rafmacalaba/data-use-mentions-extended huggingface dataset:
better at the math and science components, while women score score better at Indonesian language.

Are these point estimates large or small? What would we expect a priori of a bachelor’s education for example? Of those without a bachelor’s degree, about two-thirds has a 2- year post-secondary diploma, and the rest, one-third, has only a secondary education. This heterogenous group scores .4 of a standard deviation below the group with a bachelor’s degree on average. Not very much perhaps. Another way of looking at this is by looking at the _R_<sup>2</sup> of these regressions. They are around 0.05, showing that only 5% of the variance of the (noisy) test score data is explained by these characteristics. Discounting the fact that test scores are noisy, we can estimate an alternative _R_<sup>2</sup> , measuring what the _R_<sup>2</sup> ’s would have been if the testing data would have been noise-free.<sup>23</sup> Even these alternative _R_<sup>2</sup> ’s are not very big, indicating that these background characteristics do not explain much of the variation in subject-matter knowledge of teachers. Apparently there are many teachers without university diplomas who do very well on the test, and, similarly, many teachers with university diplomas who do badly. This suggests that Indonesia’s teacher training programs – on average – do not manage to lift teacher subject-matter knowledge to a much higher level.

With more flexible regression models we can explain much more of the variation in test scores than the models presented in table 1. School fixed-effects models (where the teacher test score is regressed on a full set of school dummy variables) have _R_<sup>2</sup> ’s in the neighborhood of 0.60, indicating that 60% of the variance of (noisy) test scores can be explained at the
Policy Research Working Paper 9953

# **Abstract**

Wildfires throughout western North America produce smoke plumes that can stretch across the agricultural regions of the American Midwest. Climate change is likely to increase the number and size of these fires and subsequent smoke plumes. These smoke plumes change direct, diffuse, and total sunlight during the crop growing season and consequently influence yields of both corn and soybeans. The analysis in this paper uses a twelve-year panel of county-level yields from all counties east of the 100th meridian combined with measures of exposure to smoke plumes of low and high density during the growing season. It shows that low-density plumes enhance yields, likely by increasing in the fraction of diffuse light, while high-density plumes decrease yields. Because there are more low-density plumes today, the net effect is a slight increase in yields on average. As climate change makes wildfires larger and more frequent, the overall impact of smoke on yields is expected to be substantially more negative.

This paper is a product of the Development Research Group, Development Economics. 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/prwp. The authors may be contacted at abehrer@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
service provision, the measure of service provision by the UN has the largest relative effect on the acceptance of the tax department’s right to make people pay taxes. A belief that the UN is doing a lot, rather than a little, to help one’s country corresponds to a percentage point increase of 8.4 in the predicted probability of deferring to the tax department. A belief that international donors and NGOs are doing a lot to help one’s country corresponds to a percentage point increase of 7.2 in the predicted probability of deferring to the tax department.

> 11These regression tables are available upon request.

> 12Using different survey data collected in Zambia, I find a negative association between donor and non-state actor service provision and reported compliance. With the exception of church provision of welfare, I do not find any association between donor and non-state actors service provision and legitimating beliefs.

22
(4) _ITi,j,t_ = _β_ 0 + _β_ 1 _Competj,t_ + _γxi,t_ + _αi_ + _uit_ Where _ITi,j,t_ =IT use of firm _i_ , from sector _j_ , at time _t xi,t_ =Vector of control variables such as age, firm’s size and share of skills _αi_ =firm fixed effects As Table 2 shows, there is indeed a strong correlation between IT use and Chinese competition as the coefficients for our main two IT-use variables (computers-per-worker and share of labor with Internet) are positive and statistically significant at the 1% level in most of the specifications. In the case of the share of labor with computers, the results are slightly lower than the ones for computers-per-worker.

# **5.2 IT use and productivity**

First of all we estimate the relation between IT use and productivity using OLS in order to analyze if there is indeed a relation between these two variables without any further consideration regarding endogeneity or the relation with China’s competition. As Table 3 shows, for all IT use variables we observe that there appears to be a positive and significant correlation between IT use and productivity measured by sales-per-worker. These results are indeed consistent with what previous studies have found for developed countries.

As mentioned before, IT use is probably endogenous because there are likely to be unobservable characteristics that are both positively correlated with IT and with productivity. Therefore, we adopt an IV strategy to estimate the same equations, where we instrument our IT use variable with the instruments described in section 3.2. Results are shown in Table 4. Overidentification tests as well as the F-test, evaluating the strength of our IV, do not indicate invalidation of our instruments (See Table 5 for the firststage results). As the table shows, results are in line with the OLS coefficients, but slightly higher which may be the consequence of treatment effect heterogeneity (Imbens and Angrist, 1994).

