rafmacalaba/data-use-mentions-extended huggingface dataset:
-3.38|-4.19|2.28|219.3|-119.3|
|Colombia|1992-96|5.20|-0.83|4.81|-.0.44|110.0|-10.0|
|Mexico|1989-95|4.93|0.46|5.03|0.37|93.2|6.8|
|Peru|1990-96|4.75|0.04|2.11|2.68|44.0|56.0|

Columns 3-6: average during inflow period minus average during the immediately preceding 5-year period.

Column 5: a minus sign means an improvement in the current account balance.

Column 6: a minus sign means a decrease in reserve accumulation.

Column 7: column 5 as a percentage of the sum of columns 3 and 4.

Column 8: column 6 as a percentage of the sum of columns 3 and 4.

_Source:_ World Bank data; IMF, _International Financial Statistics._ 8
SHRUG and fixed effects for zones (i.e., groups of districts) within Haryana. Our inference relies upon robust standard errors clustered by ITI district.

Table 5: Effects of exposure to COVID-19 using district-level COVID-19 death rate, all sample

||Wage<br>aspiration|Wage<br>expectation|Aspiration<br>gap|Unrealistic<br>high aspiration|Realistic<br>aspiration|Unrealistic<br>low aspiration|Willing to<br>migrate|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|COVID death rate|-264.476|-181.186|-83.290|-0.006|0.003|0.003|-0.002|
||[69.350]***|[57.614]***|[23.629]***|[0.001]***|[0.001]**|[0.002]|[0.001]**|
|Outcome mean|19,740.57|17,005.66|2,734.91|0.52|0.26|0.23|0.48|
|Observations|3,180|3,180|3,180|3,180|3,180|3,180|3,143|

_Notes:_ These regressions are based on a modified version of specification (1) where we replace the district fixed effects with the following district-level controls from the 2011 Census of India: population density, share of population that lives in urban areas, share of population that belongs to a Scheduled caste, share of population
a four steps basis. Demand data are drawn from household surveys (16 000 households) updated every 5 years, and provide trip matrixes for several purposes of travel: job, non job, and for duty vehicles. The network supply is defined by nodes and arcs linking these nodes. Arcs represents the links between each modes; they are defined by technical characteristics such as their length, the cost and speed, safety, comfort ... While this is all quite reasonable and standard, the details of the modeling are not always relying on the frontiers of the applier theory. For instance, modal choice is taken into account through a very crude model. Also, for traffic generation and distribution, the O.D matrix is drawn from current data, and induced traffic is determined as a fraction of cost decrease of each O.D relation.

The traffic model is run first to reproduce the current situation, and its parameters are calibrated so as to minimize the sum of the square differences between present and reckoned traffic on each link. Then the model is run with the new planned infrastructure, and it is run several times, for different values of toll.

More precisely, traffic modelling is developped following the following steps<sup>21</sup> _Step 0, Before the study : Setting up the traffic modelling framework_ (the "Modele global de la Region Ile de France"). This step is done by the regional branch of the Department of Transport, which usually runs the model ; the underlying work includes gathering data on supply (network description and encoding), demand (socio-economic characteristics of the zones into which the total area of the study is divided) and traffic countings. The result is a 21 Drawn from "Etudes de trafic et de sensibilite au peage en Ile de France", internal document, Ministere de l'Equipement, France (1999)
# **INTRODUCTION**

In 2015, an estimated 2.2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years.<sup>2</sup> Turkey’s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications.

This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘recent migrants’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities’ welfare is not observed in the data. This paper’s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants.

By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4.3 million Syrians were seeking refuge in primarily
This analysis has examined the relationship between the stock of emigrants a country has and its current passport cost. The advantage of the emigrant stock measure is that it is based on national census and household survey data in many countries around the world. It therefore captures most of the world’s legal and illegal migration. The disadvantage of this measure is that if passport costs change at different rates across countries over time, then today’s passport prices may not be reflective of the relative prices prevailing at the time of emigration. Detailed migration flow data is even rarer than migration stock data<sup>10</sup> , and so we restrict our analysis to legal emigration flow into the four traditional settlement countries of the United States, Canada, Australia and New Zealand.<sup>11</sup> The total migration flow into these four countries in 2004 is then expressed as a percentage of the sending countries 2004 population. The correlation between the emigrant stock of a country and the flow into these four countries for our sample is 0.833.

