rafmacalaba/data-use-mentions-extended huggingface dataset:
non-oil imports in our data are very similar to the corresponding total non-oil imports reported by COMTRADE (the average difference across country-years is 5.6%). COMTRADE data is accessed through WITS (World Bank, 2024b).

Measuring import market concentration requires defining a relevant market, or the set of products over which the firms in question have market power. While relevant market definition is the object of intense debate in antitrust litigation, a general principle is that it should include the set of goods that are close substitutes for the same set of consumers (Davis and Garc ́es, 2009). Benkard, Yurukoglu and Zhang (2021) note that economic census data, whereby firms are classified into industries, are collected at the point of production rather than consumption, and so may be less useful for analyzing the relation between market concentration and market power. In contrast, the Harmonized System (HS) product categories defined by the United Nations used in trade data classify goods with a similar end-use, and so are conceptually like the relevant product markets in antitrust analysis. Following Fernandes et al. (2016), we use a time-consistent consolidated classification that concords and harmonizes product codes across the HS revisions present in the raw data.<sup>5</sup> Our baseline measure of import market concentration is the Herfindahl-Hirschman Index (HHI) or where Mc,j,i,t is the import value of firm j in country c of product i in year t, J is the number of firms and Mc,i,t the total import value in the relevant import country, product market, and year. Values are measured including cost of freight and insurance (CIF) in United States dollars. Table S1 reports average and median HHI across HS 6-digit products by import country. We consider alternative measures of concentration: the share of the largest importing firm or the largest four importing firms, the number of importing firms, and the number of source countries, all defined by import country, product market
# **Table A11. Associations between SCP Quality Determinants and Academic Outcomes** **_Using Program-Level Data for all Survey Countries_**

|_Dependent variable:_|_Dropout_<br>_Rates_|_Extra time-to-_<br>_graduate_|
|---|---|---|
||(1)|<br>(2)|
|_Costs_|||
|HEI scholarships are used by some students|1.438<br>(0.876)||
|Loans b banks are used b some students|-1.284|2.595*|
|y y|(0.834)<br>|(1.555)|
|HEI has received funding from government|1.309*<br>(0.739)<br>||
|HEI has received funding from private sector|1.466*<br>(0.815)||
|||-0987**|
|Annual tuition (2019 PPP USD, log)||.<br>(0.427)|
|_Training and curriculum_|||
||-2.111**||
|Curriculum is fixed|(0.899)||
|Offers remediation classes during the program|-1.003<br>(0.782)||
|Years since last update to curriculum|-0.0439<br>(0.111)<br>||
|Enrollment trends are very important to update program|-1.122<br>(0.703)||
|||-7.358***|
|Promotes work under hardship or pressure||(2.236)|
|Test required for graduation||1.988<br>(1.623)|
|Thesis or research project required for graduation||3.153*<br>(1.778)|
# **5 The impact of COVID-19 on electricity consumption in India**

## **5.1 Modeling daily electricity consumption**

To understand the magnitude of the decline of electricity consumption due to the lockdown, we need to control for factors affecting electricity consumption like the season and the weather. We hence estimate the following model of electricity consumption using daily (t) data:

### log _Electricityt_ = _τt_ + _DWt_ + _WYt_ + _Holidayt_ + _β_ 1 _Coolingt_ + _β_ 2 _Heatingt_ + _βtTrend_ + _εt_ (2)

The explanatory variables are a set of fixed effects that control for the day of the week, _DWt_ , the week of the year, _WYt_ , and holidays, _Ht_ . We include two variables that control for the daily temperature. We control for the temperature degrees above and below of 22 _._ 8<sup>_◦_</sup> _C_ , such that _Coolingt_ = _max{tempt −_ 22 _._ 8<sup>_◦_</sup> _C,_ 0 _}_ and _Heatingt_ = _max{_ 22 _._ 8<sup>_◦_</sup> _C − tempt,_ 0 _}_ , respectively. This temperature is associated with the minimum electricity consumption in India, which we confirmed by regressing log electricity consumption on temperature and a quadratic term of temperature suggesting that electricity consumption increases above and below this temperature (see Appendix B). The variables of interest are the set of dummy variables _τt_ that indicate each day in 2020 until the last day of the sample. These variables capture how daily consumption in 2020 differs from consumption in previous years, conditional on the temperature and holidays.<sup>15</sup> As discussed above, electricity consumption in India grew over time and hence we include a linear time trend (trend). This model is estimated separately for India and for all Indian states and Union territories.

