Mercer (1997), Valuing a Global Environmental Good: US residents' Willingness to Pay to Protect Tropical Rain Forests. _Land Economics_ , 73, 196–210.

- León, C. J., F. J. Vázquez-Polo and R. L. González (2003), Elicitation of Expert Opinion in Benefit Transfer of Environmental Goods. _Environmental and Resource Economics_ , 26, 199-210.

- Lindhjem, H., S. Navrud, N. A. Braathen, and V. Biausque (2011), Valuing Lives Saved from Environment, Transport and Health Policies. A Meta Analysis. _Risk Analysis_ 31, 1381-1407.

- Linstone, H.A. and M. Turoff (1975), The Delphi Method: Techniques and Applications; Addison-Wesley; Reading, Mass., USA 616 pp.; ISBN 978-0-201-04294-8. http://is.njit.edu/pubs/delphibook/Louviere, J. J., D. Hensher, and J. Swait (2000 _) Stated Choice Methods: Analysis and Applications_ . Cambridge University Press. Cambridge, UK.

- May, P., B. S. Soares-Filho, and J. Strand (2013), _How Much is the Amazon Worth? The State of Knowledge Concerning the Value of Preserving Amazon Rainforests_ . Policy Research Working Paper no 6668. Washington D.C.: the World Bank.

- Mitchell, R.C. and R.T. Carson (1989), _Using Surveys to Value Public Goods: The Contingent Valuation Method_ . Baltimore, MD: Johns Hopkins Press.

- Moulton, B. R. (1986), Random Group Effects and the Precision of Regression Estimates. _Journal of Econometrics_ , 32, 385-397.

- Moulton, B. R. (1990), An Illustration of the Pitfalls in Estimating the Effects of Aggregate Variables on Micro Units. _Journal of Economics and Statistics_ , 72, 334-337.

34
absorbed the bulk of private capital flows entering the region, accounting for about 80 percent of private capital flows accumulated between 1990 and 1997 (figure 2)2.

Figure 2. CEE: Major Recipients of Private Capital Flows (Cumulative, 1990-97) <!-- Start of picture text --> 20 - 100<br>18 90<br>16 80<br>14 70<br>12 60 5N<br>0<br>10 50 ~<br>'~8 404<br>6 30<br>4 20<br>2 10<br>0 0<br>Hungary Czech Rep. Poland Slovak Rep. Others<br><!-- End of picture text --> _Source:_ World Bank, _Global Development Finance_ 1998.

Third, easily reversible capital flows (defined as short-term debt and portfolio equity investment) have been increasing steadily, from 7 percent in 1993 to 25 percent in 1997. In addition, there are unresolved questions about the reliability of data that distinguish direct investment from other capital flows, and some research has shown that net foreign direct investment flows are quite volatile too (Dooley, Fernandez-Arias, and Kletzer, 1994).

Fourth, debt-creating flows (lending and bonds) have been a major source of private capital.

Since 1993 these types of flows have accounted for about half of the net private flows reaching the CEE

> 2. A large part of those flows has been strongly related to major privatization initiatives. Sales of government shares in electricity and gas distribution, banking and telecommunications in Hungary (worth some $4.5 billion) and a large Telecom sale in the Czech Republic ($1.5 billion) provided in 1995 a onetime boost to accumulated foreign direct investment in these countries. As a consequence, in 1995 the Czech Republic and Hungary registered record private capital inflows of 16 percent and 19.1 percent of GDP, respectively.

