rafmacalaba/data-use-mentions-extended huggingface dataset:
markets in the OECD.<sup>17</sup>

- <u>Policy induced leakage estimates. The above estimates can be considered passive or</u> comparative-advantage based (as opposed to policy-induced and subject to leakage). Turning to policy –induced leakage estimates, Kee et al. (2010) use a gravity model to estimate the effects on exports of OECD countries of carbon taxes and energyefficiency standards, both captured by dummy variables using 3-digit ISIC data for manufacturing exports over the period 1988-2005. They find no evidence of a loss of competitiveness from a carbon tax for energy-intensive industries when exporting countries impose a carbon tax, but a significant reduction in bilateral trade the year that efficiency standards are applied, whether the efficiency standard is applied by the exporting or by the importing country. They conclude that this evidence suggests that subsidies and exemptions to the energy-intensive industries must have compensated

> 16See World Bank (2008, figures 2.2,2.3,2.4)

> 17Using a Leontief-type multi-region input-output model, similar to the method used by Peters et al. (2011) in their calculations, Atkinson et al. (2010), compute first-order effects from applying a 50$/ton tax on the carbon content of imports across all countries. They estimate that this tax would amount to an export tax rate of around 10% for China’s exports across destinations. In comparison, EU exports would face an average export tax rate of 1.2% and the US of 3.1%. This just confirms that taxing CO2 is a tax on developing countries. It also explains why, some countries, like China are already starting to tax their exports of CO2 intensive goods since it is better to collect the tax oneself than to handover the revenue to foreigners.

16
by a refugee, share of refugees going to a contiguous country, the Herfindahl – index of destinations, or share in a high-income OECD country from country _s_ in time period _t_ , and _δt_ and _δs_ are time and source country effects.

Source country effects imply that we are exploiting time variation within a source country over time in how far refugees travel. The coefficients of interest are the time effects _δt_ . The regression is weighted by total refugee stock, to obtain estimates of how outcome variables changed at a refugee, rather than country level. Standard errors are clustered at the source country level.

Panel (a) of Figure 4 plots the time effects for the average distance traveled along with 95 percent confidence intervals. Since the distance traveled is in logs, the coeffi13
disasters and environmental shocks.<sup>44–47</sup> These studies have mostly focused on single geographies and stopped short of quantifying the value of disaster related losses in smallholder agriculture. Other studies have relied on macro-data from disaster inventories to assess the impact of disasters on agriculture.<sup>14</sup> Here we offer a cross-country perspective using harmonized survey micro-data from six African countries. We value crop production losses to assess the economic importance of disaster impacts on rural households and African agriculture and the regional economies more broadly. The analysis shows that disaster related crop production losses among African smallholder farmers are widespread and significantly reduce the production of affected farms. In any given year, between 18% and 94% of cropfarming households suffer such crop losses and on average 53% of plots’ harvest potential is lost when they are hit by adverse shocks.

In aggregate, disaster impacts reduce the national crop production by 29% every year on average and by up to 81% in years with large-scale disasters. These results show that crop production losses due to adverse climatic events are significant both for individual farms and for the entire sector and the study countries’ economies.

We show that the EM-DAT disaster inventory misses out on a meaningful share of disaster impacts in the agricultural sector in Africa when compared to the micro-data analysis. The micro-data captures many smaller, more localized disaster events, which are less salient and therefore less likely to be reported on and register in the disaster inventory.<sup>37,48</sup> At the same time, disaster impacts are more likely to be missing entirely in lower-income settings.<sup>30</sup> Our analysis shows that less salient and underreported disaster events still have significant economic impacts.

The findings have implications for policies and interventions aimed at disaster risk reduction and resilience building. For such policies to be effective, it is important to recognize the risks from less salient and underreported adverse events and offer ways to insure households’ livelihoods against
scale, the Child and Youth Resilience Measure-Revised, and Locus of Control, all in terms of their raw scores; The rows marked as _H0:..._ show the p-value for a t-test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes (primary and secondary) and per round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of CRs.

