rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 5: Simulation Results Using SD Approach and World Bank Measure of GW Volume

||GW<br>Depth|Extraction<br>Recharge<br>Ratio|Aquifer<br>Productivity|Grids<br>Satisfying<br>Constraints|Grids with<br>Production|Median<br>Gains in<br>Production|Mean<br>Gains in<br>Production|
|---|---|---|---|---|---|---|---|
|Crop|Constraint|Constraint|Constraint|(%)|Gains (%)|(%)|(%)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Barley|7_._00|0_._70|0_._05|5_._47|2_._75|29_._57|101_._40|
|Barley|7_._00|0_._70||5_._47|2_._75|29_._57|101_._40|
|Barley|7_._00||0_._05|5_._48|2_._74|29_._57|101_._40|
|Barley||0_._70|0_._05|20_._98|2_._94|47_._91|98_._42|
|Barley|7_._00|||5_._48|2_._74|29_._57|101_._40|
|Barley||0_._70||20_._98|2_._95|47_._96|98_._47|
|Barley|||0_._05|24_._91|2_._48|47_._91|98_._42|
|Barley||||24_._91|2_._48|47_._96|98_._47|
|Millet
.349]||[2.290]|[2.370]|[2.383]|
|Married|||-0.244|-0.250|||0.664|0.716|
||||[1.227]|[1.231]|||[0.894]|[0.915]|
|Rural||||-2.571||||-0.505|
|||||[3.088]||||[2.705]|
|Observations|13,444|12,759|12,753|12,753|2,714|2,672|2,671|2,671|
|R-squared|0.570|0.577|0.577|0.577|0.661|0.663|0.663|0.663|
|Individual fixed effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Year fixed effect|YES|YES|YES|YES|YES|YES|YES|YES|
|Region dummies|NO|NO|NO|YES|NO|NO|NO|YES|
|Dependent variable mean|5.877|5.958|5.959|5.959|6.504|6.553|6.553|6.553|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression, relying on panel data estimation. The analysis is restricted to working age individuals, conditional on being employed. Employed individuals are defined according to the market definition of labor force participation. In columns (1) to (4), the analysis is restricted to the sample of males. In columns
50(4): 987–1008.

- Erdem, F.P. and I. Ünalmıs (2016). “Revisiting Super-Cycles in Commodity Crises.” _Central Bank Review_ , 16: 137-142.

- Erten, B. and J.A. Ocampo (2021). “The Future of Commodity Prices and the Pandemicdriven Global Recession: Evidence from 150 Years of Data.” _World Development_ , 137: 105164.

- Erten, B. and J.A. Ocampo (2013). “Super Cycles of Commodity Prices since the Midnineteenth Century.” _World Development_ , 44: 14-30.

- Fernández, A., A. González, and D. Rodriguez (2018). “Sharing a Ride on the Commodities Roller Coaster: Common Factors in Business Cycles of Emerging Economies.” _Journal of International Economics_ , 111: 99-121.

- Fernández, A., S. Schmitt-Grohé, and M. Uribe (2017). “World Shocks, World Prices, and Business Cycles: An Empirical Investigation.” _Journal of International Economics_ , 108: S2-S14.

- Fernández, A., S. Schmitt-Grohé, and M. Uribe (2020). “Does the Commodity Super Cycle Matter?” NBER Working Paper 27589. Cambridge, MA: National Bureau of Economic Research.

- Galbraith, J.K. and J.D. Black (1938). “The Maintenance of Agricultural Production During Depression: The Explanations Reviewed.” _Journal of Political Economy_ , 46(3): 305–

_— 29 —_
sector has been tremendous and has resulted in widespread resistance to the implementation of housing reforms. Budget subsidies to the housing sector still consitute almost _25%_ of local fiscal expenditure, or three-quarters of the cost of the provision of the housing and associated services. Regional and municipal governments have made very uneven progress in the implementation of such federal housing policy guidelines as have been enacted over the years and, as a rule, have not been capable of making real breakthroughs with institutional reform of the housing sector. This is particularly the case in such areas as the introduction of competitive maintenance firms (to break through the inefficiency associated with traditional monopoly suppliers of these services); the creation of condominium associations (to facilitate the transfer of housing ownership from municipalities ill equipped to deal with large holdings of the housing stock); and strengthening utility regulation and supervision.