4
Table N14: SNAP participation and effect of SNAP-benefit changes on prices, ARRA expiration

|Specifcation|(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>IV|(7)<br>OLS|(8)<br>IV|
|---|---|---|---|---|---|---|---|---|
|VARIABLES||||Log price|index||||
|Log benefts p.p.|0.0366***|0.101|-0.00252|-0.0441|0.0250|0.0895|0.00389|-0.0431|
||(0.0122)|(0.0901)|(0.0127)|(0.0492)|(0.0159)|(0.113)|(0.0156)|(0.0612)|
|x Participation rate|||||0.0775<br>(0.0537)|0.0629<br>(0.147)|-0.0427<br>(0.0780)|-0.00699<br>(0.0954)|
|Observations|85836|85836|321885|321885|85836|85836|321885|321885|
|R-squared|0.972|0.971|0.915|0.914|0.972|0.971|0.915|0.914|
|Prob _>_ F|0.000|0.000|0.000|0.004|0.000|0.000|0.000|0.002|
|Number of units|7153|7153|7153|7153|7153|7153|7153|7153|
|Number of clusters|48|48|48|48|48|48|48|48|
|First stage F-stat||5.182||9.670||2.632||4.952|
|Sample|2013|2013|2012-2015|2012-2015|2013|2013|2012-2015|2012-2015|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as store and period fixed effects are also included. Log benefits p.p. refers to log benefits per population. Participation refers to the ratio of SNAP participants to population in each county fixed to the pre-period of 2011.

82
The dependent variable is the risk index of household _i_ ’s crop portfolio at time _t_ . _Dijt_ represents a household’s access to the NREGS. Let _Xit_ be a set of time-varying household characteristics that affect preferences and crop choice (such as education, wealth, income and past experience with shocks) and _ui_ be time-constant unobserved household characteristics (such as risk aversion, farming ability and land quality). _Zjt_ is a set of time-varying village-level characteristics (e.g. weather trends, extension services, prices, etc.), _γj_ are time-constant village characteristics (such as the land’s suitability for certain crops), _δt_ is a time fixed-effect and _υijt_ is the error term.

Taking the first difference removes unobserved household and village level characteristics that are constant over time:<sup>25</sup> For _β_ 1 to have a causal interpretation, the differences in the change of the risk index between the treatment and control groups must be entirely due to the NREGS. This assumption could be violated for a number of reasons. First, since the access to the NREGS is non-random, treatment could be correlated with potential outcomes of _Rijt_ . Second, households in the treatment and control group may not be following parallel trends in their crop choices. The remainder of this section discusses how I address these points.

This article uses four different treatment variables. First, as discussed above, I explore the universal nature of the NREGS by coding as ‘treated’ those households based in districts where the NREGS was introduced in 2006. Second, I use lagged block-level disbursements under the program as an indicator of the intensity of treatment, arguing that households living in blocks with higher past disbursements expect employment to be more readily available in situations of need. The average lagged disbursement in treatment districts is INR 14.27 Mio. with a standard deviation of 9.64 in 2009/10. Third, following the same logic, I use the lagged annual total of employment person-

> 25With two time periods, taking the first differences is essentially the same as estimating the model in

15
to identify takeovers (e.g., Todd and Heining, 2020), we are able to identify all takeover events. Having data on takeovers means that we can accurately capture these events even for small firms. Appendix A provides details on specific administrative datasets that were used to construct our sample.

We combine information on takeovers with a matched employer-employee dataset for all Dutch firms and workers covering the 2006–2019 period. This dataset contains each employment spell at the monthly level, including information on earnings, hours, and contract types. To assess the labor market outcomes of workers, we also link to an administrative dataset covering income from various government benefits, including unemployment insurance benefit and social safety net payments.<sup>1</sup> The final employee-level dataset used is an education database, which contains education information for a subsample of workers. While coverage of this dataset is increasing over time (from 47% in 2006 to 71% in 2019), its sample is skewed towards younger, more educated workers. We use an inverse Mincer equation to impute education levels for workers for whom education information is missing. For the full procedure, we refer to Appendix B.

