rafmacalaba/datause-encoder-data huggingface dataset:
privatizations by legal entities by at least 23% (based on the data for all Russian regions) or from 6.4 transactions to 7.9 transactions per 100,000 residents.

# **Table 3.2. OLS results**

_Dependent variables: Number of land privatizations in a region (sales to legal entities for industrial and other special uses and purposes) per 100,000 residents_ _<u>(all Russian regions for which this data is available)</u>_

||Model 3.2.1|Model 3.2.2|Model 3.2.3|
|---|---|---|---|
|Constant|-11.53<br>(9.98)|-15.81*<br>(9.957)|-15.00<br>(12.03)|
|Administrative land pricing factor in<br>the regional capital|-0.37***<br>(0.12)|-0.38***<br>(0.12)|-0.44***<br>(0.14)|
|Ln regional GDP per capita|1.91**<br>(0.89)|2.42***<br>(0.86)|2.38**<br>(1.08)|
|Distance from the regional capital to<br>Moscow City (in thousand km)<br>||-0.59***<br>(0.20)|-0.41*<br>(0.23)|
|Ratio of per m<sup>2</sup>prices of land sold to<br>legal entities for industrial and other<br>special use to land sold to legal entities<br>for other purposes|||0.36**<br>(0.16)|
|Adj. R<sup>2</sup>|0.124|0.213|0.277|
|N|73|73|55|
|Mean of the dependent variable|6.43|6.43|7.63|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

For the surveyed regions, this can be illustrated with the example of Novosibirsk region where the land price factor equals 17 - the maximum allowed by law and the reported number of land sales is reported at 4.31 per 100,000 or regional population. If the Novosibirsk authorities were to reduce their land price factor by half to 9, then, other things being equal, their land sales would more than double to 8.7 transactions per 100,000 residents. If they were to reduce the price factor to the minimum allowed by the law for cities with a population of over 500,000 residents, i.e. to a factor of five, then the number of privatizations of municipal land would be expected to reach about 11 per 100,000 residents.

Chart 3.1 shows how many more land privatization transactions could potentially be achieved in the surveyed regions by reducing land price policy factor to the minimum allowed by law.

Another key factor influencing the number of land privatization transactions in regions is the duration of land privatization procedures. The longer the duration, the lower the number of land privatization transactions occurring in any given region (see Chart 3.2 below). Unfortunately, official Russian statistics do not collect information on procedure duration. Therefore, the following example is based on the information on procedure duration collected in the 15 regions surveyed by FIAS. A decrease in the average procedure duration by one month (30 days) or by approximately 14%, other things being equal, increases the number of land privatization transactions per 100,000 residents by about 11%.

15
Policy Research Working Paper 8523

# **Abstract**

Evening-hour luminosity observed using satellites is a good proxy for economic activity. The strengths of measuring economic activity using nightlight measurements include that the data capture informal activity, are available in near real-time, are cheap to obtain, and can be used to conduct very spatially granular analysis. This paper presents a measure of monthly economic activity at the district level based on cleaned Visible Infrared Imaging Radiometer Suite nightlight and rural population. The paper demonstrates that this new method can shed light on recent episodes in South Asia: first, the 2015 earthquake in Nepal; second, demonetization in India; and, third, violent conflict outbreaks in Afghanistan.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at rcmbeyer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Rigorous evidence of the effect of conditional cash transfers (to parents) on test scores is limited. This evidence is generally based on school data and thus potentially affected by selection into school attendance at the time of the test (Saavedra, 2016). Most studies find no evidence of test scores gains. The two exceptions that we are aware of are Barham, Macours and Maluccio (2016), who find positive effects for boys in Nicaragua, and Baird et al. (2011) who find positive effects for girls in Malawi. In columns (3) and (6), we provide estimates based on test scores at a math (ASER) test administered to eligible girls in our endline household survey, irrespective of attendance at school, which are therefore not affected by the type of selection bias which may undermine test scores effect estimates from CCTs based on school tests. Similar to most previous evidence, we find that gains in attendance from cash incentives to parents do not translate into gains in test scores. On the contrary, both the information treatment and the girls' incentives treatment improve math scores by 8.5 to 9.4% of the control group’s mean.