Still, given the overall fragility of the Russian reform process, Russia has been able to accumulate substantial experience in reforming its housing sector 4. Starting from late 1993, a number of key pieces of a regulatory framework have been approved, cost recovery has increased from less than 2% to about

**1 1** would also like to express my gratitude to Raymond Struyk of the Urban Institute, Washington, with whom I had the good fortune to work during the past five years in the course of conducting housing surveys in Russia. I am especially grateful to my sociologist colleagues Ludmnila Vatfa, Alexander Temnitsky, Irina Kiseleva ofthe Institute of Sociology, Russian Academy of Sciences (RAS); Nadezhda Nozdrina of the Institute of Economic Forecasting, RAS; Gennady Diachenko of the Institute of Physics, RAS; Vera Gromova of InfiDemo, Vladimir, Yuri Gapienkov of Institute of Sociology, RAS, Nizhny Novgorod; and Natalia lagodunskaya and Olga Kuzina. I would also like to thank Natalia Veligura of the Moscow Resident Mission of the World Bank, translator Natalia Belskaya and editor Michael Haney.

2 The term "housing stock" as used in this paper in reference to privatization will
oil consumption would be less than expected as a result of a “rebound” in oil use as real price of oil decreases. The magnitude of the rebound effect can vary from “backfire” (also known as the Jevons paradox), where energy use increases following an improvement in energy efficiency, to super-conservation, where energy consumption declines more than the improvement in energy efficiency (Bruns et al., 2021). Thus, the sign restrictions imposed here are sufficiently flexible to capture the full spectrum of effects in the period following impact. However, at impact, a backfire effect is ruled out by assumption, which is not problematic given that overall existing evidence provides little support for the backfire hypothesis (see Gillingham, Kotchen, Rapson, & Wagner, 2013; Gillingham, Rapson, & Wagner, 2015).

Table 2 provides the sign restrictions imposed on the set of VAR models augmented with country GDP data. The sign restrictions only differ in terms of how country output responds to oil demand and oil supply shocks across oil exporting and importing countries. In particular, in response to a positive oil supply shock, oil exporters’ GDP declines and oil importers’ GDP increases as the real price of oil falls. Similarly, in response to an unanticipated improvement in U.S. energy efficiency, oil demand decreases and, consequently, the real price oil falls. This results in a decrease (increase) in oil exporter’s (importer’s) GDP, whereas, in response to a positive precautionary oil demand shock, oil exporters’ GDP declines and oil importers’ GDP increases as the real price of oil increases. A positive aggregate economic activity shock increases GDP across all countries.

**_Elasticity Bound on Impact Price Elasticity of Oil Supply._** A fundamental drawback of the VAR model identified based on sign restrictions is that it does not provide point estimates

14
to the total benefit, as yielded by the model, will then yield an estimate of the total benefit that has a substantial empirical anchoring.

The object of this paper is to estimate the _relative_ size of the contribution of reduced transport costs, in connection with the all-India rural roads program known as _Pradhan Mantri Gram Sadak Yojana_ (PMGSY), a very large scheme that will eventually cover some 170,000 habitations, take at least 20 years to implement and cost in excess of US$40 billion (World Bank, 2010). The unified framework employed for this purpose is the overlapping generations (OLG) model of Bell, Bruhns and Gersbach (2006), substantially extended so as to cover transport costs and morbidity. Important elements of the empirical basis for this application are supplied by an analysis of a survey of – – some 240 households drawn from 30 villages lying in the upland and poor region of Orissa (Bell and van Dillen, 2012) and two large surveys in Bangladesh (Khandker _et al_ ., 2009). There is also an indirect check, in the form of completely independent estimates of the so-called value of a statistical life provided by Lui _et al._ (1997) and Simon _et al._ (1999).

The plan of the paper is as follows. The model is set out and analyzed in Section 2, the essential difficulty with valuing the benefits arising from effects other than those of lower transport costs being addressed in Section 2.3. The numerical set-up follows in Section 3, which is divided up into subsections dealing with functional forms, parameter values and calibration under perfect foresight. This is the basis for the exact welfare analysis in Section 4, treating in sequence the benchmark of ‘no-road’, the world with the road and the contribution of lower transport costs to the whole resulting benefit. The conclusions are drawn together in Section 5.

# **2 The Model**

The basis is the model of Bell, Bruhns and Gersbach (2006),
, spending on water, and the use value associated with household durables. The setting is Vietnam in the 1990s. Vietnam’s social health insurance program, or Vietnam Health Insurance (VHI) as it was known at the time, was set up in 1993 in response to a growing concern over the scale of out-of-pocket payments for health. In 1993, over 70% of health spending in Vietnam was paid out-of-pocket, and out-of-pocket payments absorbed 6% of the average household’s total consumption (World Bank et al. 2001, Wagstaff 2002). As many as 18% of households spent more than 10% of their total consumption on health care, and as many as 47% of households spent more than 10% of their non-food consumption on health care (Wagstaff and van Doorslaer 2003). In 1993, a single visit to a public hospital by someone from the bottom quintile cost the equivalent of 41% of their _annual_ non-food consumption (World Bank et al. 2001). Unsurprisingly, health care payments were a major source of concern among Vietnamese participants in the global participatory poverty study _Voices of the Poor_ (Narayan et al. 2000).

