rafmacalaba/data-use-mentions-extended huggingface dataset:
and climate monitoring systems to identify threats early. Public officials must also have response plans in place to implement if a crisis occurs. Countries that are most vulnerable will also need to consider climate risks in their urban planning. Cities, such as Tunis, are preparing against the risk of floods by improving drainage, building seawalls, and relocating new infrastructure developments away from high‐risk areas. Countries that are reliant on agriculture and are at high risk of droughts, such as in Sub‐ Saharan Africa, will need to adapt by investing in drought‐resistant crops, irrigation systems, and effective markets that facilitate the adoption of new technologies. Moving away from a rainfall‐dependent agricultural economy will help these countries reduce their exposure to risk. Finally, governments must have access to funds that can be immediately deployed for reconstruction and as safety nets if a disaster strikes. Here, international cooperation is imperative. To assist in this effort, the World Bank has worked with donors and the private sector to develop a Disaster Risk Financing and Insurance (DRFI) facility to quickly fund emergency responses.

**Information systems.** A prerequisite to helping the destitute and vulnerable through insurance is the availability of information systems. These systems take different forms and play different roles depending on a country’s situation. The provision of social assistance requires policy makers to gain a good understanding of poverty in order to identify the poor and the geographic areas where an intervention would help most. This requires periodic data collection through household surveys. In addition, panel surveys are especially helpful for identifying the chronic poor. By shedding light on poverty dynamics and identifying vulnerable, yet non‐poor, populations, they also help design social insurance systems that complement the growth and investment components of the three‐point strategy.  Some forms of social insurance require specialized forms of data. For weather index‐based insurance, for example, historical and disaggregated data on rainfall patterns is crucial for a functional insurance scheme. Last, information systems are crucial for
and a control group that did not – to measure the impacts of CIMO on enterprise and worker performance. Section IV reports the findings for our re-examination of the data, where production functions were estimated that fully exploited the panel (longitudinal) nature of the firm-level data to measure the net impacts of program participation on productivity growth, taking into account potential biases from self-selection into CIMO. The paper concludes in Section V with comments on methodological and policy lessons gleaned from this exercise.

2
specification is used instead of a tobit.<sup>37</sup> Results are very similar.

Figure 7 shows that there were two broad oil price regimes during this period: a relatively flat stretch followed by a steeply rising one. In order to ensure that the results above are not simply driven by the difference between these two regimes, column 6 reports the results of a specification in which two linear splines were fit for each city. The “knot” year, the transition between the two regimes, along with both slopes, were estimated separately for each city to minimize the variance in city-specific residuals. The magnitude of the coefficient of interest is slightly smaller than in the baseline case, but it is still negative, despite the fact that even more temporal variation has been removed.<sup>38</sup> The coefficient in column 4 can be interpreted as a semi-elasticity in the context of the model above. An elasticity of city product with respect to transport costs is in some respects a more intuitive measure, but since _ln_ ( _ptxi_ ) is equal to _ln_ ( _pt_ ) + _ln_ ( _xi_ ), 37This specification uses 5.5 because of the integer nature of the DN data in combination with the censoring at 6 described above.

> 38Note that the standard errors have not been corrected for the first stage estimation of the splines. The key point is that the point estimate is very similar to that in the baseline specification.

16
Figure 4: Share of Students in the Different Categories of the IHA <!-- Start of picture text --> 45,68<br>23,38 24,26<br>5,33<br>1,35<br>0 1 2 3 4<br>Poverty index<br>Share of sample (%)<br><!-- End of picture text --> To test for heterogenous impacts by socio-economic status the evaluation sample was divided in two groups according the value of the IHA: those with a value of the IHA of 2 or less and the rest. Table 4 shows the effects of PROBEMS on graduation rates, by socioeconomic status in a specification with and without controls. The effects of PROBEMS are zero, even within the group of more marginalized individuals (IHA of 2 or below). As a complementary exercise we estimate the effects of PROBEMS among individuals with a value of the IHA of 0 or 1 (the most marginalized) and the results are shown in Table A6 in the Appendix. For students with a value of the IHA of zero or one, in the specification without controls, being awarded a scholarship increases the probability of graduation by 8 percentage points (p-value of 0.06), on a mean value of 60% among the control group. However, when the controls are included in the specification, the positive effect remains but is no longer statistically significant (p-value of 0.104). The lack of statistically significant effects among the group of poorest students could be explained by the relatively small number of students with a value of the IHA of 0 or 1 (483). We also test for heterogenous effects on test scores by different levels of IHA, splitting the sample as indicated in Table 4 and found very small and statistically insignificant effects.<sup>11</sup> To identify how well targeted PROBEMS was, we use the Mexican national household

> 11The results are available from the
Yagan. 2010. “How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project STAR.” _NBER Working Paper 16381_ , National Bureau of Economic Research, Cambridge, MA