# **2.2 Sample Construction and Matching Procedure**

We focus on takeovers that happened between 2011 and 2015. We make a number of sample restrictions. First, we restrict the sample to takeovers that involve firms that are active in the four years prior to the takeover and that do not experience any firm event (e.g., takeover, merger, etc.) in those intervening years. Second, because we are interested in employee outcomes, we require that both consolidating firms have at least three employees in the year prior to the takeover. This restriction ensures that we eliminate firms with only self-employed individuals and owners. Third, we restrict the sample to takeovers in which both consolidating firms only have one business unit prior to the takeover.<sup>2</sup> Finally, because our empirical strategy uses matching, we require that the variables used in the matching process are non-missing.

For our main analysis, we focus primarily on effects for _incumbent workers_ who were present at the target firm four years prior to the takeover. While this restriction prevents us from looking at results for workers with shorter tenures, it also allows us to avoid conditioning our sample on

> 1Our administrative dataset covers all Dutch residents and firms, therefore selective exit from our sample does not affect results related to retention and compensation at workers’ original firms.

> 2Appendix A provides more details on our definition of a firm vis-`a-vis Dutch categorization and firm hierarchies.

5
12 Table 9: Main NTBs Applied on EC Imports from Hungary,<sup>1988</sup>

|Type of NTB<br>Fr|equency|Main cat. of products<br>concerned|
|---|---|---|
|Tariff quotas<br>Seasonal tariffs|4<br>17|sheep and lambmeat<br>apples,cherries|
|Variable levies|19|sheep and porkmeat,other<br>animal pr.,preserved fruit|
|Variable component|1|extracts of coffee|
|Licenses|48|meat,preserved fruit and<br>vegetablea,maize,wine|
|Quotas|2|preserved vegetables|
|Quotas by country<br>VERs|4<br>22|sheepmeat<br>iron and steel products|
|MFA restrictive agr.|217|textiles and clothing|
|Retr. surveillance|1|nitr.fertilizers|
|Community surveillance|<br>32|apples,footwear,steel pr.<br>|
|Reference prices|47|fr.fruit&veg.,maize,wine|
|Price surveillance|228|text./clothing,iron/steel pr.|
|Anti-dumping investig.|<br>2|urea,nitr.fertilizers|
|Undertakings|7|alum.oxide,sulphates,drawn<br>wood, glass, transformers,<br>el.lamps and bulbs|

Source: Official sources in the EC (mainly the EC Official Journal,<sup>GATT</sup> documents, and the EC Tariff Schedules) computerized by<sup>UNCTAD. See Annex III</sup> for more details.

The relative importance in the use of NTBs by the combined<sup>member states</sup> of the EC-10 (i.e., not EC group measures) in the food sector is given in Table 10. This table shows the percentage of EC imports from Hungary (in 1984 value terms, excluding intra-EC trade) affected by different broad<sup>categories of NTBs.</sup> Trade is defined by broad standard groupings (with SITC item 67 being included in Ores and metals). The types of NTBs have been categorized<sup>into the following</sup> broad categories: price-increasing NTBs (e.g., variable<sup>levies, anti-dumping</sup> duties, tariff quotas, etc), threats (e.g., monitoring<sup>systems, automatic</sup> licensing, anti-dumping or countervailing investigations. etc. )<sup>8</sup> , and quantitative restrictions (e.g., quotas, voluntary export restraints, restraints under the MFA, etc.).

The data in Table 10 are aggregated from the data for the individual member states which have applied a number of NTBs independently of the<sup>EC policy.</sup> The continued existence of some differences in the application of NTBs by individual members states mainly results from the grandfathering of NTBs<sup>applied</sup> prior to the Treaty of Rome. In 1992 with the unification<sup>of the market it is</sup> intended that NTBs will only be commonly applied.

**8** The inclusion of "threats" as NTBs is based on the chilling effect of these measures which have been the subject of other<sup>studies.</sup> For example, Messerlin (1989) shows that five years after the initiation<sup>of anti-dumping</sup> investigations by the EC, imports of items covered in the<sup>inquiries dropped to</sup> half the initial level. Imposition of anti-dumping duties led<sup>to even further</sup> reductions in imports.
However, it might also be the case that parents/students attempted to enroll their students in alternative types of schools but were not successful, perhaps due to crowding or limited spaces in other types of schools. Distinguishing the mechanisms that affect enrollment in the different types of schools is an important question for future research.

