rafmacalaba/data-use-mentions-extended huggingface dataset:
resources such as libraries, multi-media resources, science laboratory equipment, facilities for the fine arts, and computers were all found to be correlated with higher test scores (Fuchs and Wößmann, 2004, and Santos, 2007), although school infrastructure was found to be less important (Santos, 2007).

Among the most potentially influential factors leading to learning are teachers and school directors. Recent work in the United States underlines both the tremendous impact teachers can have, as well as the great variance of the performance among teachers, and the lasting consequences this can have on students. Students lucky enough to have great teachers can gain 1.5 grade levels or more, while students with poor teachers can end up mastering only half or less of the curriculum (Hanushek and Rivkin, 2010). Research from developing countries also corroborates the singular potential impact of teachers: Glewwe et al. (2014) find that, of 63 studies estimating the effect of teacher experience, 43 show no statistically significant impact. But of the 20 that do show an impact, 17 of them are positive. The evidence for teacher education is slightly stronger, as is the evidence for teachers’ knowledge of subject matter. As with computers, the evidence seems to indicate that effective interventions by teachers should be tailored to their skill level and provide them with specific guidance and support in their teaching (Evans and Popova, 2015).

Recent evidence from Latin America also points to the strong effect of the quality of teachers on learning outcomes. Using 2006 SERCE data, Bruns and Luque (2014) show that, within the same schools, large variations exist in student learning outcomes between classrooms, accounting for more than 40 percent of the total variation in Nicaragua and Cuba. Although these measures probably reflect policies of streaming children by ability into different classrooms, such large learning gaps are revealing of how being assigned to different teachers influences student 4
- Wilkinson, G. G. (1996). A review of current issues in the integration of gis and remote sensing data. _International Journal of Geographical Information Systems 10_ (1), 85–101.

- Wineman, A., N. M. Mason, J. Ochieng, and L. Kirimi (2017). Weather extremes and household welfare in rural Kenya. _Food Security 9_ , 281–300.

- Yeh, C., A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke (2020). Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. _Nature Communications 11_ , 2583.

- Zandler, H., T. Senftl, and K. A. Vanselow (2020). Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia. _Scientific Reports 10_ , 22446.

30
sup>25</sup> ) on private finance.<sup>†</sup> _Table 2: Percentage of consultations, admissions, physicians and facilities in public sector_<sup>_‡_</sup>

||Doctors<br>per 10,000<br>persons|Beds per<br>10,000<br>persons|Ambulatory<br>consultations<br>% public|First-level<br>physicians<br>% public|Hospital<br>admissions<br>% public|Hospital<br>beds %<br>public|Hospit<br>als %<br>public|
|---|---|---|---|---|---|---|---|
|Hong Kong||||||||
|(China), China|15.8|52|28%|45%|82%|86%|76%|
|Japan|20.2|129||36%||33%|20%|
|Korea, Rep.|19.4|66||10%||10%|10%|
|Singapore|15.0|28|20%||74%|81%||
|Taiwan (China),||||||||
|China|14.0|59||3%||32%|15%|
|OECDaverage|28.0|60||||||

The five systems all share a common feature, namely that their ambulatory facilities offer a wider range of services than a typical ambulatory facility in the West. In Singapore, for example, ambulatory clinics offer X-rays, clinical laboratory tests, and some rehabilitative services.<sup>27</sup> In Japan, many physician offices (one third of the total) even have a
reductions for workers in jobs incompatible with remote work was higher for women than for men. On the other hand, the estimated coefficient of the interaction between gender and WFH is negative but very small and not statistically significant, implying that the possibility of working from home did not have a differential effect on incomes of men and women who continued to work during the pandemic. Additionally, the results in columns 2, 3 and 4 suggest that income reductions are not associated with the presence of children at home for these workers.

Another interesting result that emerges from Table 9 is that the estimated coefficient of the _Employee_ dummy is negative, large in absolute value, and highly significant in all the specifications, indicating that salaried workers were less exposed to income reductions than self-employed workers.

> 20We would also like to assess the effects on hours worked but unfortunately this information is not available from the HFPS.

> 21These results should be taken with caution because the dependent variable _Income fall_ presents a lot of missing data, especially for the second wave.

