rafmacalaba/data-use-mentions-extended huggingface dataset:
et al. 2002). The informal settings and business climates in which rural enterprises operate make for much different entry barriers.

According to the RIC survey data, start-up capital is the most important obstacle to starting rural farm and nonfarm business as perceived by existing rural entrepreneurs (figure 4.1). The second most important obstacle is access to inputs or raw materials for nonfarm enterprises, and the availability of water for farm business. The third most important obstacle, which affects both farm and nonfarm businesses, is market information and knowledge. Market knowledge can be hindered because of poor infrastructure such as limited roads and transportation. Availability of electricity comes at the fourth for nonfarm business, whereas the availability of skilled labor and roads and transportation are the fourth for farm business.

In terms of motives to start a business, identifying market opportunities (44.4 percent) and obtaining skills (32.6 percent) were the top two motives reported by nonfarm entrepreneurs. For farm entrepreneurs, identifying market opportunities was less prevalent, and reported by just 18.2 percent of respondents. 34.3 percent reported obtaining skills as their primary motive for beginning operations (figure 4.2). These self-reports on the part of farm and nonfarm entrepreneurs should be useful in informing policies that promote incentives for business entry. Policies that effectively enable people to obtain skills and identify existing market opportunities clearly fall within this category.

**Figure 4.1: Obstacles to Start Business Figure 4.2: Motives to Start Business**

<!-- Start of picture text --> farm nonfarm farm nonfarm<br>50.5 18.2<br>start-up capital/funds 71.1 market opportunities 44.4<br>34.3<br>access to inputs/raw 9.1 obtain skills 32.6<br>materials 19.5<br>28.3<br>19.2 household lost wage earning 26.9<br>knowledge about market
# **Figure 14 Export of Wood and Wood Products, 2001**

<!-- Start of picture text --> Sawnwood<br>45%<br>Roundwood<br>13%<br>Joinery and<br>Plywood<br>carpentery<br>9%<br>6%<br>Furniture Other wood<br>11% products<br>Packing boxes 6%<br>Firewood and pallets<br>3% Pulp of wood, 3%<br>paper and Particle board<br>paperboard<br>1%<br>3%<br>Source : Swedish National Board of Forestry, 2003<br>This export structure contrasts markedly with that of Finland. Whereas Latvia’s forestry<br>sector exports are concentrated almost entirely in labor intensive activities which lead to<br>little value added or processing in Latvia, most of Finland’s exports are in high value<br>added, technology intensive activities.<br>Figure 15 Exports of Forest Cluster in Latvia and Finland<br>Export Structure of Forest Cluster in Latvia and Finland, 2000<br>25<br>20<br>15 Latvia<br>10 Finland<br>5<br>0<br>Labor-intensive Technology-driven<br>Saw & Other Mills, Planing Containers & Boxes Furnitures & Fixtures Pulp of wood, paper & paperboard Metal & Wood working Machinery Special Industrial Machinery Electrical Industrial Machinery<br><!-- End of picture text --> This export structure contrasts markedly with that of Finland. Whereas Latvia’s forestry sector exports are concentrated almost entirely in labor intensive activities which lead to little value added or processing in Latvia, most of Finland’s exports are in high value added, technology intensive activities.

_Source_ : Swedish National Board of Forestry, 2003 What does all this imply for Latvia? Simply put, Latvia needs to focus not just on promoting high tech
crisis never occurred.<sup>10</sup> The turnaround that began in April 2020 was officially acknowledged on July 19, 2021, when the National Bureau of Economic Research (NBER), the arbiter of recessions, officially declared that the U.S. recession ended in April 2020.<sup>11</sup> The two-month duration make it the shortest recession in U.S. history, just one-third as long as the six-month 1980 recession, the prior shortest on record since 1854.<sup>12</sup> During the recession as economic output fell, personal incomes of many Americans actually rose, and debt loads lightened due to government stimulus programs providing additional income. Research suggests that government policies during the pandemic led poverty rates to fall and low percentiles of income to rise across a range of demographic groups and geographies (e.g., Han, Meyer, and Sullivan, 2020). An Experian report suggests that the average consumer credit scores increased significantly in 2020, mostly driven by a significant reduction in the number of consumers with subprime designation (Lembo Stolba, 2021). Credit card balances reported in the G.19 Consumer Credit statistical release fell by a very large annualized rate of 32 percent by 2020:Q2 (Adams and Bord, 2020). The Wall Street Journal reports that most Americans got richer during the first year of the pandemic owing to fiscal and monetary stimulus, as well as stock and housing market booms.<sup>13</sup> The fates of small businesses – which often are the key income sources for the proprietors and employees that operate them – were not as bleak as first predicted. While many businesses failed during the COVID-19 crisis as discussed above, one study using Bureau of Labor Statistics