Given this background, and given its very generous initial coverage (copayments were introduced only in late 1998), VHI can reasonably be expected to have had effects on both medical care use and on the consumption of other budget items, with likely consequences for health outcomes. We estimate insurance impacts through the use of propensity score matching (PSM) on longitudinal data collected just before the introduction of VHI in 1992/93 and five years later in 1997/98.<sup>3</sup> Two-thirds of those joining VHI in this period—and 95% of adults

> 3  The use of PSM and longitudinal data are two features that distinguish the present study from that of Trivedi (2003), who used regression analysis on the 1997/98 cross-section data.  The present study also looks beyond health care use and health care spending.  There
seen from Appendix C. Indicators of poverty levels, as opposed to our indicator of poverty commitment, do no better. The signs are inconsistent across runs and the coefficients are actver significant as can be seen from Tables C8.

The findings of regressions including poverty incidence indicators, as opposed to our poverty commitment indicator, are consistent with those found for the central budget regressions. However, poverty alleviation commitment, PCOM, was found to significantly influence central government per capita infrastructure expenditures, while PCOM does not influience consolidated budget expenditures. It may be that at lower levels of government, investments in infrastructure are not viewed as an effective means to alleviate poverty. Hlowever, it may also be that there is a relationship, but because our review of documents only focused on the central government, the variable PCOM is a poor indicator of the extent of commitment to poverty alleviation at lower levels of government. This issue warrants further exploration.
|859|859|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.255|0.257|0.243|0.245|

Notes: The post-crisis sample includes quarterly panel data for 26 emerging and frontier market economies from Q2 2009 until Q4 2017. All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. QE refers to the episodes of Quantitative Easing by the US Federal Reserve. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

18
grants for the beneficiary households. Transfers were made shortly after the midline survey, and so the Early TASAF treatment group had received funding over a year earlier, while the Late TASAF treatment group received their VG grant just several months before the UCT. The endline survey captures outcomes roughly two years after the transfers were made. To analyze UCT impacts we include as the treatment variable the size of the transfer to a household, in units of hundreds of US dollars (randomized between $50 and $350, with the mean and median transfer amount among UCT recipients equal to roughly $200) and set to zero for households who received no transfer.

Table 6 analyzes impacts of the cash transfers in the ENB stratum, which, given the absence of any other interventions in that population, studies an intervention much like the large cash transfer literature, such as the numerous studies on GiveDirectly transfers in Kenya (Haushofer and Shapiro 2016, Egger et al 2019) and Rwanda (McIntosh and Zeitlin 2022), albeit with substantially smaller transfer amounts. Given the relatively small size of our transfers and the 24month follow-up duration, we do not find any appreciable effects on consumption or livestock value. We do detect a large but noisy increase enterprise assets and income, significant at 90%, indicating an increase of $29 per $100 of UCT, or an average treated impact of $58 over a controlgroup mean of $78.

Given the complex research design among TASAF beneficiaries by R3 and the concerns raised by Muralidharan et al. (2019) over the pooling of arms in factorial designs with multiple cross-cuts, we present the R3 UCT impacts in two distinct ways. In Panel A of Table 7 we follow the same structure as the prior table, pooling all UCT treatment and estimating a single ANCOVA regression for the TASAF beneficiaries. In Panel B we show every cell of the research design separately to avoid conflating the impacts of Early TASAF treatment, training, UCT, and their 28
economic characteristics of the mother and especially the education of the mother are likely to play a very important role. While these features imply that it is imperative to take into account the potential influences of these confounding factors in determining the impact of maternal education on ECD, the DML approach that addresses these issues explicitly reveals that in São Tomé and Príncipe at least, maternal education has a direct effect on ECD. Yet, one more intriguing result from the analysis in São Tomé and Príncipe is that maternal education has no relationship at all with child stature. This result appears to contradict much of the conventional wisdom derived from other countries regarding the effect of maternal education on child nutrition. However, it is important to bear in mind that the conventional wisdom is based more on correlations as opposed to established causal relations (Desai and Alva, 1998). A more recent study by Alderman and Heady (2017), addressing explicitly the problem of omitted variable bias in the relationship between parental education and child nutrition, finds that the nutritional returns to parental education are reduced substantially in models that include cluster fixed effects, which are typically omitted from conventional regression models, thus resulting in omitted variable bias in the estimated coefficients.

In conclusion, country-specific policy advice based on relationships observed from data in other countries may be quite risky, if not misleading. DML offers the opportunity to identify and validate causal relationships with country-specific observational data. Given the constraints faced by randomized control trials to address every possible question, DML provides a practical and feasible approach to reducing threats to internal validity to derive robust inferences based on observational data for evidence-based policy advice. The extent to which the DML estimates are close to the true causal or impact estimates has yet to be established and this can only be ascertained by comparing with causal estimates derived from an RCT. Nevertheless, there is promising evidence that estimates based on the DML represent an improvement over the usual OLS in the sense that they are closer (and sometimes quite close) to the impact estimates from the preferred
same, the value of _τ_ when personal diary format is used is _τ ≈_ 0 _._ 66 _·_ 0 _._ 35 _/_ 4 = 0 _._ 06.