- Dang, H-Anh and Lanjouw, P. (2013). “Measuring Poverty Dynamics with Synthetic Panels Based on Cross-Sections”, Policy Research Working Paper Series 6504. The World Bank, Washington DC

- Dang, H-Anh, P. Lanjouw, J. Luoto and D. McKenzie (2014). “Using Repeated Cross-Sections to Explore Movements In and Out of Poverty”. _Journal of Development Economics_ . 107(6) 112-128

- Easterly, William (2001). “The Middle Class Consensu and Economic Development”, _Journal of Economic Growth_ . 6(4): 317-35

- Ferreira, Francisco H. G., Julian Messina, Jamele Rigolini, Luis-Felipe Lopez-Calva, Maria Ana Lugo and Renos Vakis. (2013). “ _Economic Mobility and the Rise of the Latin American Middle Class_ ” World Bank, Washington DC

20
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of the distribution of profits. SL equals sanitation loan arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p- values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. Amounts are in Indian Rupees (1 USD = Rs. 67.5). Data source: household survey.

Unfortunately, our data does not allow us to get a detailed enough picture on consumption expenditures over the study period. This could be the missing piece in the puzzle, given that existing evidence suggests that a significant proportion of micro-finance loans are used for consumption purposes (referencing the AEJ Applied studies) and households might also rely on micro-finance and informal borrowing sources to fund unexpected consumption expenditures following unanticipated shocks (Udry, 1994; Besley, 1995). In line, Table 10 suggested a switch away from these informal sources. The only data we have available is information on food and non-food expenditures (reported as two aggregated numbers) the week prior to the endline survey, rather than when the loans were taken. For completeness, we show impacts estimates on these outcomes in Table 14, showing impacts on levels with and without excluding the top 1% of the distribution.<sup>53</sup> We do not find any significant impacts of the intervention on these outcomes. Impacts on non-food expenditures in the week prior to the endline survey are significantly negative at the 10% significance level. This does however not survive multiple hypothesis testing. The insignificant average impact might be driven by the time period the survey covers when asking about consumption expenditures, measurement error, or, there may be significant impacts on these outcomes for specific
62,844.5|0.7%|0.3%|no|||
|Health care|662,987.1|7.0%|2.8%|yes|241,200|36%|
|Other expenditures|5,414,257.9|56.9%|22.7%|no|||
|Indirect transfers|||||||
|Off‐budget expenditures|||||||
|Mortgage Subsidy||||yes|279,600||
|ElectricitySubsidy||||yes|32,800||
|HeatingSubsidy||||yes|1,140||
|Sources: National Statistics Office of Mongolia(2017)._Mongolia Statistical Yearb_<br>Notes:|_ook 2016, _and tabula|ted data from MinistryEd|ucation,Culture|and Science and M|inistryof Health||
|(1) Total transfers accruingto social securityco<br>|ntributions and one t<br>|ime contributions for retir<br>|ement(from 20<br>|16 Yearbook,table<br>|15.6.1)<br>||
|(2) We include both contributoryand non‐cotr<br>(3)Total transfers accruingto social welfare co|ibutory pensions here<br>ntributions(from 201|,because the Hosuehold<br>6 Yearbooktable 15.6.1)|Surveyfor 2016|does not separate|this two.||
|<br>(4) Total education expenditure and distributio|<br>n byeducation level b|, <br> yWorld Bank staff using|data from Minist|ryof Finance.(tot|al is slightlydiffere|nt to data from Op.Cit.|

27 MONGOLIA Distributional impact of taxes and transfers / November 2018
the central bank’s task of stabilizing inflation and makes exchange rate movements a more effective buffer against external shocks.

The contribution of this paper to the literature is threefold. First, it utilizes a rich set of results to shed new light on the heterogeneity of pass-through estimates across countries and over time.

Second, this paper supplements a burgeoning empirical literature linking exchange rate passthrough to underlying shocks in a structural vector autoregression (SVAR) model framework. This contrasts with traditional reduced-form approaches that estimate “average” pass-throughs based on conditioning variables. The estimation of shock-specific pass-throughs refines the analysis of factors affecting the link between exchange rate movements and inflation.

Third, compared to the few preceding studies that have derived state-dependent estimates of ERPTRs (Forbes, Hjortsoe, and Nenova 2017, 2018; Shambaugh 2008), this paper investigates additional shocks and uses a larger sample of countries. It looks at the impact of three domestic shocks (monetary policy, demand, and supply), three global shocks (demand, supply, and oil price), and a residual shock capturing, among other factors, changing risk premiums. A unique FAVAR framework combining global and domestic developments allows identification of these different shocks in a unified setup. Moreover, the identification strategy uses an efficient algorithm to combine sign and zero restrictions, preserving a certain level of agnosticism (Arias, Rubio-

> 1 Defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change to three domestic shocks (monetary policy, domestic demand, and domestic supply), three global shocks (global demand, global supply, and oil prices), and one residual shock (risk premium).