Table 2 presents the estimation results for different
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* Percent of governing body that belongs to: current students @<br>Almost all or all faculty participated in professional training last year<br>* Percent of faculty working in the industry<br>Industry has internship agreements with HEI @<br>Communicate with local firms about their needs more than once per year @<br>Industry has agreements to train faculty @<br>Loans by banks are used by some students e<br>Collect student satisfaction data more than once per year @<br>Student feedback is very important to update program @<br>Government standards are very important to update program e<br>* Years since last update to curriculum e<br>Test required for graduation @<br>Analyze student performance to solve problems more than once per year @<br>Maintenance of largest lab: every year<br>Program offers at least one online class<br>-1 -.05 (e) 05 J 15<br>Percentage points<br>Infraestructure @ Training and curriculum © Costs<br>@ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text -->

<!-- Start of picture text --> Update / review admin data more than once per year @<br>Interview is an admission requirement e@<br>Class planning is very important in faculty evaluation<br>Students and peers’ informal comments are very important in faculty evaluation<br>* Percent of faculty with graduate degree<br>Classroom observation is very important in faculty evaluation<br>Peer evaluation is very important in the faculty evaluation<br>Evaluate faculty more than once per year<br>HEI trains students for job interviews e@<br>Collect data on employment or employers' satisfaction more than once per year @<br>HEI providesjob market information e@<br>Loans
# Firms in Global Value Chains during Covid-19: Evidence from Indonesia

Devaki Ghose<sup>*</sup> Angella Faith Montfaucon<sup>†</sup> World Bank World Bank

## _JEL Classification_ : F13, F14, F42

_Keywords_ : Trade policy, global value chains, resilience, non-tariff measures

- Development Economics Research Group, The World Bank, dghose@worldbank.org.

> †Macroeconomics, Trade and Investment Global Practice, World Bank. amontfaucon@worldbank.org. The authors would like to thank Bayu Agnimaruto for playing a key role in updating the Indonesia non-tariff measures data, Chalisa Kallayanamitra and Jana Mirjam Silberring for excellent research assistance, and Csilla Lakatos and Ana Margarida Fernandes for helpful discussions. This paper has partially benefited from financial support from the Umbrella Facility for Trade trust fund, financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors or the countries they represent. All errors are our responsibility.
must fewer changes in ordinality for EO temperature data compared to rainfall data within a country, there again is little consistency across country. In Ethiopia and Uganda, CPC tends to produce the largest coefficients while in Malawi and Nigeria it is MERRA-2 and in Niger and Tanzania it is ERA5. But ERA5 produces the smallest coefficients in Ethiopia, Malawi, and Uganda, while MERRA-2 produces the smallest coefficients Niger and Tanzania. The EO product that produces the largest or smallest coefficients varies from country-to-country and again suggests that the accuracy of these products is a function of geography and climate. Not all products perform equally well across space. This means what results a researcher gets will depend on their choice of EO product as well as their country of study.

Taken together, the preponderance of evidence from our regressions leads us to conclude that our results are not robust to the choice of remote sensing EO weather product. The correlation between rainfall or temperature and agricultural productivity varies not only across countries but within a country based on the econometric specification or how productivity is quantified. Our findings show that different measurement methods from different EO sources result in different ordering of coefficients and not just a change in the relative size of the coefficients.