In order to analyze what previous literature has found
Tables 4a-4c present the findings in Tables 2a-2c from a different perspective. We ask now how much more cheaply one could have achieved the same impact on poverty as with the uniform lump-sum transfer, if use had been made of a poverty map in an optimal fashion. We can see, for example, in Table 4a that in rural Ecuador even a poverty map at the region level (the level of spatial disaggregation that is representative in the household survey) would have permitted the same reduction in the FGT2 as the uniform transfer (in the low budget, low poverty line base case) at only 83% of the cost of the uniform transfer. With a more detailed poverty map that allows for disaggregation down to the parroquia level, the same impact could have been achieved at only 58% of the cost of the uniform transfer. In Madagascar, and Cambodia the savings are even more striking. For example, in these two countries one would need, respectively, only 37% and 31% of the uniform transfer budget to achieve the same reduction in the FGT2 with optimal targeting at the firaisana and commune-level (Tables 4b and 4c).

# _“Naïve” targeting_

The optimization scheme implemented above is intuitively straightforward. But working out exactly how much to give to communities is not always easy to describe. Given that the design and implementation of targeting schemes is often part of a political process, and that there is generally a need to be able to explain allocations in a simple and clear manner, it is of some interest to ask whether gains from spatially disaggregated geographic targeting are also significant when the poverty map is combined with simplistic, non-optimal, transfer schemes. We describe in Tables 5a-5c the results in our three countries of transferring our given budget on the basis of one such naive and simplistic scheme It is striking that in all 25
distributional change in India has centred on consumption-based trends);<sup>6</sup> second, the 1993/94 survey collects data on rural areas in India only, and thus its panel households with the 2004/05 survey are restricted to rural India and are not nationally representative.

Furthermore, the sample size of these surveys is large in absolute terms, but is not sufficient to allow an analysis of mobility at a finer, state-level disaggregation. The IHDS data are also prey to the standard concerns leveled at panel data regarding attrition (which is estimated to be around 20 percent) and the impact of measurement error. In short, it appears that these survey data do not enjoy the same official standing and recognition as the NSS surveys, at least in terms of household consumption and poverty consumption analysis.

Setting aside these concerns, emerging findings from the IHDS/HDPI-based analysis point to considerable churn within the income distribution, with both upward and downward mobility occurring alongside rising average income levels (Azam 2019; Ranganathan et al. 2017). Azam (2019) suggests that rural income mobility does not show a clear trend increase over time when one compares the 1993/94–2004/05 interval against the 2004/05–2011/12 interval. He goes on to show, however, that the characteristics of households experiencing upward mobility are different from those prone to downward shifts in relative position. Notably, households belonging to the disadvantaged Hindu castes (Other Backward Castes, as well as Scheduled Castes and Scheduled Tribes) are less likely to experience upward mobility than forward Hindu castes. By the same token, forward Hindu castes are less likely to experience downward mobility (Azam 2019). Muslim households display roughly similar mobility patterns as disadvantaged Hindu castes, but in urban areas (covering the period 2004/05–2011/12 only) they stand out as most likely to experience downward mobility. Azam (2019) goes on to show that mobility patterns across these population groups are attenuated, but
For the other two items, we use data from the Survey of Technological Innovation (PINTEC). However, this survey was conducted only for the years 2000, 2003, 2005 and 2008. We extrapolated our estimates to the other years by using a previous finding (Araujo, Cavalcante and Alves, 2009) that the PINTEC data on R&D spending is strongly correlated (correlation coefficient above 0.8) to the number of scientific-technical personnel, on which data is available for the intervening years. For example, to obtain the expenditures for year 2001 , we use data from the closest years available (2000 and 2003) in the following extrapolation: where is the ratio of spending on R&D from PINTEC per scientific-technical personnel (STP) for year Employment data comes from RAIS (a survey of the Ministry of Labour and Employment, roughly translated as Annual Registry of Social Information). Since PINTEC is only available for industrial and mining sectors, we are missing estimates on R&D expenditures for services, commerce and construction industries.

Copyright and license costs mainly include R&D in information-sector industries such as radio, television, sound recording, book publishing and the motion picture industry, usually leading to a copyright or license. Following an admittedly arbitrary choice in CHS, the development costs of new products in the entertainment and artistic industries are estimated as 20% of their employment and operating costs. To this we add estimates of industrial and mining sectors spending on royalties from the Annual Survey of Industry (PIA).

The third item – other product development, design and research expenses – is composed of new product development costs in financial services industries, new architectural and engineering design, and R&D in social sciences and humanities. The first is estimated as 20% of intermediate purchases of financial services, obtained from the Make and Use Tables of the National Accounts System. New financial products development is assumed to be wholly own-account intangible investment in the financial services industry.