While the girls’ incentive treatment has a larger effect on attendance than the information treatment, the effect of both treatments on test scores is of similar magnitude and statistically significantly larger than in the parent CCT arm. It could be that part of the effect on test scores of the information treatment stems from something else than more regular school attendance. Prompted by the realization of their imperfect attendance monitoring technology, parents in the information treatment may plausibly increase their monitoring of other aspects of schooling effort such as homework, and do so more in the information only treatment where imperfect monitoring may at first be more salient than in the transfers treatments.<sup>24</sup> Or the similar magnitude of the effects on math scores 24 It may indeed take parents more time to realize the informational content of the attendance report in the transfers arms, where it fullfils the additional function of allowing verification of the condition, than in the information only arm. Consistent with this, the effect of the information treatment has a flatter 27
Table A.3.

||**All Refugees**|**Refugees with RAMV**|**Refugees with PEP**|
|---|---|---|---|
|**Education**|Nursery, pr. and sec.<br>Food and school bus|Nursery, pr. and sec.<br>Food and school bus|Nursery, pr. and sec.<br>Food and school bus|
||No|No|Promotion across levels|
||No|No|Degree recognition|
|**SISBEN**|No|No|Yes|
|**Health**|Emergency care<br>Public health programs<br>Vaccines|Emergency care<br>Public health programs<br>Vaccines|Emergency care<br>Public health programs<br>Vaccines|
||Prenatal care|Prenatal care|Prenatal care|
||Prevention campaigns|Prevention campaigns|Prevention campaigns|
||No|No|Subsidized regime|
|**ICBF****|No|No|Childcare|
||No|No|Earlychildhood service|
|**Formal Labor**|No|No|Jobpermit|
|**Financial Services**|No|No|bankingaccess|

_Source:_ Ib ́a ̃nez et al. (2024). *SISBEN: score used to target social safety net programs in Colombia, and ** ICBF: Colombian Family Welfare Institute.

58
Kooreman, P. 2000 “The Labeling Effect of a Child Benefit System,” _American Economic Review_ . 90 (3): 571-583.

Levinger, B., 1986 “School Feeding Programs in Developing Countries: An Analysis of Actual and Potential Impact,” Aid Evaluation Special Study 30, U.S. Agency for International Development.

Pollit, E.. 1995. “Does Breakfast Make a Difference in School _?” Journal of the American Dietetic Association_ . 95 (10): 1134-1139.

Ray, R. 2000. “Child Labor, Child Schooling, and Their Interaction with Adult Labor: Empirical Evidence for Peru and Pakistan,” _World Bank Economic Review_ , 14: 347-367.

Ross, T., 1991 “On the Relative Efficiency of Cash Transfers and Subsidies,” _Economic Inquiry_ . 29(3): 485-496.

Schultz, P. T. 2004 “School Subsidies for the Poor: Evaluating the Mexican Progresa Poverty Program,” _Journal of Development Economics_ . 74(1):199-250.

UNESCO. 2007 “Global Education Digest 2007: Comparing Education Statistics Across the World,” technical report, UNESCO Institute for Statistics, Montreal: Quebec, Canada.

WHO Multicentre Growth Reference Study Group. 2006. “WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development,” technical report, World Health Organization, Geneva.

Yamauchi, F., 2008. “Early Childhood Nutrition, Schooling and Sibling Inequality in a Dynamic Context: Evidence from South Africa,” _Economic Development and Cultural Change_ . 56: 657-682.

Zoungrana, A., B. Sawadogo, S. Zerbo, I. Tagnan, N. Terpend, S. Sanon, and D. Michiels. 1999. “Plan National d’Organisation et de Coordination des Secours d’Urgence et de Réhabilitation: Volet Sécurité Alimentaire,” Rapport Technique, Comité National de Secours d’Urgence et de Réhabilitation, Ouagadougou, Burkina Faso.

22
summarizes the distributional effect of income growth by plotting the cumulative share of the population ranked in ascending order of income against the income growth rate of the _n_ th percentile of the distribution. The latter is calculated by comparing the _n_ th percentile of the initial distribution in 2005 to the one of the final distribution in 2030; hence, when interpreting an upward sloping GIC, it can be inferred that lower percentiles grew slower than upper percentiles and thus that inequality rises. It is important not to conclude that individuals who were at the bottom end of the distribution in 2005 benefited less than those at the higher end of the distribution. This is because an individual, who was in the 10th percentile in 2005, may find himself wealthier in 2030 and in the 55th percentile<sup>17</sup> .

In Brazil, projected growth has significant un-equalizing effects: incomes of the top two deciles in 2005 will grow by more than 80% while the earnings of the bottom two deciles will rise by only less than 70%. In Peru, the opposite happens: the bottom 20 percent will experience an expansion of incomes at a rate above 180%, while earnings of the top 20% will increase by less than 80%.

In each country, income distribution is affected by two set of factors: shifts in the demographic structure of the population, in terms of ageing and education attainment, and changes in rewards for individuals’ characteristics, such as their education level and sector of employment. Although in reality these demographic and economic shocks occur simultaneously and jointly determine inequality changes.<sup>18</sup> A statistical decomposition can be performed to infer how much of the total change can be attributed to each shock.