21
1 Below 1<br><!-- End of picture text --> Source: Haver, International Monetary Fund.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. Assessing Reserve Adequacy (ARA) metric is based on IMF (2011) and determines the appropriate reserve cover on a risk-weighted basis covering shortterm debt, medium and long-term debt, and equity liabilities, Broad money and export earnings. Risk weights are based on observed outflows during periods of exchange rate pressure.

B.-D. Based on data for 55 EMDEs.

B. Unweighted averages.

C. Mean is an unweighted average. Blue vertical bars represent the 90th percentile of the distribution.

D. Data for 2019. A value below one suggests that EMDEs do not necessarily have enough reserves to meet their balance of payments requirements as defined by the IMF’s ARA metric.
food and non-alcoholic beverages, while utilities expenditure is deflated using the consumer price inflation for energy (capturing housing, water, electricity, gas and other fuels). Remaining nonfood consumption expenditure is deflated using the headline average consumer price inflation. The year 2022 is used as the base year.

Hence, the consumption expenditures as measured in our experiment in 2022 are taken in their nominal values, while the expenditures in previous rounds are deflated. The expenditure values elicited during the TZNPS 2020/21, conducted between December 2020 and January 2022, are deflated in accordance with the 2021-2022 inflation. Similarly, the expenditure values elicited during the TZNPS 2019/20, conducted between January 2019 and January 2020, are deflated in accordance with the 2020-2022 inflation. In what follows, all expenditures are reported in year2022 Tanzanian shillings (TSH), and total annual consumption per adult equivalent is compared to the TZNPS 2020/21 poverty line deflated to prices in 2022.

Table 1 provides descriptive statistics for TZNPS 2020/21, 2019/20 rounds and for each of the survey treatment arms, coupled with the results from the tests of mean differences among the TAs. The “good” news is that across treatments, comparisons of the prospective poverty predictors that are required for Models 1, 2, 8 and 9 largely do not reveal statistically significant differences. The only exceptions are participation in wage work, and bicycle ownership, between TA 1 and TA 2; radio ownership, urban–rural residence and utility expenditures (though with marginal differences) between TA 2 and TA 3; and access to piped water, between TA 1 and TA 3. These findings are in stark contrast with those of Kilic and Sohnesen (2019)<sup>8</sup> and do not raise flags regarding the

> 8 Kilic and Sohnesen (2019) report on a randomized survey experiment that was conducted in Malawi in 2016 and that shows that <mark>observationally equivalent, as well as identical, households in fact answer the same questions differently
Policy Research Working Paper 5695

# **Abstract**

This paper analyzes previously unreleased firm-level customs transaction data from the Lao PDR in order to assess the determinants of cohort survival among exporters. The authors find that export flows in value terms are dominated by the intensive margin, with large firms continuing to supply the same products to the same markets. On the extensive margin, new export spells for firms, products and firm-product-destination units are very small and short-lived, suggesting that although there is significant experimentation and discovery by firms, there is only limited capacity to stay in markets once an entry is made. Regression analyses of the factors that influence survival past the first year reveal that this is positively correlated with the initial dollar value (starting big makes a difference) and is helped by the firm’s experience with the product and the destination, but hindered by a lack of focus. Agglomeration of exporters in the same destination with the same product is beneficial, an effect analogous to external economies of scale. The authors conclude by recommending that the focus of export promotion activity should be on helping existing exporters find and stay in new markets.

This paper is a product of the Poverty Reduction and Economic Management Sector Department, East Asia and Pacific 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 rrecord@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
, Guinea, Guinea Bissau, Kenya, Liberia, Mali, Malawi, Niger, Nigeria, Senegal, Sierra Leone, Sao Tomé and Principe, Eswatini, Togo, Uganda, Zambia and Zimbabwe. Internet average includes Cameroon, Benin, Burkina Faso, Chad, Cote d’Ivoire, Gabon, Ghana, Guinea, Guinea Bissau, Kenya, Mali, Senegal, Sierra Leone, Sao Tomé and Principe, Togo and Zambia.