13
The results of the latest ENVIF-VCM,<sup>3</sup> carried out by the Undersecretary for Crime Prevention, show that 2 in 5 women report having been a victim of some type of intrafamily violence in their lifetime, whether psychological or physical violence by a relative, partner or ex-partner, or sexual violence by a partner or ex-partner.<sup>4</sup> The intrafamily violence indicator has increased significantly over the years, from 32.6 percent in 2012 to 38.2 percent in 2017 and to 41.4 percent in 2020. This trend is mainly explained by the increase in psychological violence.<sup>5</sup> Additionally, 73.7 percent of reported incidents of psychological violence and 72.2 percent of those of physical violence were perpetrated by the current or ex-partner.

<mark>Chilean legislation criminalized femicide for the first time in 2010, through Law No. 20,480,</mark><sup>6</sup> <mark>which defines femicide as “</mark> _<mark>the killing of a woman carried out by someone who is or has been her spouse or partner, or with whom she has or has had a child in common, by reason of having or having had a sentimental or sexual relationship with her without living together</mark>_ <mark>.” Some emblematic cases of femicide in recent years have given rise to laws and bills that seek to modify the definition of femicide, such as the "Gabriela Law," enacted in March 2020 (Law No. 21,212</mark><sup>7</sup> <mark>), which broadens the typification of femicide to include as the perpetrator of this crime whomever, for reasons of hatred, contempt or abuse due to gender, kills a woman, not only when it is her spouse or partner. The "Justice for Antonia" bill seeks to criminalize the incitement of suicide
|5.77|3.39|5.26|
|Mean total per capita<br>expenditures per week (on all<br>food and nonfood iterns)|15.51|20.47|25.60|32.39|54.85|32.29|

Source: IFPRI Egypt Integnrated Household Survey, 1997.

Notes: N = 1,274. Rural population is divided into quintile groups o0 the basis of total per capita fitted expenditures.

a In 1997, 1 Egyptian pound (LE) = US$0.295. All expenditure figures are in nominal terms. b Includes expenditures on fully and partially subsidized sugar.

Difference between lowest and highest quintile groups is significant at the 0.05 level.
directions we propose under the current calls for urgent action.

# **7 Potential of frontline public health workers**

Citizens report greater reliance on publicly provided preventive and promotive health services than on curative health services. For example, in response to a question about how often they use government health facilities when someone in the family falls ill, only 12 percent answer "often", while 47 percent answer "rarely" or "never". This is consistent with the findings from the work of Das et al (2016) that people in rural India tend to rely on private providers for curative health care. In contrast, when women in the sub-sample of households who had experienced a recent pregnancy (within the past 5 years) were asked which type of provider gave them ante-natal care, 85 percent responded by indicating a public provider (the ASHA and AWW are the most frequently cited); only 14 percent indicated a private sector provider.

This difference in the extent to which the public sector is used for preventive and promotive rather than curative care is particularly large for poorer and less politically connected citizens. Table 5 shows that respondents who are in positions of political power–elected politicians of local government (the panchayats at the village, block and district levels), members of the state legislative assembly (MLAs) and members of the national parliament (MPs)–are significantly more likely to use government clinics or hospitals for curative care needs. We are unable to control for measures of income and assets because these questions were not asked in the surveys of higher-level politicians owing to sensitivity issues and time constraints in getting these powerful respondents to sit down for a survey. We hypothesize that the estimated influence of political power in accessing curative care in government hospitals may be even higher if we were to be able to control for income, since higher-level politicians would tend to have higher incomes, and higher incomes are associated with greater use of expensive private care facilities.

Among the different frontline workers who could provide maternal and child
’s classification also considered to be agricultural based economies. These concepts are not at odds as those countries with a sizeable base of minerals, energy and forest, and exports (not GDP) counts for the resource rich country classification.

**Togo is not immune to hazards of natural origin, but natural disasters recorded in the country have not been as frequent or as severe as in other Sub‐Saharan African countries.** EMDAT<sup>36</sup> , the most comprehensive database on disasters of natural and technological records a total of about 1,200 deaths and 770,000 affected in Togo between 1965 and 2005, most of them linked to epidemics, droughts and floods (Table 9). This is not an insignificant number, but as shown in the table the number of killed and affected taking population size into account is much smaller than the average for SSA.

**Table 9: Summary of Natural Disasters since 1990, Togo.**

||**Togo **|**SSA excl. Togo **|
|---|---|---|
|Population 2000|5,247,486|670,817,822|
|Killed 65-05|1,172|1,061,604|
|Affected 65-05|766,161|420,367,666|
|Killed/year|29|26,540|
|Affected/year|19,154|10,509,192|
|Killed/1000000people|6|40|
|Affected/1000 people|3,650|15,666|

_Source_ : EM‐DAT: The OFDA/CRED International Disaster Database.