The demographic structure of the world in 2030 is based on the UN’s population projections by age group and a simple model of human capital accumulation that assumes a continuation of the educational trends observed over the 1980–2000 period. Controlling for other factors, both the level and dispersion (inequality) of household income tend to increase with the age and education of the household head. Therefore as the population ages and becomes more educated, the population shares of groups with higher income inequality rise, and one may thus expect to see higher inequality. Figure 14 shows how the Growth Incidence Curve would look like in Brazil and Peru if only the demographic structure were to change over the next decades, with no economic adjustment other than growth in the mean income. As the upward sloping curve indicates, it clearly appears that in both Peru and Brazil, as well as in most of the other countries in LAC, changes in demographic structure work to increase income inequality. As shares of older

> 17 The only exception to this would be if panel data were used to construct the growth incidence curve.

> 18 This method suffers from path-dependence, but there is no clear better alternative.

30
_Appendix Table A.4. Exports, Employment Coefficients and Proportion of Production Workers By VSIC 2-digit Level_

|VSIC categories<sup>a</sup>|Exports<br>Value 2004<br>($ million)|Share in<br>Total<br>Exports<br>2004<br>(%)|Employment<br>coefficient<br>2004<sup>b</sup><br>(person/bn dong)|Share of<br>production<br>workers<br>1998<br> <br>(%)|
|---|---|---|---|---|
|Crude petroleum (11)|5671|21.4|.1|88.9|
|Apparel (18)|3633|13.7|6.7|88.5|
|Food and beverages (15)|3492|13.2|2.8|81.7|
|Footwear and leather products (19)|3030|11.4|7.9|92.3|
|Furniture and manufacturing n.e.s. (36)|1276|4.8|9.1|88.7|
|Textiles (17)|1257|4.7|6.8|89.7|
|Electronics (31)|771|2.9|2.3|79.3|
|Office machinery (30)|655|2.5|.6|79.8|
|Radio, TV and communication machinery (32)|537|2.0|1.8|74.7|
|Rubber and plastics products (25)|454|1.7|3.5|81.0|
|Mining of hard coal lignite and peat (10)|354|1.3|6.5|86.9|
|Wood products (20)|320|1.2|10.4|85.7|
|Chemical products (24)|317|1.2|1.9|73.2|
|Non-metallic mineral products (26)|291|1.1|5.0|84.8|
|Transport equip. other than motor vehicle (35)|289|1.1|2.1|75.7|
|Metal products (28)|265|1.0|4.1|78.6|
|Machinery and equipment (29)|237|.9|4.1|74.3|
|Basic metals (27)|212|.8|1.4|85.6|
|Coke and refined petroleum products (23)|209|.8|.6|30.8|
|Tobacco products (16)|128|.5|1.1|85.3|
|Mining of metal ores (13)|120|.5|9.4|81.4|
|Motor vehicles (34)|102|.4|1.3|70.3|
|Medical, optical instruments, watches and clocks (33)|98|.4|4.8|78.1|
|Paper and paper products (21)|80|.3|4.2|81.7|
|Publishing and printing (22)|36|.1|4.0|72.2|
|Other mining and quarrying (14)|34|.1|11.8|92.7|
|Recycling (37)|0|.0|5.1|89.4|
|Electricity, gas, steam and hot water (40)|0|.0|2.4|54.3|
|Collection, purification and distribution of water (41)|0|.0|7.5|78.0|
|Exports other than industrial exports|2618|9.9|||
|Total|26485|100.0|||

_Sources_ : the _Enterprise Survey_ Data 2004; Jenkins (2004); the UN Comtrade System. _Notes_ :<sup>a</sup> Between parentheses are the VSIC codes at the two digit level.

b Employment coefficient reflects the number of workers needed to produce one billion dong worth of goods. Following Jenkins (2004), output for apparel (18) and footwear and leather products (19) are multiplied by 2.5 and by 2 respectively, assuming that the gross output recorded in the _Enterprise Survey_ data refers not to the value of the products but to the processing costs. Without this adjustment, the employment coefficients for apparel (18) and footwear and leather products (19) are 13.7 and 11.4 respectively.

49
Table 4: Sectoral Linkages Channel: Impact of tradable services on employment in nontradable services

||(1)<br>Input <br>Low|(2)<br> to<br>High|(3)<br>Input <br>Low|(4)<br> from<br>High|
|---|---|---|---|---|
|Log tradable services|0.0711**|0.0118|0.0574*|0.0450|
||(0.0314)<br>|(0.0212)|(0.0331)|(0.0318)|
|Observations|1,173|1,173|1,173|1,173|
|District FE|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|

*** ** * Standard errors (in parentheses) are clustered at the district level. _p <_ 0.01, _p <_ 0.05, _p <_ 0.1. Column 1 includes district-level employment in health, education, accommodation and food services, legal and accounting activities. Column 2 includes district-level employment in veterinary, repair, residential care, wholesale and retail, transport, postal, finance, real estate activities and rental leasing, employment activities, and travel agency. Column 3 includes district-level employment in health, education, real estate activities and rental leasing services, and legal and accounting activities. Finally, column 4 includes districtlevel employment in veterinary, repair, personal and residential care, wholesale and retail, transport, postal, finance, real estate activities and rental leasing, employment activities, and travel agency, and accommodation and food services.

ducers. In our case, consumer services following Fan et al. (2021) correspond to: (i) retail trade, except of motor vehicles and motorcycles, (ii) personal services, (iii) human health, (iv) residential care, and (v) accommodation and food services, which were largely drivers of the baseline results. We consider all other non-tradable services to be non-consumer services.