> 10 <u>https://www.wsj.com/articles/the-economic-recovery-is-here-rebound-jobs-stock-market-unemployment-bidenaid-package-11622642152</u>

> 11 <u>https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021</u
Those who switched to the new system at the age of 47 or higher will not meet the guarantee eligibility criteria. Thus the largest risk is posed by individuals who have switched at age 41 to 47 years. To reduce the risk, government public awareness campaign has focused on explaining the negative implications of switching to the new pension system at an older age. The age distribution of fund members will not be know until the end of the transition period (end of year 2000). The first almost one million fund members, however, shows a fairly reassuring picture: almost three fourth of the switchers is 35 or younger, around 15 percent is 36 to 40 years and approximately 10 percent is 41 to 47 years.

Since the level of the normative annuity is linked to the pension benefit offered in the first pillar of the pension system, the risk of the normative guarantee being called also depends on the developments under the PAYG scheme. Changes affecting the benefit level under the first pillar will directly affect the level at which the normative guarantee

> For reference see The World Bank: Hungary Country Economic Memorandum 1999; Fiscal Chapter. The calculation is based on the assumption that average real wage growth equals 3 percent, the average return of pension funds is above real wage growth by 1.5 percent, administrative cost of pension funds is 0.15 percent of the contribution collected. For income, the average individual wage profile was used based on historical data available at the Hungarian social security funds.

13
spending on subsequent operation quality. In Table 9 we find that prior analytical work is indeed linked to improved project outcomes, especially in lower-income IDA settings and possibly even fragile and conflictaffected settings (FCVs). Analytical work completed up to three years before project approval is associated with an increase of 0.12 in IEG outcome ratings (0.15 points for IDA countries); half the impact associated with a one-point improvement in the CPIA. This suggests an important role for World Bank initiated analytic work in country environments that may be relatively “knowledge scarce”, where the information generated by these analytic efforts informs subsequent project design.

# **5. Summary and discussion of unsettled questions**

This paper reviews the literature on the correlates of World Bank project outcome ratings and then extends previous analytic work by identifying further staffing inputs into the production process, linking previous sector analytic work to individual projects, and extracting a richer set of design quality explanators from a subset of projects. Results from these analytic extensions all point towards the importance of project design in ultimate project success, especially in the more sectoral complex settings of middle- and upper-middleincome countries (IBRD). Previous quantitative work has found fairly weak evidence for the importance of project design; however, it appears that this is in part due to the paucity of relevant indicators captured in routine World Bank administrative data. It would be an error to conclude from these earlier studies that project design is not an especially influential determinant of project success.

Like much of the project effectiveness literature, the analysis discussed here is descriptive in nature. Hence, any observed association between project inputs and outcomes may either represent an underlying causal relationship or may be confounded by further unobserved factors. While our understanding of the project cycle leads us to consider these correlations as plausibly capturing the role certain inputs play in project success, there are alternative explanations that render the correlations spurious. For example, project staff are not randomly assigned to develop or supervise projects but are chosen by management. Other
# reforms.

We  begin  our  analysis  by  exploring  the  behavior  of  average  firm-­‐level  wages  and  find  that  a decline  in  trade  protection  is  associated  with  an  increase  in  average  wages  in  exporting firms  relative  to  domestic  firms,  consistent  with  the  findings  of  Amiti  and  Davis (forthcoming)  for  the  liberalization  period  in  Indonesia<sup>5</sup> .   However,  we  argue  that  the analysis  of  average  firm-­‐level  wages,  although  informative,  is  incomplete  along  several dimensions.  First,  it  cannot  fully  account  for  the  impact  of  a  change  in  trade  barriers  on workforce  composition  in  terms  of  both  observable  worker  characteristics  (not  available  in firm-­‐level  datasets),  as  well  as  factors  that  are  observable  to  the  managers  of  the  firm  and hence  impact  wages  but  are  unobservable  in  the  data,  such  as  the  innate  ability  of  the worker  and  any  additional  productivity  (ability)  that  obtains  in  the  context  of  employment in  the  specific  firm  (match  specific  ability).   Furthermore,  the  firm-­‐level  analysis  is undertaken  under  the  assumption  that  the  assignment  of  workers  to  firms  is  random  and ignores  the  sorting  of  worker  into  firms  and  the  resulting  change  in  the  distribution  of match-­‐specific  ability  in  different  firms.  Using  matched  employer-­‐employee  data,  we  test whether  wage  behavior  at  the  worker  level  confirms  the  maintained  assumption  of exogenous  worker  mobility,  as  in  Abowd,  McKinney  and  Schmutte  (2010).  Consistent  with theoretical  models  emphasizing  search  dynamics  or  other  frictions  as  important determinants  of  the  relationship  between  trade  liberalization  and  wages  such  as  Helpman, Itskhoki,  and  Redding  (2010)  and  Davidson,  Matusz  and  Schevchenko  (2008),  we  find  that the  data  decisively  reject  the  assumption  of  exogenous  job  mobility.