36
**Figure 4: Impact of U.S. interest rate shocks on EMDE financial markets** A.  Impact of 25 basis point shock on EMDE interest rates after one quarter

<!-- Start of picture text --> A. Impact of 25 basis point shock on EMDE B. Impact of 25 basis point shock on EMDE<br>interest rates after one quarter financial variables after one quarter<br>Basis points Percent Percent<br>50 1.5 7<br>40 5<br>30 0.5 3<br>20 1<br>10 -1<br>0 -0.5 -3<br>-10 -5<br>-20 -1.5 -7<br>-30 -9<br>-40 -11<br>-2.5 -13<br>Real Inflation Reaction<br>Real Inflation Reaction<br>Source: World Bank<br>3-month 10-year EMBI+ 3-month 10-year EMBI+ 3-month 10-year EMBI+ REER (RHS) REER (RHS) REER (RHS)<br>Capital flows Equity prices Capital flows Equity prices Capital flows Equity prices<br><!-- End of picture text -->

# _Source:_ World Bank

_Notes:_ Panel local projection models with fixed effects and robust standard errors. See table A3 for details. Models estimated over periods as long as 1997Q2-2019Q4; they exclude observations during global financial crisis (2008Q4-2009Q4) and the COVID-19 pandemic. Bars reflect impact on first quarter ( ). Orange whiskers reflect 90 percent confidence intervals. EMBI = emerging market bond index. tt+1 B. Positive “capital flows” values reflect an increase in net liabilities of portfolio and other investments as a percent of GDP yy for EMDEs. Positive “REER” values reflect an appreciation in the exchange rate. Figure excludes fixed exchange rate economies. REER = real effective exchange rate.

25
**Polac,Pnnlng, and ResearchC**

# Education and Employment

Eighty percent of the trainees headed for Israel's labor force go to full-time vocational secondary schools that devote a third to a half of the curriculum time to general studies. Students tei.d to come from a higher socioeconomic level than those in other training programs.

The rest of Israel's vocational students are evenly divided among the Ministry of Labor's remaining three programs. The apprentice attends school one day a week and works on the job the rest of the time. The student is typically a low academic achiever with a low socioeconomic background.

Students in industrial schools spend three days a week in the plant where the school is located and three days in the school. The program is less demanding and more practical than the vocational school curriculum. No tuition is charged and trainees are paid for their work.

The one-year full-time training courses for 16 and 17-year-olds, mmny of whom are dropouts, concentrate on practical training. Those who complete the course get additional training in the army during their compulsory three-year service.

Of the four types of training in Israelvocational secondary schools, apprenticeship courses, industrial schools, and full-time training courses - vocational school is by far the most expensive. In 1977 Michael Borus found that the cost of apprenticeship courses was one-seventh that of vocational schools; industrial suiools and trairing courses cost only a third to a quarter as much. This 7-year longitudinal study confirms Borus' earlier tentative findings, concluding that since post-training earnings were roughly the same for all four programs, the apprenticeship system was the most cost-effective.

These findings echo those of case studies in other countries. But, to conclude that vocational training schools are not cost-effective on the basis of eventual earnings alone is to ignore part of tC. picture. The goals of the vocational secondary school in Israel extend beyond those of providing usable labor market skills. These wider social and educational goals are largely absent from the narrower and more workoriented types of training. In any cost-benefit
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE 2001, and when Ghana Airways stopped services, Kenyan Airways and Slok Air (originally from Nigeria but now from the Gambia) quickly replaced and augmented the capacity that had disappeared. Because nine carriers provided a relatively evenly balanced set of seat capacities, the Herfindahl index shows the sector to be somewhat more competitive (table 7). This may be the result of there being no domestic carrier; due to the size of the country all scheduled services are international and intercontinental.