**<u>Table 4. Urban Infrastructure and Services Correlates with Urban Informal Firms</u>**

||Low self-e<br>|mployed<br>|High self-e<br>|mployed<br>|Empl<br>|oyer<br>|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|Has grid electricity|-0.405|-0.242|0.050|0.01|0.355|0.232|
||[0.084]***|[0.112]**|[0.026]*|[0.016]|[0.085]***|[0.115]**|
|Has non-grid electricity|-0.234|-0.306|0.013|-0.005|0.221|0.311|
||[0.174]|[0.203]|[0.024]|[0.027]|[0.180]|[0.204]|
|Full water access|0.105|0.01|-0.064|-0.084|-0.041|0.074|
||[0.126]
and much further along in the process of structural change than previously thought. However, caution is warranted when jumping to such a conclusion, as agriculture may still be the primary means of livelihood for many of the individuals that are classified as employed in a non-agricultural sector under the 19<sup>th</sup> ICLS standards. Though not shown in this paper, this should be reflected by a large number of employed individuals simultaneously engaged in own-use production work in agriculture, which highlights the importance of considering employment and own-use production work together under the revised standards. Even in urban areas, the measured share of agricultural employment in total urban employment declines significantly, albeit from a much lower level. Hence, even though the 19<sup>th</sup> ICLS standards may not lead to a substantial decline in urban employment, due to the prevalence of secondary activities in urban areas, they may still alter the sectoral distribution of urban employment.

**Table 5: Employment statistics, Nigeria and Malawi**

|||**Nige**|**ria**|||**Mala**|**wi**||
|---|---|---|---|---|---|---|---|---|
||**Urb**|**an**|**Ru**|**ral**|**Urb**|**an**|**Ru**|**ral**|
||**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|
|**_Employment-populatio_**|**_n ratio_**||||||||
|
27 The primary sources of relevant data were pre-pandemic household surveys on availability of information and communication technologies (e.g. mobile phones, radio, television) and, in some countries, household phone surveys launched after the pandemic began. In Ethiopia, researchers financed by Research on Improving Systems in Education (RISE) and ELP mobilized data within weeks of school closures to demonstrate low and unequal access to radio and mobile phone technology, as well as possible constraints to parents in supporting learning at home (Kim & Rose, 2020). The same group completed a caregiver phone survey in August and September of 2020. They documented limited access to children’s books in homes, continued large urban/rural divisions in access to technology, very little contact between schools and parents, and increases in behavioral problems among ECE-age children (Kim et al., 2020). However, in Ethiopia’s case the lack of effective interventions seems to have impeded government action on these issues. The lack of an existing platform for distance learning at the ECE level was a major challenge, in contrast to primary education where radio programming already existed and could be adapted to the pandemic context.

In Liberia, by contrast, data were not readily available, but the country’s previous experience with distance learning during the Ebola crisis provided a head start on interventions. Public documents on the education response make few references to data, although the Emergency Response Plan (ERP) makes reference to collection of data and a monitoring and evaluation strategy. Among key informants, there was a consensus that radio-based ECE programming was a necessary effort to provide some level of service under difficult circumstances, and that the development of both materials and experience during the Ebola crisis was critical. However, the limitations of the medium meant that radio was not seen as a good substitute for in-person instruction. Key informants noted that the child-centered and play-based methodologies desired
Due to concerns about manipulation in the 2018 elections, we include the last presidential elections of 2024, for which we have results for over 83.5 percent of the country. The opposition parties collected and published these results, which should attenuate these concerns. 23 We also find consistent effects for mayoral elections (see Table E.15). Although the coefficients indicate smaller effects, they are negative and statistically significant. This is a remarkable consistency test since these estimates evaluate forced displacement outflows over five years of elections in 335 municipalities, totaling 1,675 independent municipal elections.

Figure 7 examines the timing of the electoral effects. Before 2013, municipalities with high and low foreign settlement shares followed statistically indistinguishable trends in both turnout and opposition support, consistent with the parallel trends assumption. The divergence emerges clearly after 2013 and intensifies following 2017, mirroring the two-phase pattern documented for economic outcomes. This timing is consistent with the accelerating exit of opposition-leaning citizens as the crisis worsened, progressively eroding the political base needed to challenge the regime.