Importantly,  using  detailed  information  on  worker  and  firm  characteristics  to  control  for compositional  effects  and  using  firm-­‐worker  match  specific  effects  to  account  for  the endogenous  mobility  of  workers,  we  find  the  differential  effect  of  trade  openness  on  wages 5  To  our  knowledge  Amiti  and  Davis  (forthcoming)
strongly constrained in our model. Regarding formal housing, inertia in housing supply slows down potential formal housing relocations. As a result, urban reconfigurations have a low potential for emissions reduction in our model. We conducted two sensibility checks (Appendix B). First, we implemented a fuel tax of the same monetary amount (10cts) per kilometer for all transportation modes (Table 9). Second, we look at the result after 20 years, to let formal housing supply the time to adjust despite inertia (Table 10). In these cases, scenario 1 remains more efficient in terms of emissions mitigation, and urban reconfigurations play a small role compared with modal shares.

As a result, taxing polluting public transportation modes might be less efficient in terms of emissions mitigation than taxing private cars only, and policy makers should in priority account for potential modal shifts induced by the tax. This result is in line with Avner et al. (2014), which finds that, in Paris, reducing transportation emissions requires a lower level of gasoline tax if a dense network of public transportation is available: indeed, having public transportation options allow to play on modal shift, in addition of urban reconfiguration, to reduce emissions.

|||Pas<br>|senger-km var<br>|iations<br>||Emissions<br>|
|---|---|---|---|---|---|---|
||Walking|Train|Private car|Minibuses/<br>taxis|Buses|variations|
|**Scenario 1**|**+0.45%**|**+1.24%**|**-9.34%**|**+4.52%**|**+9.13%**|**-5.04%**|
|-<br>modal shares<br>
One study studies the effect of school size on student performance using TiMMS data for both developed and developing countries: Schütz (2006) finds significant size effects for 11 out of 51 countries, 8 out of these 11 are developing countries. Most of these significant effects are positive, suggesting that, if there is a relationship between size and performance, bigger schools appear to be typically better.

# II.2 _The evidence from transition countries_<sup>_9_</sup>

The literature on schools in transition countries is much less developed than the literature on either schools in developed countries or schools in developing countries. This paper is indeed the first that tries to summarize the existing literature on schools in transition countries. We divide what follows into two parts: we first focus on the cross-country studies that include transition countries. Next, we focus on single-country studies.

# _a. Cross-country studies_

There is a number of studies that do include data from transition countries, even though they do not focus on transition countries or have specific conclusions based on the estimations they obtain for transition countries.

Hanushek and Luque (2002) and Hanushek (2003), reviewing a hundred production function estimates covering 37 developed and developing countries<sup>10</sup> , state that there is no consistent evidence that more school resources and better teacher quality lead to better test results, since in the majority of studies these variables are found to be insignificant. Despite this, studies for developing countries find some positive relationship in a greater share of cases<sup>11</sup> (possibly because the initial level of education spending matters). Class size is most often found to be insignificant, but for transition countries, if significant typically positive. These authors

> 9 Table A1 and A2 in the appendix summarize the empirical findings of education-relevant factors in transition countries, and in developed and developing countries.

> 10 Among them eight transition economies: Hungary, Latvia, Lithuania, Poland, Slovak Republic, Czech Republic, Romania and
.576)|
|HGF intra‐industry share|0.008|0.016**|0.014**|0.012**|
||(1.174)|(2.463)|(2.354)|(2.029)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|28,633|33,084|36,946|41,931|
|R‐squared|0.236|0.189|0.195|0.181|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch

> 25 The quartiles are created based on labor productivity. For each firm we calculate the quartile for each period, and take the modal quartile to ensure that firms do not move across subsamples.

15
the size of crop losses) and their **attribution** to specific disasters. For the quantification of losses, the paper assesses the suitability of proxies based on (i) land area and crop damage, (ii) expert assessments, (iii) past harvest data or data from similar but unaffected plots, and (iv) farmers’ postplanting expectations to inform a micro-level crop production loss estimate. Subsequently, it (v) studies the reliability of self-reports for linking losses to specific disasters and (vi) compares them to geospatial data on disaster exposure.