As shown in Table 5, the effects of tradable services on non-tradables are indeed driven by consumer services. The coefficients on employment and firms are statistically significant, with coefficients of 0.51 and 0.39 respectively. Conversely, the effects on non-consumer services are smaller and insignificant. As consumer services tend to not have input-output linkages with tradable services, this provides additional suggestive evidence for the consumer demand channel.

15
outset of the railway age.9 Though even in Europe only incomplete information was available regarding economic conditions, the problem was most severn in overseas regions of recmt Eiuropean setement. For regions like the North Ameican West that had onily rcentdy appeared on maps, not even geography could be tkn for granted: one writer recalls a dinner in London where a British investor asked an American visitor whether Cincinnati or Ohio was the' larger city.

The amount of traffic a railway would attact was contngent on the economic development of the adjoining region, which depended on inponderables such as the fertility of the soil, the reliability of rainall, and the exotent of mineral reserves. -Want of local knowledge' was cited in 1852 by the chairman of the Madras Company as an obstacle to atacting exnal finance for railroad building in India.'° Wher the volume of traffic ultimately depended on fte ectent of mineral deposits, uncertainty about reserves posed a fomidable risk; North Ameican railways were built or not built on the basis of crude forects of coal or silver deposits. Where land had to be setded, dleared, and irrigated before it could be fanned, many years might be required before the picture was claified. Cnadian railway building peaked in the final decades of the 19th century but significanty stimulated wheat production (and rail traffic) only in the second decade of the 20th century.?' Even where setment was no at isue, years might have to pass before ' It war mimilarly problematic to extrpolte from expeienc with gas-hintinglmp to detemine the costs ad benefits of eleCtifiCAtioc.

0ocpherm (1955, Lp1SO).

A-nkli (1980, p.260 _adptwim)._ 9
- **_Treat households as freight attractor nodes in mobility surveys_** . Freight transport studies usually focus on studying trip generation and attraction by production, logistics and sales nodes, including only retail as the final destination point and without considering households as a point of attraction for freight trips. Household mobility surveys can include basic questions about online shopping habits without significantly increasing the response time.

- **_Manage e-commerce as a mobility-generating challenge in the short-to-medium term_** . The existing pressure on mobility planning to find ways to reduce externalities in the transport sector means that “virtualization” of activities is routinely seen as a powerful tool to reduce mobility demand (Pettersson et al. 2018). In the case of e-commerce, international evidence and the results of the analysis conducted this study suggest that this is not the case; as of today there is no substitution effect of shopping trips that compensates for the increase in freight vehicle traffic. In this sense, e-commerce differs from the effects of other virtualization processes (telework, online leisure, etc.) in which there may be more opportunity to reduce mobility demand without generating other types of trips. In any case, the effect of each social trend on mobility demand has to be carefully analyzed prior to pinning hopes on it in terms of mobility reduction. For example, the available evidence on the effects of teleworking suggests complementarity effects with other travel purposes and increases in distances traveled (Ravalet and Rerat 2019).

- **_Focus on the optimization of distribution routes and the electrification of fleets_** . The optimization of distribution routes is the measure that can most effectively mitigate the impacts of e-commerce on mobility, as it can reduce the number of kilometers traveled by freight vehicles in the city, with all that it entails in terms of reducing emissions, mitigating road accidents associated with this type of vehicles and containing congestion. The co-creation of more sustainable alternatives for distribution together with the sector stakeholders must be a priority for the transport authorities. This optimization includes the implementation of freight consolidation nodes and alternative delivery mechanisms (e.g., automatic ticketing machines at transport interchanges). After the optimization of distribution routes, fleet electrification is the second measure with the highest return, as it has a direct impact on emissions from e-commerce-generated freight vehicle traffic. Transport authorities can incentivize this electrification by segmenting traffic requirements and restrictions according to vehicle profile.  Finally, the trend of reducing order size and delivery time increases the generation of freight vehicle trips associated with e-commerce. Potential measures to reduce the number of freight vehicle miles traveled include, among others, urban consolidation centers, proximity locker delivery, etc.

14