It should be noted that Beegle et al. (2012) only consider a relatively short questionnaire with a complete consumption module and not a large multi-topic survey. Therefore, when we consider a typical Living Standard Measurement Survey, the value of _τ_ may be higher as the weights of non-consumption modules become more important. On the other hand, if we are only concerned with getting an accurate estimate of poverty, _τ_ could be lower because we typically need a small fraction of non-consumption modules to predict consumption.

In Bangladesh, Ahmed et al. (2014) adopt the assumption that five enumerators have to stay for two weeks to complete the survey in one primary sampling unit (PSU) to estimate the cost of conducting Household Income Expenditure Survey in Bangladesh. They further assume that 38
he announced through [social] media, we may not know about it, even if there are people with experience for the job offered.”

> 26 Kadoda and Hale (2015) are careful to note that meaningful social and other divides have existed and continue to exist among the youth who live in greater Khartoum, such as the divide between Riverain youth, or youth who are native to Khartoum, and Darfuri youth who migrated to Khartoum to escape conflict and unemployment.

> 27 World Bank Staff calculations based on NHBPS 2014/15.

44
capital, and concentration in low wage jobs. Furthermore, most of these jobs are in the service sector which potentially could have freed up time, especially for Peruvian women to be more engaged in the labor market, as well as lowered the costs for these types of goods and services. Venezuelans also might have expanded opportunities by increasing the demand for certain goods and services.

One widespread claim mentioned in some media reports is that Venezuelan migration led to an increase in crime (Freier et al., 2021). We test whether this claim is supported by the data in Table 6, where we use administrative information on the number of non-violent and violent crimes reported in each municipality, the personal security index from Gallup, and reports on whether crime is perceived as a major problem in ENAHO. The structure of this table is the same as the previous with Panel C our preferred specification.

Consistent with the idea that Venezuelan inflow lead to labor market conditions improving, we observe that locations that received a larger number of immigrants have lower number of reported non-violent crimes (columns 2). This effect is large with a double of Venezuelans in a province leading to a 42% decline in reported non-violent crimes. Individuals are also less likely to report that crime is a major national problem, this is true for both men and women.

Finally, in Table 7 we examine the effects of immigration on local communities. Consistent with our previous findings, Peruvians living in areas with a higher share of Venezuelan immigrants report that the quality of local services and community quality are higher and that they have a greater trust in their neighbors. However, in these locations they also report that the community is less likely to value diversity. In general, these findings are stronger for men than for women. It is important to note that these findings could be driven by the positive impact that Venezuelans have on the labor market outcomes of Peruvians.

Overall, we find that increased Venezuelan migration leads to improvements in both objective and subjective measures of the
Strata 1-2 Strata 3-4 Strata 5-6 Low Medium High Low Medium High<br>2020 2021 2020 2021<br><!-- End of picture text --> _Note: Negative values indicate that trips to shopping center zones fell more or grew less than to other areas;_ Source: Team’s estimate based on CDR data The results indicate that either there was no shopping trip demand substitution effect from increased e- commerce adoption or this effect was accompanied by other trends that affected mobility to shopping areas to a greater extent:

- A drop in employment in commercial areas, which would have caused the drop in mobility to these areas to be greater among the “compulsory” mobility segment than among the segments that include trips for shopping purposes.

- A reduction in consumption among the lower strata, which would have caused the drop in mobility to shopping areas to be greater among people of lower socioeconomic status, contrary to what would be expected as a result of the adoption of _e-commerce_ .

- A complementarity effect of increase in telework, leading to an increase in shopping trips among the teleworking population and offsetting the possible substitution effect of _e-commerce_ . Most studies on the effects of teleworking on travel demand have found this complementarity effect (e.g., Hook et al. 2020).

In Bogota and especially in Buenos Aires, carrier trips on weekdays originating in areas with e-commerce distribution centers fell less than those originating in other areas, consistent with the overall steep increase in e-commerce (Figure 6). In Bogota, this effect was accentuated in 2021 versus 2020, as the difference in the 11
|0.22|0|0|0|
||7|0|0|0|0|0.03|0.18|0.56|0.23|0|0|
||8|0|0|0|0|0|0.04|0.13|0.6|0.23|0|
||9|0|0|0|0|0|0|0.03|0.14|0.69|0.13|
||10|0|0|0|0|0|0|0|0|0.12|0.87|

_Source_ : Authors calculation based on HEIS 2019 36