3
66 Holland, 1990, pp. 3-62.

- Pagano, Marco. "Financial Markets and Growth: An Overview," European Economic Review, April 1993, 32(2-3), pp. 613-22.

- Park, Yun Chul. "The Role of Finance in Economic Development in South Korea and Taiwan," in Financial and Development: Issues and Experience, ed. Alberto Giovannini, Cambridg: Cambridge University Press, 1993, pp. 121-50.

- Patrick, Hugh. "Financial Development and Economic Growth in Underdeveloped Countries," Economic Development Cultural Change, January 1966, 14(2), pp. 174-89.

- Patrick, Hugh T, and Park, Yung Chul. Financial Development in Japan, Korea and Taiwan. New York: Oxford University Press, 1994.

- Petersen, M.A. and Rajan, Raghuram G. "The Benefits of Lending Relationships: Evidence from Small Business Data," Joumal of Finance March 1994,42(1), pp. 3-37.

- Pollard, Sidney and Ziegler, Dieter. "Banking and Industralization: Rondo Cameron Twenty Years On," in ed. Youssef Cassis, Finance and Financiers in European History 18801960 New York: Cambridge University Press, 1992, pp. 17-38.

- Pozdena, Randal J. and Volbert, Alexander. "Bank Structure in West Germany" in Ranking Structures in Mraor Countries, ed. George Kaufman, Boston: Kluwer, 1992, pp. 555-90.

- Rajan, Raghuram G. "Insiders and Outsiders: The Choice Between Informed and Armslength Debt," Journal of Finance September 1992,42(4), pp. 1367-1400.

- Rajan, Raghuram G. and Zingales, Luigi. "Financial Dependence and Growth," University of Chicago mimeo, May 1996.

- Rebelo, Sergio T. "Long-Run Policy Analysis and Long-Run Growth," Journal of Political Economics, June 1991, 92(3), pp. 500-21.

- Robinson, Joan. "The Generalization of the General Theory
.|0.781|0.017|9541|Senegal|0.820|0.054|838|
|Jordan|0.769|0.032|4118|Sierra Leone|n.a|n.a|n.a|
|||||South Africa|0.877|0.054|1325|
|South Asia||||Tanzania|0.581|0.034|1617|
|Afghanistan|n.a|n.a|n.a|Togo|0.832|0.069|1130|
|Bangladesh|0.854|0.015|4275|Uganda|0.784|0.041|876|
|India|0.860|0.009|13288|Zambia|0.726|0.028|1710|
|Nepal|0.680|0.024|1816|Zimbabwe|0.688|0.046|1150|
|Pakistan|0.823|0.017|6202|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1970s birth cohort sample.

43
Monteiro, Carlos A, Geoffrey Cannon, Renata B Levy, Jean-Claude Moubarac, Maria LC Louzada, Fernanda Rauber, Neha Khandpur, et al. 2019. “Ultra-Processed Foods: What They Are and How to Identify Them.” _Public Health Nutrition_ 22 (5): 936–41. https://doi.org/10.1017/S1368980018003762.

- Monteiro, Carlos Augusto, Geoffrey Cannon, Mark Lawrence, Maria Laura Costa Louzada, and Pricilla Pereira Machado. 2019. _Ultra-Processed Foods, Diet Quality, and Health Using the NOVA Classification System_ . _Fao_ . Rome: FAO. https://www.fao.org/3/ca5644en/ca5644en.pdf.

Moradi, Sajjad, Mohammad ali Hojjati Kermani, Reza Bagheri, Hamed Mohammadi, Ahmad Jayedi, Melissa M. Lane, Omid Asbaghi, Sanaz Mehrabani, and Katsuhiko Suzuki. 2021. “Ultra-Processed Food Consumption and Adult Diabetes Risk: A Systematic Review and Dose-Response Meta-Analysis.” _Nutrients_ 13 (12): 4410. https://doi.org/10.3390/nu13124410.

- Murray, Christopher J L, Aleksandr Y Aravkin, Peng Zheng, Cristiana Abbafati, Kaja M Abbas, Mohsen Abbasi-Kangevari, Foad Abd-Allah, et al. 2020. “Global Burden of 87 Risk Factors in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the Global Burden of Disease Study 2019.” _The Lancet_ 396 (10258): 1223–49. https://doi.org/10.1016/S0140-6736(20)30752-2.

- Mwangome, Martha, and Andrew M. Prentice. 2019. “Tackling the Triple Threats of Childhood Malnutrition.” _BMC Medicine_ 17 (1): 210. https://doi.org/10.1186/s12916-019-1464-9.