**Figure 12. Evolution of capacity and city pairs in Sierra Leone**

<!-- Start of picture text --> a. Capacity b. City pairs<br>450,000 12<br>400,000<br>10<br>350,000<br>300,000 8<br>250,000<br>6<br>200,000<br>150,000 4<br>100,000<br>2<br>50,000<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total Total<br>International International<br>Intercontinental<br>Intercontinental excluding flights between NA and SSA<br>City Pairs<br>Number of seats<br><!-- End of picture text --> Source: Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Note: As reported to international reservation systems.

# **Challenges**

Airport accessibility needs to be improved. Sierra Leone‘s main airport at Freetown is an island, requiring passengers to take ferries to reach the mainland.

Aging aircraft and air safety also pose a concern. The average age of aircraft serving Sierra Leone is high; close to half of the total aircraft are obsolete. The aircraft age may be related to the
to a university. The use of 2007 TLSS data allows me to include a set of controls for ethnicity and education level of his/er parents. Such control variables are not available in the 2003 data.

Next, to estimate whether students who lost on their education during the war were able to catch up between 2003 and 2007, I use pooled data from the 2003 and 2007 TLSS. To be consistent between the surveys, in the regression analysis of the pooled 2003 and 2007 data, I use the answers to the survey question on the highest level of schooling completed. In both surveys this question appears in Module 3, part B, question 5: “What is the highest diploma you have obtained? (do not include incomplete degrees)” with the following categories: none; primary (grades 1-4); basic (grades 1-8(9)); secondary general (grades 9-10(11)); secondary special; secondary technical; higher education; graduate school/aspirantura. Table 1 reports results of regressions using the 2007 TLSS data that control for an individual’s ethnicity and rural residence. All regressions include fixed effects at the raion level and are estimated with robust standard errors. The results from these regressions provide a longer-term perspective of the effect of the conflict on schooling as the 2007 data were collected about nine years after the end of the war in 1998. Two dependent variables are being used. The first dependent variable is equal to one if an individual completed at basic level of schooling or more and the second is equal to one if an individual completed secondary school or more (zero otherwise).

Looking at these two levels of completion separately allows us to understand at what level of education the conflict had the most impact in the long-run. The main coefficient of interest is the one estimated on the interaction between the war cohort dummy (born in 1976-1985) and living in an area more affected by the conflict.

The regression results (Table 1, Panel B: Col. 5
drier‐than‐usual conditions, indicating the vegetation health is also below‐average. NASA’s MODIS Terra and Aqua platform provides the daily global NDVI data at 500m resolution, which serve as the source of data for this analysis (Schaaf, 2015). While four consecutive rainy seasons delivered poor rains in 2016 and 2017, the severe rainfall deficits in the second rainy season of 2016 and first rainy season of 2017 were the key drivers of the 2016/17 drought in Somalia (FEWSNET, 2018).<sup>2</sup> Hence, each household’s level of drought exposure is defined in this analysis as the percentage deviation of the NDVI during these two seasons from the pre‐drought 2012 to 2015 average,<sup>3</sup> within a 25km radius around each household. The levels of drought exposure range from NDVI values of 6 percent above average to 20 percent below average in wave 1, and from 4 percent above average to 36 percent below average in wave 2, reflecting the overall spectrum of drought severity (Figure 1).

> 2 Somalia has two main rainy seasons: the main Gu rains from April to June and the short Deyr rains from October to December. Significantly below‐average rainfall started with the 2016 Gu rains and extended to the 2017 Deyr rains.

> 3 In 2010‐11, there was a severe drought in Somalia. The reference period was chosen to start after the 2010‐11 drought to preclude this unusual event from interfering with the series average, following World Bank (2018).