Overall, our results consistently show that forced displacement reduced both electoral participation and opposition support, weakening the political mechanisms through which voters could demand accountability from their government. These findings connect directly to the economic consequences documented in the previous section. The municipalities that experienced the largest economic declines were also those where political opposition eroded most sharply, suggesting that forced displacement simultaneously undermined both the economic and political foundations of institutional accountability.

> 23The data come from Resultados con VZLA (2024).

35
>Brekelmans, S., and G. Petropoulos (2020). “Occupational Change, Artificial Intelligence and the</u> Geography of EU Labour Markets.” Bruegel Working Paper 2020/03 :33.

- <u>Browning, M., Crawford I. and M. Knoef (2012). “The Age-Period-Cohort Problem: Set</u> Identification and Point Identification.” _CEMMAP Working Paper_ CWP02/12, Institute for Fiscal Studies.

- <u>Bryson, A., Erhel, C. and Z. Salibekyan (2021). “Perceptions of non-pecuniary job quality using</u> linked employer–employee data.” _European Journal of Industrial Relations,_ 27(2):113-129.

- <u>Burgess, S. (1999).</u> _The reallocation of labour: an international comparison using job tenure_ (No. 416). Centre for Economic Performance, London School of Economics and Political Science.

- <u>Burgess, S. and H. Rees (1998). “A Disaggregate Analysis of the Evolution of Job Tenure in Britain,</u> 1975-1993.” _British Journal of Industrial Relations_ , 36(4): 629-655.

- <u>Burgess, S., Propper, C., Rees H. and A. Shearer (2003). “The Class of 1981: the effects of early</u> career unemployment on subsequent unemployment experiences.” _Labor Economics_ , 10: 221 – 262.

- <u>Bussolo, M., Davalos, M., Peragine, V., and R. Sundaram (2018).</u> _Toward a New Social Contract: Taking on Distributional Tensions in Europe and Central Asia_ . Europe and Central Asia Studies. Washington, DC: World Bank.

- <u>Caroli, E. and J. Van Reenen (2001). “Skill-Biased Organizational Change? Evidence from A Panel</u>
2012). Evidence of the effect of school-based management on student learning is more mixed and has generally employed weak evaluation techniques, though some recent research shows promising impact on Spanish in 3<sup>rd</sup> grade (Santibañez **,** Abreu-Lastra, and O'Donoghue, 2014).

In Argentina, the literature on the impact of the institutional setting on learning outcomes is also mixed. Regarding longer schools days, one study in Buenos Aires shows some positive results on secondary school graduation rates (Llach, Adrogue and Gigaglia, 2009), but impacts on learning weren’t estimated. Another study shows that schools in large cities had higher average test scores than other schools, and that top achieving schools were in large and medium-sized cities, while schools in villages and small towns had higher variation than schools in cities (AbdulHamid, 2007). Pupil-teacher ratios appear to have no effect (Fresoli et al., 2007).

One question that has garnered much attention in Argentina is whether the observed performance gap of private schools over public schools is attributable to the private nature of the school. Both Marchionni et al. (2013) and Albornoz et al. (2015), examining PISA 2009 and PISA 2009 and 2012 data respectively, conclude that when controlling for peer effects the performance advantage of private schools disappears. Formichella (2011), using data from PISA 2006, also finds that private school outperformance is in large part accounted for by having students who are more prepared to learn. The high segmentation of students, which is, to a lesser extent, also present in public schools, features prominently in the literature, and seems to be an important element behind the inequality of learning in Argentina (Kruger, 2011).

# **III. Country Context and Trends in Argentina’s Learning Outcomes**

_3.1 The Education Sector in Argentina_ Argentina was one of the first countries in the region to achieve universal primary education, and more recently saw rapid expansion of