15
Bank||21,141|0|0|21,141|
|Total||40,179|3,105|5,829|49,113|
|Other banks||3,956|491|14,933|19,380|
|District total||44,135|3,596|20,762|68,493|

_Panel B: Sample Frame by Bank and District_

||District|||||
|---|---|---|---|---|---|
||Anand|Kheda|Gandhinagar|Mehsana|Total|
|Bank of Baroda|276|276|35|70|657|
|Bank of India|14%|8%|7%|7%|9%|
|Central Bank of India|84|95|33|34|246|
|Dena Bank|10%|11%|10%|8%|10%|
|State Bank of India|215|39|25|16|295|
|Union Bank of India|16%|5%|10%|6%|11%|
|Kaira District Coop Bank|1,442|1,170|||2,612|
|||12%|||12%|
|Total|2,515|2,117|410|512|5,554|
|||12%||13%|9%|

56
0.28|0.65|
|**IV. Growth prospects:**|**2010-1015**|3.58|3.33|3.22|
|_Source:_Authors’ calculations based on<br>**V. Growth assumption**|data from the World|Development In|dicators database. Growt<br>3.57|h prospects are from the<br>3.02|

_Source:_ Authors’ calculations based on data from the World Development Indicators database. Growth prospects are from the IMF’s World Economic Outlook (2010).

_Note:_ The growth assumption is growth during reference period plus one standard deviation, or 1.8. Growth rates are all simple averages, giving equal weight to each country GDP per capita, purchasing power parity constant 2005 international dollars.

21
Policy Research Working Paper 6027

# **Abstract**

There is general agreement that the existence of participatory institutions is a necessary condition for accountability, especially where top-down institutions are malfunctioning or missing. In education, the evidence on the effectiveness of participatory accountability is mixed. This paper argues that participation is a social dilemma and therefore depends, at least partly, on individuals’ propensity to cooperate with others for the common good. This being the case, the mixed evidence could be owing to society-level heterogeneities in individuals’ willingness and ability to overcome collective action problems. The authors investigate whether individuals’ propensity to cooperate plays a role in parents’ decisions to participate in both a school accountability system—a “short route” to accountability—and parliamentary elections—a “long route” to accountability— by combining survey data on 1,800 individuals’ participation decisions with measures of their willingness to contribute to a public good in the context of a very simple, clearly defined laboratory experiment. They conduct a study in a new democracy, Albania, involving parents of children enrolled in primary schools. The findings confirm that, both across individuals within communities and across communities, the decision to hold teachers and school directors accountable _directly_ through participation at the school level, and _indirectly_ through political participation correlates with cooperativeness in a simple public goods game.

This paper is a product of the Albania Accountability for Better Governance Program, conducted by the Human Development Economics Unit, Europe and Central Asia Region. 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://econ.worldbank.org. The author may be contacted at tpackard@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those
consistent with targeted action since female students have lower test scores at baseline.

Finally, data from the school inspections suggest an improvement in teaching preparation and practice. Teachers in the in-kind treatment arm made better use of resources in the classroom, were better prepared for the class, and implemented better teaching practices. Consistent with the results on student learning, the impacts on teaching practice are no longer statistically significant at endline.

Our paper adds to a growing literature with experimental evidence from teacher pay-forperformance interventions in low- and middle-income countries. This literature documents a range of findings: some studies find no impacts (Barrera-Osorio and Raju, 2017); others document positive impacts (Behrman et al., 2015; Cilliers et al., 2018; Filmer et al., 2020; Leaver et al., 2020; Loyalka et al., 2019; Muralidharan and Sundararaman, 2011); and others have shown impacts only in the presence of complementary resources (Gilligan et al., 2018; Mbiti et al., 2019). Last, one study finds impacts on the incentivized test, but no evidence of impacts on any other learning outcomes (Glewwe et al., 2010). In contrast, null and negative impacts have been typically been found in experiments in high-income settings 3
expansion of exports for beneficiary countries, for products in general and for apparel specifically. The MFA end allows us to assess whether any expansion in apparel exports persisted beyond the reduction in trade preferences.

The paper utilizes a highly disaggregated trade and tariff database spanning 26 years, which we constructed by combining US import data from the US Census Bureau with US tariff data published by the US International Trade Commission (USITC) at the HS 8-digit tariff line level, aggregated to the 6-digit level for the econometric estimation. The sample includes product-level data for 208 exporting countries over the period 1992-2017, with a total of 27 million observations.