3
<!-- Start of picture text --> Figure 5. Linkage to manufacturing GVCs (%): Oil rich country cases<br>100<br>90<br>80<br>70<br>60 59<br>50<br>40<br>33<br>30<br>20<br>10<br>0<br>-10 Cameroon Nigeria<br>-20<br>-30<br>Upstream 2015 Downstream 2015 Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

<!-- Start of picture text --> Figure 6. Linkage to manufacturing GVCs by industry (%): Benchmark<br>group<br>100<br>90<br>85<br>80<br>75<br>70<br>65<br>60 62<br>50 49<br>40<br>33<br>30 29<br>20 20<br>10<br>0<br>-10<br>-20<br>Upstream Downstream Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC linkage 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

27
to changes in markups via competitors’ prices, and the other half due to changes in marginal costs due to imported intermediates. However, foreign competitors’ prices are likely to be measured with some error (e.g. sampling error, or the

> 12I tried several imperfect proxies, which were either inconclusive, or failed to point to a large role of imported intermediates. Specifically, I tried to construct imported intermediate weights using the World Input Output Database (WIOD.org) and then create an imported-intermediate-weighted exchange rate (IIWER). However, the IIWER was highly collinear with competitors’ RERs, leading to over-fitting when both variables were added to the regression. In an earlier draft, I also tried interacting the coefficients of interest with a dummy variable if the country × two-digit sector had an above average imported intermediate share (using data from OECD STAN). The interactions terms were insignificant.
subsidies relative to the pre-COVID era is indicative of the potential effect of COVID on household electricity consumption if the subsidies were not offered.

Our second strategy exploits the differences in the duration of the subsidy across residential user types to estimate the effect of the subsidy on consumption. Given that the subsidy for non-lifeline customers expired after the third month, while lifeline customers continued to enjoy the subsidy, we leverage the removal of the subsidy for the non-lifeline customers to conduct a "conventional" difference-in-difference analysis by using consumption data of the two groups between April-December, 2020 and comparing the differences in consumption between lifeline and non-lifeline customers before and after July 2020 when the subsidy for non-lifeline customers expired. So essentially, we estimate the effect of subsidy removal to identify the extent to which subsidies contributed to the stark increase in electricity consumption in Ghana as shown by the preceding results. Note that

> 24 _._ 494 ( _exp −_ 1) _×_ 100 = 63 _._ 8%

> 25An exception is month _t_ = 8, i.e. December 2020, where we see a spike in consumption.

14
workers than on the number of hours per listed plot, as the omitted workers are the ones working relatively few hours.

# **6. Conclusion**

Measuring labor in agriculture is a key input to the analysis of agricultural labor productivity, which in turn is an important metric in the design of policies for developing countries. Nevertheless, research on the best methods to measure agricultural labor is scarce. We contribute to the literature on measuring agricultural labor by analyzing the data of a randomized survey methodological study conducted in Ghana. The study was designed to allow for the comparison of estimates of farm labor obtained from a recall survey conducted at the end of the season with data collected weekly throughout the season.

The results indicate that the recall method overestimates farm labor per person per plot by about 12 percent, conditional on differences in baseline characteristics. This recall bias in farm labor is accounted for by listing bias, as households in the recall group report significantly fewer marginal plots and farm workers. Moreover, even though farm labor per person per plot is overestimated, the recall bias is very different at higher levels of aggregation. Because recall households report too few plots and too few workers, the end-of-season recall method _underestimates_ farm labor per plot and total farm labor per household. As a result, plot-level estimates of labor productivity in agriculture are significantly overestimated by the recall method.

We also moved beyond proxy determinants to understand the deeper forces behind recall bias. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, we find that better educated households recall farm labor with greater accuracy. This educational gradient – together with variations in the design of the experiment – most likely explains why this study finds much lower recall bias in farm labor in the Ghanaian context than the previous study found for the Mara region in Tanzania (Arthi et al. 2018). We find that more educated households are better able to accurately list all their plots at endline. Additionally, we find no significant recall bias
Nov/Dec 2022 45.7 22.4 17.5 14.3<br>Mar/Apr 2023 27.1 22.7 25 25.2 Mar/Apr 2023 44.5 22.7 16.7 16.1<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Ouest Central Southern Northern Ouest Central Southern Northern<br><!-- End of picture text --> Note: Results for 2021 are from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Results for 2023 are from entire sample of wave 4.

**Figure 5** **_:_ Percentage distribution of households in the two bottom and the middle -and high-consumption quintiles by region (2021-2023)**

Note: Results for 2021 are from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Results for 2023 are from entire sample of wave 4.