Based on these questions, the paper makes a contribution in three ways. First, it operationalizes the methodology proposed in Conforti, Markova and Tochkov (2020) for use with state-of-the-art, crop production survey data at the plot level based on the format of the LSMS-ISA questionnaire. Second, the paper provides an in-depth investigation into the feasibility of measuring pre-harvest crop production losses due to disasters at scale and yet a highly disaggregated level and provides concrete recommendations for survey design. As such, it informs a new generation of survey data that facilitates the integration of a diverse set of data sources for measurement and analysis. Lastly, the paper identifies crucial remaining knowledge gaps and delineates relevant avenues for future research. Importantly, the methodology explored lends itself primarily to small- to medium-sized disasters and those with slow onset (e.g. droughts). For large scale disasters and swift policy action requiring an immediate appraisal of damages and losses, post-disaster needs assessments (PDNAs) and aggregate-level analyses may present complementary vehicles that are more readily deployable.

The remainder of this paper is structured as follows. Section 2 contextualizes the measurement of disaster crop losses in the literature and depicts the key approaches and challenges associated with their accurate quantification and attribution to disasters. Section 3 introduces the data sources the paper draws on and the key questions that motivate its empirical strategy. Section 4 presents the paper’s analysis for each of these questions while Section 5 closes with our headline findings and
stunting and underweight of children using predicted data.

When new data is needed, it may be tempting to purposefully only collect the covariates _x_ , which are used to predict the outcome variable of interest _y_ , and to scale down the collection of _y_ itself, particularly if this yields a significant financial cost reduction. It is not uncommon that predictors of household welfare such as demographic and dwelling characteristics, education, employment, and asset ownership, can indeed be collected relatively inexpensively. In health sciences, simple oral questions and anthropometric data taken by a simple non-invasive device too may serve as a predictor of the outcome that is expensive to measure. Collecting _y_ for a sub-sample only, and the covariates _x_ for all, is referred to as “double-sampling”,<sup>3</sup> see e.g. Hidiroglou (2001).<sup>4</sup> The advantage of this approach is that the prediction model can be estimated with data for the relevant population and time period (the same population for which the covariate data is available). The alternative where the model is estimated to data from an entirely different dataset that may describe a different population at a different point in time is referred to as “non-nested double sampling”, in which case one will have to make assumptions about how the model has evolved between the two different datasets (or assume that the model is invariant), see e.g. Kim and Rao (2012) as well as Douidich et al. (2015) for an empirical application to poverty measurement. We will refer to any estimator that works with predicted data (i.e. imputations of _y_ ) as a prediction estimator.

There is a considerable practical interest in adopting a double-sampling approach and relying on predicted data in the hope of reducing financial costs while preserving a reasonable degree of statistical precision. Consider for example the recent initiative by the World Bank under the name of SWIFT (Survey of Well-being via Instant and Frequent Tracking) which “does
proportional to the income and population of nearby coastal counties (Pielke and Landsea 1998; Pielke 2007; Pielke et al. 2008). Other studies assume that damages are simply a function of wind speed cubed (Hallegate 2007). The remaining studies assume that damages are proportional to US GDP (Nordhaus 2006; 2010). This study empirically tests the importance of local income and population. Income and population were based on the five coastal counties near where the eye of the storm strikes land. The assumption that damages largely depend on coastal counties is supported by empirical evidence suggesting this is where over 85 percent of aggregate storm damages occur (Pielke and Landsea 1988, Pielke 2007; Pielke et al 2008, Nordhaus 2010; NOAA 2009). Income and population for the historical storm sample was calculated using decennial census data by county (US Census of Population 1960, 1970, 1980, 1990, 2000). Values between Census years were inferred. Dollar values were updated to 2010 USD using the GDP deflator.

The results of the regressions of log damages on the log of the independent variables are shown in Table 1. The regressions reveal that the intensity of the storm is highly significant. Whether measured by maximum wind speed or minimum pressure at landfall, damages are a highly nonlinear function of intensity<sup>4</sup> . Damages increase inversely with the 86<sup>th</sup> power of minimum pressure. A 1% reduction in minimum pressure almost doubles storm damages. Damages increase with the fifth power of wind speed. A 20 percent increase in wind speed doubles storm damages. These results are much more nonlinear than commonly assumed (that damage is related to the cube of wind speed e.g. Emanuel 2005). However, these values are lower than comparable estimates by Nordhaus (2010) who finds that damages increase with the ninth power of wind speed. Comparing the results of the minimum pressure and wind speed regressions reveals that minimum pressure has a more significant coefficient. The