Tables 8a to 8c show similar tables for the effect on high school enrolled in the twelfth grade. These results show that there is an increase in the probability of attending a general high school and a corresponding reduction in the probability of attending a technical high school. There is no significant effect on the probability of attending a technical professional school or on the probability of attending a private school. Compared with the results for secondary schools, these results do not suggest that the enrollment effect leads students to shift towards lower quality schools. However, further examination of the characteristics of the schools that PROSPERA children attend would be useful. The results do not differ by gender.

Table 8a: Impacts of PROSPERA on Type of High School All 6<sup>th</sup> Graders in Medium, High and Very Highly Marginalized Communities 2008 State Fixed Effects

||General<br>HS|Technical<br>HS|Technical/Prof|Private|
|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|
|PROSPERA beneficiary 2008|0.049***|-0.049***|0|0|
||(0.005)|(0.005)|(0.002)|(0.002)|
|Female beneficiary|-0.003|0.01|0.001|-0.008***|
||(0.007)|(0.007)|(0.002)|(0.002)|
|Female|-0.003|0|-0.004***|0.007***|
||(0.006)|(0.006)|(0.002)|(0.002)|
|Constant|0.649***|0.343***|0.013|-0.005|
||(0.033)|(0.035)|(0.01)|(0.01)|
|Observations|62,870|62,870|62,870|62,870|
|R-squared|0.079|0.085|0.011|0.037|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

23
- 24 -

on a permanent basis solely for this purpose. Calcutta Corporation, for example, maintains a staff of 768 field surveyors. Bombay employs 1,895.

# D. Billing -and Colec-Lon

3.70 Low collection efficiency is a major constraint on the yield of the property tax, and it can offset any gains made from improving discovery and valuation. The available data suggests that typically half of current property taxes are not paid in the year in which they are due. In Amman, Jordan, the collection ratio is 62%; in Rio, 60%; in Calcutta, 55%. Ibadan, Nigeria, has the lowest collection efficiency among the cities surveyed, at 9%.iW 3.71 Success at collection is essentially a matter of information management and leverage: knowing who owes what, and having the means and incentive to induce them to pay. There are two targets for reform: (1) the legal framework--defining what is liable, what constitutes notification, and what penalties may be imposed; and (2) collection administration: managing the production of bills, the monitoring of payments, and the pursuit of delinquents.

# The Legal Framework

3.72 Define Owner as Liable. but Define OwnershiR Broadly. An appropriate legal definition of liability and notification can simplify the billing function and increase the taxing authority's leverage over delinquents. For tax purposes, the sole objective in defining tax liability is to make the tax collectible--to find a person to whom the taxing authority can apply sufficient leverage to extract the tax. The first decision to be made is whether to designate the owner or the occupant as liable.

3.73 Most countries--both developed and developing--define the property owner, rather than the occupants, as ultimately liable for the property tax. This has two administrative virtues: (a) It reduces the number of individual taxpayers, because only one bill need be issued for structure housing multiple occupants; and 1§/ Data on property tax collections often are not organized in a way that would permit an accurate analysis of ccAlection performance. Some jurisdictions (Onitsha) combine current payments and payments on arrears (including interest and penalties), overstating the numerator. Others do not distinguish current liabilities from liabilities on arrears, overstating the denominator. Some do not report collections on arrears as part of property tax revenue at all. In Brazil, for instance, payments recovered through lawsuits are grouped with receipts from other fines. Results can therefore be misleading. In some Indian cities in particular, high rates of collections on arrears compensate for low collection efficiency on current account. In Calcutta, for example, late payments comprise 45% of total property tax receipts--suggesting that most liabilities are paid, eventually.
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