Table 5 then repeats Table 4, using emigrant flow rather than emigrant stock as the dependent variable. The median flow into the four traditional settlement countries in 2004 was only 0.02% of the sending country population. Passport costs as a share of per capita annual income are found to be significantly related to emigration flows, in a nonlinear fashion. The size of the estimated effect is quite large: among countries with passport costs less than 20% of per capita annual income, dropping from the 75<sup>th</sup> percentile of passport costs (2.5% of income) to the median (0.77% of income) is associated with a 0.024% increase, or doubling, of the emigration flow.

> 10 Many countries do not break down country of origin beyond the top ten or so sending countries.

> 11 Migration flow data collected from the Office of Immigration Statistics, Department of Homeland
# **3.2 Sensitivity of PLs to measurement error**

As noted in previous sections, measurement error is highly concentrated in certain parts of the country. In this section we use the 2008/09 data<sup>24</sup> to show how the construction of location-specific PLs is likely to be affected by measurement errors and how this might produce, in the areas where measurement error is more concentrated, PLs that do not reflect the baskets consumed by the poor (see also Maia and van den Berg, 2010). Southern Mozambique and urban areas nationwide appear to be particularly affected by observations in which the food consumption component is excessively low (due to the systematic underestimation of food consumption in urban areas described above) and whose non-food consumption share is consequently overestimated.<sup>25</sup> These observations, we posit, have a significant impact on both the food and non-food components of the location-specific PLs.

The large share of non-food consumption in these observations makes the non-food component of the PLs inordinately influential (see Appendix). In the Maputo area, for instance, the non-food component accounts for about 1/3 of the total PL, causing the Maputo poverty line to be roughly twice as high as all other PLs in the country. This differential is not justified by corresponding differences in any spatial price index (see Appendix).

Regarding the food component, the direct impact of underestimated consumption (beyond the consequent overestimation of non-food shares) is less clear. Broadly speaking, relying on these observations to construct ‘local baskets’ may produce counterintuitive results. Two types of measurement errors typically occur.

The first type of error results from the fact that many food transactions are not reported in the survey; this causes the aggregate level of food expenditures to be lower than expected. The consequences of this error are clear in terms of food/non-food shares but less so in terms of their impact of the food component of the food poverty line. This is because calculating the
and rail in the optimal path and compare them to the modal shares from the pre‐BRI scenario. If the optimal route after the BRI features a change in the modal share lower than 5%, we keep the result as is since there is no major shift in transportation mode. If, instead, the optimal route features a modal switch above 5%, we run additional scenarios and change the preference for maritime links until we find a shipping route post‐ BRI that keeps the transportation mode unchanged.

The procedure described above allows us to obtain results at the city‐pair level where before and after BRI travel time can be compared in a sensible manner. It should be emphasized that this lower bound scenario indeed corresponds to a conservative estimate since exporters benefit from an improved network but they cannot take advantage of those improvements to change their transportation mode. Figures 1 and 2 below show two examples of routes before and after the BRI where there is no significant mode switch – and which therefore are part of the lower bound analysis. In Figure 1, we can see that the shipping route between Beijing and Dushanbe goes through rail links both before and after the

> 12 One difficulty of such a network analysis is the association of each city with an “entry point” in the transportation network – usually referred to as “snapping”. Because cities are never exactly located on the network, we need to specify a snapping tolerance when performing this step. Since we compare two scenarios

> ( _before_ and _after_ the BRI), we also need to make sure each city is associated with the same snapping unless there is a new network element in their surroundings. To ensure we do not have inconsistencies between scenarios, we compare the snapping and manually exclude all cities featuring difference between scenarios. The number of cities quoted in the paper (1,000 in the global database and 1,964 in the regional database) reflects the cities selected through this procedure and which are actually used in our analysis.

11
financial incentives conditional on the quantity and quality of provided services, including family planning, antenatal and postnatal care, child vaccination and growth monitoring, and hypertension. Up to 70 percent of the quarterly performance payments can be distributed as bonuses to the clinical staff, while at least 30 percent had to be reinvested in the facility. The program included multiple layers of supervision and verification by regional and national agencies and a third-party counter-verification of the performance indicators.

This impact evaluation relies on a difference-in-difference design, which compared changes in outcomes between the districts implementing PBF and control districts within the same regions. Coverage rates were measured at the population level by household surveys conducted before the launch of the program and after three years of implementation. Detailed data on quality of care was collected through extensive facility-based surveys that included general facility assessments, interviews with health providers, and direct clinical observations of patient-provider interactions.