To identify the causal impact on African countries’ exports, we take a treatment and control group approach. This approach relies on several variants of the triple-differences specification with a highly stringent set of fixed effects following Frazer and Van Biesebroeck (2010). The estimators identify the impact of GSP LDC and of AGOA based on the growth in exports to the US for eligible countries of eligible products, relative to the growth in exports to the US for the control group, which includes non-eligible products in eligible countries; non-eligible products in control countries; and eligible products in control countries. The specification controls for country-product fixed effects, so impacts are identified relative to average pre-AGOA exports to the US of that country-product; country-year fixed effects, so impacts are net of (supply) shocks to overall exports to the US from a country and net of macroeconomic shocks (such as the Great Recession); and product-year fixed effects, so impacts are net of shocks to overall US imports of a product (due to changes in US preferences or global technological/supply shocks). A concern with the triple-differences research design is differential pre-treatment dynamics. Estimates of the GSP LDC and AGOA impacts could be spurious if there were differences in the dynamics of exports to the US of eligible countries and products before the enactment of these trade preference 4
||MAC|parameter|MIC parameter|
|---|---|---|---|
||<sup>_m_</sup><br><br>|$/tCO2<br><br>�|_c_<sup>_m_</sup><br><br>�<br>$/tCO2<br><br>�|
||_γ_<br>_i_<br>|GtCO2_/yr_<br>|_i_<br><br>GtCO2/yr<sup>3</sup><br>|
|Waste||34|3732|
|Industry||17.6|1932|
|Forestry||15.9|1745|
|Agriculture||11.9|1306|
|Transport||11.6|1273|
|Energy||10.3|1131|
|Buildings||3.6|395.2|

Table 3: Calibration of the Marginal Investment Cost (MIC) functions and of the Marginal Abatement Costs (MAC) functions. This gures are consistent with our modeling framework (18, 20) and the data from IPCC reported in the Tab. 2.

We also ensure that relative costs (when comparing two sectors) are equal in the two models. This way, dierences in optimal strategies arise only from the introduction of economic inertia.

We rst solve the problem exposed in section 2, assuming quadratic abatement costs. Quadratic costs are a simple specication that grants that the _γi_ are convex, and simplies the resolution as marginal abatement costs are linear: Where _γi_<sup>_m_</sup> are parameters specic to each sector. We calibrate these costs functions using the abatements corresponding to a 20 $/tCO 2 marginal cost in gure SPM.6 of IPCC (2007) presented in Tab. 2; the result of this calibration is presented in the rst
## **Results: One-year impacts**

As explained above, given the nature of the HFPS data, we can analyze income loss after the first year of the pandemic only for Uganda and Nigeria. The results for one-year income loss (Table 4) show that the only demographic factor significantly related to income loss is the interaction between female household head and children aged 5-18 (significant at the 10 percent level). Compared to male-headed households, income loss was nine percentage points more likely in female-headed households with children age 5-18. In addition, income loss by early 2021 is associated with the different sources of income households relied on during the 12 months before HFPS R1. NFE income and income from domestic remittances are associated with higher probability of income loss. In the medium run, these were the most vulnerable sources of income, relative to farm income, wage income, and remittances from abroad.

# **6. Discussion and conclusions**

In this paper, we analyze high frequency phone survey data from Ethiopia, Malawi, Nigeria, and Uganda to document the impacts of the COVID-19 crisis on employment and income. We focus on heterogeneity by gender, family composition, education, age, job industry, and between the rural and urban sectors. For employment, we compare the early-phase impacts (up to July 2020) with the medium-term (up to February/March 2021).

By July 2020, job loss had affected 18% to 30% of respondents in Ethiopia, Malawi, and Uganda, and a striking 61% of respondents in Nigeria. In all countries, with the exception of Ethiopia, the rate of job loss continued to increase after July 2020. Even without necessarily losing employment, many households were affected in terms of income. Between March and June 2020, 46% of respondents in Ethiopia and more than 75% of respondents in Malawi, Nigeria, and Uganda reported a loss of household income. After one year into the pandemic, 41% of respondents in Nigeria and 49
Policy Research Working Paper 10985

# **Abstract**

Since 2000, China has been upgrading its infrastructure, exemplified by the expansion of the high-speed railways (HSR), while simultaneously moving up the global value chains, evidenced by the rising domestic value-added ratio (DVAR) in exports. To analyze the impacts of the HSR on China’s DVAR, this paper develops a new methodology to estimate firm-level DVAR using only customs transaction data, without relying on the industry input-output tables or matching firm-level industrial census data. This paper also proposes a novel way to capture firm-level input-output linkages by matching the custom product codes of importers and exporters. The results confirm that the HSR increases the DVAR through firm linkages by connecting downstream exporters with upstream domestic suppliers. A two-sector model shows that, by improving the probability of exporters connecting with low-communication cost domestic suppliers, the HSR decreases domestic material prices and increases the variety of accessible domestic materials, hence pushing up DVAR.

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 hlkee@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 Team
levels (around 5 years of education) (see Ib ́a ̃nez and Moya, 2006; Garay, 2008; Carrillo, 2009).