- NCD Child. 2023. NCD Global Burden of Disease Dashboard. <u>https://www.ncdchild.org/global-burdens-ofdisease-dashboard/</u>

- Neufeld, Lynnette M, Sheryl Hendriks,
coding the level of production and the proven reserves of oil, in per capita terms; Ross (2008) variable recording oil (and gas) rents per capita (i.e. accounting for levels of production, country specific costs and prices); Lujala et al. (2005) dichotomous indicator variable for diamond production, as well as for the type of diamond resource – primary (kimberlite) vs. secondary (alluvial); Lujala (2010) dichotomous indicator variables for the production of oil from both onshore and offshore deposits.

For the empirical estimations we use a logit model with robust standard errors and we control for the temporal relation among observations by including the duration of peace and cubic splines (Beck et al. 1998). The control variables are based on Fearon and Laitin (2003) and include: the lagged income per capita, the log of the country’s population, noncontiguous territory, the log of the estimated percentage of country’s mountainous terrain, lagged democracy score and religious fractionalization.<sup>10</sup> To test our conditional hypotheses, we use interaction terms between measures of resource endowment and measures of state weakness.

# **4. Results and discussion**

Tables 1 and 2 show the results for the context variables for which we expect natural resource endowments to exacerbate the chances of civil war onset: anocracy, political instability, riot 10 Income per capita comes from Penn World Tables and World Development Indicators, supplemented with per capita energy consumption. Population size is based on World Bank World Development Indicators. We do not control for recent independence because for some of our specifications data is only available post 1960, and this variable may not have any variance. Democracy is based on lagged values of Polity IV (-10 to 10). For the models using anocracy as an indicator for weak states only a democracy indicator is also included (Polity IV greater than five). Data on control variables is updated to 2003 using Lujala (2010).
**_Table 6: Walking speed by landcover_**

|**Landcover**|**Walking speed on a**<br>**flat surface**|
|---|---|
|Build‐uparea|5.0 km/h|
|Barren Area|3.0 km/h|
|Grassland|4.86 km/h|
|Scrubland|3.6 km/h|
|Agriculture area,<br>Forest|3.24 km/h|
|Water body|0.06 km/h|
|Snow|1.62 km/h|

We merge slope information with the land cover data to calculate a final walking speed. Results from Tobler’s hiking function and a model by van Wagtendonk and Benedict (1980)<sup>1</sup> were compared to reported walking times from the household survey. We found Tobler’s function yielded results most consistent with survey responses and consequently employed it within the model. Formally, Tobler’s hiking function is:

### **_Equation 4_**

Where: W= 6 e<sup>−3.5 abs (tanθ+0.05)</sup> W: Walking speed (km/h) Θ: Angle of slope The speed of a level surface is calculated to be around 5.04 by Tobler’s hiking function. The following formula is used to combine the walking speed by land cover and slope.

### **_Equation 5_**

Where: V: Modified Walking speed (km/h) V = v<sup>W</sup> 5.04 v: Walking speed on flat surface by land cover

## Monsoon modification

A uniform 25% reduction in walking speeds has been applied to all off‐road surfaces in the monsoon model.

# Combining on- and off-road results

We calculated each raster cell’s “cost” in units of
**Public financing** : The current NAIS is mainly funded by ex-post public contributions, whereby at the end of the crop season aggregate claims exceeding premium income are funded 50-50 by the state and central governments. While subsidy for agriculture insurance programs are used around the world and can be justified as a development measure (Mahul and Stutley, 2010), this post-disaster funding arrangement leads to an open ended fiscal exposure for governments and volatile annual contributions.  This post-disaster funding arrangement was in turn partly necessitated on account of a lack of an actuarially sound premium rating methodology without which predicting likely payouts was not feasible.

**Delays in claims settlements** : Another critical problem has been the systematic delay of NAIS claims settlement by 9-12 months or more. This has been partly caused by the time taken for the CCE data to be collated, but perhaps more importantly by state and central governments providing funding on an ex-post basis without adequate ex-ante budgeting. Delays in claims settlements not only cause cash flow problems for farmers already under the stress of a poor harvest, but also mean that they are not eligible for the next round of formal credit from banks for the next crop cycle, which follows immediately from the previous cycle. This can expose them to a debt trap and continued financial stress at the household level. This delay in claim settlement has contributed to the relatively low take up of crop insurance, despite significant increase in outreach in recent years. In a recent survey of farmers in Andhra Pradesh, over half of farmers cited delays in claim settlement as the key issue facing NAIS (Raju and Chand 2008), consistent with the findings of Joint Group (2004).

**Risk classification** : The NAIS rules for designing and pricing products, mean that the value of NAIS coverage for any given crop varies considerably across insurance units in the same state, and changes significantly year to year, even though farmers’ premium rates are uniform for each crop. As an illustration, consider two insurance units which are