> 33 A country is deemed to be resource‐rich if all the following are true: 1) Current rents from energy, minerals and forests exceed 5% of GNI; 2) A forward moving average of these rents exceeds 10% of GNI; and, 3) The share of primary commodities in
. For example, in the case of Agriculture, the trade weighted average tariff for the CEFTA countries is 11.58 percent, which is significantly lower as compared to the 19.65 percent border differential tariff equivalent that we obtain from the structural gravity regressions. The corresponding numbers for Mining are 1.68 percent weighted average tariffs and 10.62 percent tariff equivalent border differential. And for Manufacturing the numbers are 5.22 percent weighted average tariffs and 7.92 percent tariff equivalent border differential. Importantly, there is no tariff data that corresponds to the large (16.87 percent) tariff equivalent border differential that we obtain for services. This highlights the advantages and importance of using our estimation structural gravity approach to measure border differentials. Third, comparison between the tariff data and our border

> 20 Following the literature, we set _σ_ = 7. This value is also close to the corresponding meta-analysis index

> ( _σ_ = 6 _._ 13) from Head and Mayer (2014).

18
<!-- Start of picture text --> Predicted probabilities of stunting during late childhood (24-60 months of age)<br>for select conditions<br><!-- End of picture text --> <!-- Start of picture text --> : ar 35.8<br>Predicted baseline risk —oO-<br>Pre-natal environment: Inadequate =e40.3<br>Pre-natal environment:. Adequate —o—29.2<br>Food security/diversity environment: 344<br>Inadequate<br>Food security/diversity environment: 3°<br>Adequate<br>Water/sanitation environment: Inadequate —o—38.7<br>Water/sanitation environment: Adequate 2634.5<br>Post-natal health care environment: y<br>Inadequate<br>Post-natal health care environment: 336<br>Adequate<br>0 10 20 30 40 50%<br>Predicted probability of stunting (%)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: The predicted probabilities are based on model 2c (adjusted) and calculated at the means for all other predictors. 95% confidence intervals are shown
We calculate the transport price, _P,jk,_ as the share of liner transport charges in import values for good k (at the 6 digit HS aggregation), multiplied by the unit value of imports. The U.S. Department of Transportation defines transport charges as all freight, insurance and other charges (excluding import duties) incurred in bringing the merchandise from alongside the carrier at the port of export and placing it alongside the carrier at the first US port of entry.<sup>27</sup> However, actual data reported may include charges for port services and inland transportation.<sup>28</sup> To reduce the potential bias resulting from differences in inland transportation costs, we exclude observations for which the origin of the import is different from the country of the port of shipment (e.g., landlocked countries) as well as all in-transit shipments.<sup>2 9</sup> Additional information on the construction and sources of all variables is provided in the data appendix.

Table I presents an overview of our estimation dataset. It covers all U.S. imports carried by liners from the 59 countries for which we could find information on maritime policies. Data refer to 1998. Liner imports account for around 65 percent of the total value of maritime imports, the remaining 35 percent being carried by tramp services.<sup>30</sup> About half of all U.S. imports (including all modes of transport-maritime, air and road) are carried by liners.

# **IV.** Econometric Estimates

# _Estimation methodology_

We begin with an ordinary least squares estimation of equation (5) over the entire data set. The error term _eUk_ is assumed to be independently distributed across exporting countries, but we _27_ If insurance costs are not closely correlated with transport charges, there is the possibility that our transport price variable is distorted. However, this should at least partially be remedied by the inclusion of product fixed effects
# Table 1: Summary Data and Estimated Welfare Changes for Rural Poor, Bangladesh, 1978-79

|Monthly<br>household<br>income<br>(Taka)|Cumulative<br> <br>percent<br>households|<br>Expen<br>(% inc<br> <br>Food<br>(total)|diture<br>ome)<br>Food-<br>grain|In<br>Vages|come by sou<br> <br>Business<br>income|rce_(W)_<br> <br>Imputed<br>income|*<br> <br>n<br>(Z)|Welfare ef<br>10% price<br> <br>Short-run|fects of a<br>increase<br> <br>Long-run|
|---|---|---|---|---|---|---|---|---|---|
|<300|8.7|70|46|62|11|24|35|-.84|.71|
|300-399|19|72|48|57|16|24|35|-1.1|.28|
|400-499|31|72|47|48|21|27|42|-.94|.26|
|500-749|55|73|47|38|26|30|45|-.86|.09|

Sources: Expenditure data are from BBS (1984, Table 15.25) and are given as a proportion of income, uising the ratios of mean expenditure (BBS, 1984, Table 15.25) to mean income (BBS, 1984, Table 15.28). Income data are from BBS (1984, Table 15.28). Welfare<sup>