Several studies have attempted to characterize the migration decision of forced migrants using surveys. Their findings suggest that forced displacement in Colombia mostly originates from rural areas, where the internal armed conflict has taken place. In that sense, the migration decision of forced migrants is mainly driven by safety concerns related to the presence and activities of illegal armed groups. In particular, data from RUV suggests that for the 59 percent of individuals for whom information is available on the cause of displacement, 54 percent of migration cases were attributed to the activities of illegal armed groups and 84 percent were attributed to a death threat (Unidad para la Atenci ́on y Reparaci ́on de V ́ıctimas, 2013). Most displaced individuals, consequently, had previously worked primarily in agriculture (Ib ́a ̃nez and Moya, 2006; Carrillo, 2009). Households that had access to basic public services, had better economic opportunities, or had private property showed a lower probability of migrating (Engel and Ib ́a ̃nez, 2007; Ib ́a ̃nez and Moya, 2010). In areas with extreme levels of violence, however, owning land increased the probability of displacement because these household were targeted for extortion by illegal armed groups (Engel and Ib ́a ̃nez, 2007).

Forced migrants in Colombia have moved to areas where they had friends or relatives and that were closer in distance to their municipalities of origin (Ib ́a ̃nez and Moya, 2006; Carrillo, 2009; Lozano-Gracia et al., 2010). Yet, in regions with extreme violence, individuals preferred to relocate to more distant locations and to cities that were more populated; they were attracted to the sense of anonymity both provide (Carrillo, 2009; Lozano-Gracia et al., 2010). Other criteria migrants take into account when choosing their destinations includes the provision of
35

**Table 6: Regression on private sector attitudes, and wanting to work**

||(1)<br>Okay to work in<br>private|(2)<br>Okay to work in<br>private|(3)<br>Prefer Private|(4)<br>Want to work|
|---|---|---|---|---|
|Social empirical|0.0262**||0.00389|0.0574***|
||[0.0104]||[0.0121]|[0.0199]|
|Social normative|0.0517***||0.00649|0.0188|
||[0.0106]||[0.0123]|[0.0196]|
|Personal Belief||||0.0526**|
|||||[0.0224]|
|Expectation of counterpart|0.132***||0.0280**|0.0383*|
||[0.0107]||[0.0125]|[0.0210]|
|Social empirical index||0.0397***|||
|||[0.0109]|||
|Social normative index||0.0298**|||
|||[0.0133]|||
|Expectation of counterpart|||||
|index||0.0918***|||
|||[0.0134]|||
|Female|0.104***|0.104***|-0.00715|0.0908**|
||[0.0209]|[0.0219]|[0.0243]|[0.0412]|
|Age|
using equal weights.

We call “objective” the indices that are constructed using the joint distributions of outcomes. Objective indices first aggregate across dimensions at the individual level to ˆ generate an individual index u(di). Then, the individual indices are aggregated across individuals, typically by averaging them. This is for instance the case of the two main global multidimensional poverty indices: the global MPI of UNDP-OPHI and the World Bank’s MPM, both defined in Appendix 6.1. These indices generate their individual indices uˆ(di) following the Alkire-Foster aggregation methodology (Alkire and Foster, 2011a). This methodology makes a weighted sum of dichotomous deprivation statuses in a number of dimension-specific variables (see Section 3.2).

From a conceptual perspective, objective indices are more appealing than synthetic indices because the former account for well-being inequalities across individuals (Fleurbaey, 2009). Synthetic indices ignore that some individuals cumulate low achievements in several dimensions. Moreover, synthetic indices do not generate an individual index uˆ(di) and can therefore not be used for inter-personal well-being comparisons. However, synthetic indices can be computed even when the joint distribution is missing.

Consider then the columns in Table 1. In absence of data on preference u, the typical practice consists in using ad-hoc methods to set weights, like the popular “equal-weights” procedure.<sup>12</sup> The risk when resorting to this ad-hoc procedure is to select weights that are not plausible, which casts doubt on the added-value of the multi-dimensional indicator (Ravallion, 2012). Going beyond ad-hoc methods may help increase the confidence in well-being comparisons obtained from multidimensional indicators. This requires selecting the values of weights from some empirical source, like dedicated preference-elicitation surveys (willingness-to-pay, stated preferences, etc.), subjective well-being data or external sources in the literature. For instance, the dimension-specific statistics feeding synthetic indices are sometimes aggregated using a parametric utility function. Its parameters can be calibrated using empirical values taken from specialized literatures. For the
Perspectives, 11(3), 105–118.

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- Cancho, Cesar A., Davalos, Maria Eugenia, Sanchez‐Paramo, Carolina, 2015, Economic mobility in Europe and Central Asia: exploring patterns and uncovering puzzles. Policy Research Working Paper 7173; World Bank, Washington DC.

- Cancho, Cesar; Davalos, Maria E.; Demarchi, Giorgia; Meyer, Moritz; Sanchez Paramo, Carolina. 2015b. Why so gloomy? perceptions of economic mobility in Europe and Central Asia. Policy Research Working Paper 7519; World Bank, Washington DC.

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