The results provide evidence of substantial positive impacts of the PBF reform on many dimensions of quality of care. It increased the availability of equipment and supplies at the primary health centers. It had positive impacts on infrastructure and infection prevention and control standards, such as the availability of containers for sharps and needles in consultation rooms. We also find positive impacts on provider competency, measured through clinical vignettes. Finally, we find evidence that the improvements in structural quality and provider knowledge also translated into better content of care. For example, providers in the PBF facilities are more likely to perform key physical exams such as to measure the height and weight of children under 5.

The PBF pilot had positive impacts on health providers. Their income increased by about twothirds due to performance bonuses, and they reported higher satisfaction, especially concerning the working conditions in the facilities. Community perceptions of health providers were also positively impacted. During the follow-up survey, individuals living in PBF districts reported significantly higher perceived competency of providers and that the staff work closely with and listens to the community. Additionally, individuals reported that during the three years of PBF implementation, the
# **4.3 Extension to a food insecurity risk model**

Regression analyses in section 4.1 allow to advance our understanding of the heterogeneous impact of the RSQ on food consumption and we have largely exploited those results to calibrate the vulnerability component of our prediction model in section 4.2. Predictions for the period 2011-2015 were produced to show that the model is very much in line with historical values. In this section, we move from historical RSQ scenarios to a probabilistic hazard component in order to extend the prediction model to a food insecurity risk model. The objective is to illustrate how the proposed modeling framework can also be used to estimate a probability distribution of future food insecurity, which allows to accurately depict the volatility of national food insecurity caused by the variability in the quality of rainy seasons.

To that end, we produce a catalogue of 10,000 synthetic RSQ scenarios based on a statistical analysis of historical monthly precipitations in Mauritania.<sup>43</sup> The set of synthetic events is a probabilistic representation of all possible RSQ scenarios in the country. We calibrate a simplified food insecurity model using the 5-month SPI only in the hazard component in order to simplify the hazard simulation procedure<sup>44</sup> and we compute national-level food insecurity estimates for each individual synthetic scenario. We can then infer a probability distribution of future food insecurity from the resulting sample of 10,000 estimates. We show the result in Figure 10. This is an example of how our methodology can be applied to welfare risk assessment, which is critical to the design of adaptive social protection programs. Improved risk information can support governments’ preparedness for food welfare shocks through adequately scaled response systems and coherent risk financing strategies.

In line with previous studies on the risk of poverty such as that of Hill and Porter (2017) and Skoufias et al. (2021), we also adopt a different approach where we directly use our nationally representative sample of households and simulate food consumption responses under
0.020|0.303|0.004|0.010|0.017|0.157|0.024|0.347|
|Group1+int1=Group2+int2=Group3+int3=0<br>Note: Administrative data from GUFE and su<br>and interaction terms (treatment dummies X<br>each triplet). α : sample restricted to non‐tr|0.000<br>rvey data<br>variable<br>aders outs|0.008<br>March 2015.<br>for heterog<br>ide Tokpa|0.000<br>OLS regre<br>eneous an<br>market.**|0.000<br>ssion of th<br>alysis), co<br>*, **, * ind|0.000<br>e outcome<br>ntrolling fo<br>icate statis|0.000<br>variable on<br>r strata dum<br>tical signifi|0.000<br>treatment d<br>mies (dum<br>cance at 1, 5|0.000<br>ummies<br>mies for<br>and 10%.|

36
facts on market concentration in trucking from Colombia, which inform a spatial imperfect competition model of carriers. When transport costs are high, they could be further magnified through endogenous entry decisions of carriers and high mark-ups. In a more general setting, high trade and transport costs could also affect market structure in downstream markets. Using market and farm-gate agricultural prices in India, Chatterjee (2020) estimates the role of spatial frictions in increasing the market power of intermediaries. Bergquist and Dinerstein (2020) provide similar evidence from Kenya using a randomized controlled trial (RCT) design.

Given the importance of data collection for the subject matter of this review, it would be befitting to conclude with a note on what data governments should aim to collect. The overall message is clear: there is need for high quality micro data on road freight transportation that is more direct and detailed than those usually available.

> 10Existing evidence on the presence and implications of the Alchian-Allen effect comes from studies of international trade (Hummels and Skiba, 2004; Irarrazabal et al., 2015).