8
.”

- World Bank. 2006. “Nepal Resilience Amidst Conflict An Assessment of Poverty in Nepal, 1995-96 and 2003-04.” Available at: http://documents.worldbank.org/curated/en/2006/06/6919674/nepalresilience-amidst-conflict-assessment-poverty-nepal-1995-96-2003-04.

- World Bank. 1991. “Nepal: Poverty and Incomes.” Available at: http://wwwwds.worldbank.org/servlet/WDSContentServer/WDSP/IB/1999/09/17/000178830_9810190148263 7/Rendered/PDF/multi_page.pdf.

- World Bank. 1999. “Nepal: Poverty at the Turn of the Twenty-First Century Main Report and Background Studies.”

- World Bank. 2014. _Poverty and Inequality Measures in Practice: A Basic Reference Guide with Stata Examples_ . Washington, D.C.

- World Bank. 2016b. “The World Development Indicators.” Available at: http://data.worldbank.org/datacatalog/world-development-indicators.

World Bank. 1989. _World Development Report 1989 : Financial Systems and Development_ .

35
Extra-curriculars**||||||
|EXC1<br>~~B~~AND|School ofers|0.4710|40044|0.1678|4882|
||Band, orchestra or choir|(0.4992)||(0.3737)||
|EXC2<br>~~P~~LAY|School ofers|0.5928|40122|0.8509|4882|
||school play/musical|(0.4913)||(0.3562)||
|EXC5<br>~~M~~CLUB|School ofers<br>mathematics club|0.453<br>(0.4978)|40154|0.2687<br>(0.4434)|4882|
|EXC6<br>~~M~~ATHCOMP|School ofers<br>Mathematics competition|0.6268<br>(0.4837)|40215|0.8032<br>(0.3977)|4882|
|EXC10<br>~~S~~PORT|School ofers|0.9321|40581|0.992|4882|
||sporting activities|(0.2516)||(0.089)||
|**Leadership account**|**ability and autonomy**|||||
|SCORE<br>~~P~~UBLIC|Achievement data<br>posted publicly|0.345<br>(0.4754)|40965|0.7567<br>(0.4291)|4882|
|SCORE<br>~~A~~UTHRITS|Achievement data<br>tracked by authority|0.8003<br>(0.3998)|41139|0.8282<br>(0.3773)|4778|
|SCHAUTON|School Autonomy|-0.2542|41394|-1.0419|4882
14

# **IV.B How Reforms Affect Banking Systems**

How have reforms to bank regulations and supervisory practices affected national banking systems? In countries that changed their regulatory policies, have these reforms boosted banking system development? Have these policy changes enhanced the efficiency of intermediation and reduced the fragility of the banking system? Answers to these questions will help some countries adjust their reforms and help other countries avoid mistakes and select more appropriate reform strategies. We should point out that it is of course early to make these assessments. No one would argue, for example, that changes to financial regulation and supervision in 2011 would have an immediate impact on financial development, the efficiency of banking or the fragility of the system, yet 2011 is the latest for which we can get measures of the outcome variables.

In light of data constraints, we first re-run the regressions of the relationships between bank regulations and banking-system fragility, development, and efficiency, using the Survey I data posted on the World Bank website, and we then re-run these regressions using the Survey IV data. Tables 1 – 4 present estimates of the relationships between various bank regulations and bank development, efficiency, and fragility. Although we employ the same control variables as in BCL (2004), there are some differences. First and most importantly, BCL (2004) uses the first principal component of the underlying survey questions to construct an index while in this paper we use the summation of the individual questions. Second, there are some small differences in sample size, as noted below, which emerge both because of revisions to the survey responses and because of the treatment of missing values in the underlying survey questions.

What has changed from earlier research? The first three rows of Table 1, which correspond to the three pillars of Basel II, show no impact of capital regulation on bank development or either measure of efficiency, and some effect in the right direction of capital on fragility as measured by