> 11Also, the high level of informality and prevalence of owner-operators, as well as the general lack of list pricing in trucking, pose a challenge for using digital technologies such as web scraping to collect data.

20
17 data set, which we compare to the GDP values to construct the value added share of each sector in GDP. We also need information on factor shares in national income for the estimation. A United Nations national account data set is obtained which has information on labor share in GDP.<sup>7</sup> One minus the labor share gives us the sum of capital share and land share, which we are not be able to separately identify. To overcome this shortcoming, we chose to estimate the land share in the productivity equation as follows. It can be shown from (18) that the log of GDP is the sum of the log of overall prices (denoted Pt for brevity), productivity (At), and the weighted log of endowments: ln(GDPt ) = ln A t + ln Pt + s Lt ln L t + s Kt ln K t + 1( − s Lt − s Kt ) ln Tt . Then we can estimate the share of land (Tt) by moving those terms for which we have data to the left-hand side: Thus, when we use the labor share and one minus the labor share to weight ln l t<sup>≡</sup> ln(L t / Tt ) and ln(K t / Tt ), respectively, we should also include capital per unit of land ln k t<sup>≡</sup> ln(K t / Tt ) on the right-hand side of the equation, as shown in (19). The estimated coefficient associated with capital per unit of land is interpreted as the negative value of the average share of land in GDP.

We proceed with a system of eight equations, consists of the seven sectoral share (16) equations and the country productivity equation. According to (18) and (19), let the dependent variable of the country productivity equation be TFP _adjusted_ for capital-land ratio and prices of nontraded goods: 7 We thank Ann Harrison for
individuals subjectively rank themselves compared to others in their country on an economic ladder. Subjective economic ranking is defined as the response to the following question: “Please imagine a 10-step ladder where on the bottom, the first step, stand the poorest 10% people in our country, and on the highest step, the tenth, stand the richest 10% people in our country. On which step of the 10 is your household today?”. It differs from questions on well-being, life satisfaction or objective income rankings. The position on the economic ladder thus reflects a _relative_ measure of welfare, because it does not ask respondents about their absolute level of welfare but, rather, how does their welfare compare to that of the richest and the poorest in society.

We use the three waves of the Life in Transition Surveys for European countries covering the years 2005, 2010 and 2015. All surveys ask the question used to define our subjective economic standing. LiTS I was conducted in 2006 and surveyed almost 29,000 individuals across 29 countries. LiTS II was conducted in 2010 and surveyed almost 39,000 individuals across 29 countries. LiTS I was conducted in 2015 and surveyed almost 51,000 individuals across 34 countries. Our total sample for the study ranges from about 45,000 – to 60,000 individuals depending on the availability of relevant variables.

In order to contrast the respondent’s subjective ranking with objective information, we rely on the expenditure data collected by LiTS to estimate respondent’s consumption level. Each respondent is then placed in the objective decile of his/her country-year’s consumption distribution that emerges from the LiTS data. We also estimate an asset index based on principal component analysis of asset data also collected by LiTS. The assets that feed into this index are durable goods (like cars, washing machines, motorbikes and others) but also include some services (like heating or internet connection, for instance). In the same way as consumption, respondents are placed in the objective decile
were more positive in the second survey than in the first one - as documented by the positive coefficients of the indicator variable for the 2018 survey round. The improvement in project LGAs, however, was no different than that observed in comparison LGAs: the coefficients on the interaction between the project indicator and the second round survey indicator is small and indistinguishable from zero. Across all outcomes, we can reject impacts larger than 0.3-0.4 s.d.<sup>24</sup> This is consistent with the fact that we observed no differential improvement in responses in the vast majority of the underlying variables used to construct these indices.

The same overall result is found for the government officials’ survey on Table 10. Here we present three aggregate indices - the staff capacity index, the management index, and the performance culture index - as well as three of the main indicators of state capacity - having an updated master plan, perceiving the internal audit office as independent, and capacity to raise local revenue. For all outcomes, the point estimates suggests an improvement in the second survey round, though not always statistically significant. The difference-in-differences coefficients, on the other hand, are always smaller in magnitude and never statistically different from zero. For several estimates, point estimates are negative, suggesting that, if anything, comparison LGAs

> 24As a reference, we estimate overall increases of 0.41 and 0.52 s.d. for the indices on Urban Planning system and Views on taxation and fees, respectively, between the first and second round surveys. In both indices the differencein-differences estimates are an order of magnitude smaller, and we can reject effects larger than 0.25 s.d.

24