rafmacalaba/data-use-mentions-extended huggingface dataset:
**|**1,797**|**1,498**|**-17**|**10,940**|**12,614**|**15**|
|Fish|862|654|-24|1,292|1,170|-9|
|Oil-bearing|379|343|-10|154|317|106|
|Nuts and derived products|79|108|36|253|202|-20|

5 To account for the changing dates of the Chinese New Year, we present the trade data for January and February 2020 combined and relative to their respective levels in 2019.

6 Data on food production in China for the first two months of 2020 are not yet available. The decline in food exports and the increase in imports over this period are suggestive of disruptions in domestic supply chains and a decline in food production in China. Although, other factors not related to Covid-19 also mattered, such as the swine fever that reduced China’s stocks.

> 7 See for instance: <u>https://www.freshplaza.com/article/9188594/chinese-exports-and-imports-feel-the-impact-of-coronavirus/</u> 8 Indeed, news reports indicated that the authorities in China had concerns that the spread of coronavirus and the policies to mitigate it, which reduced the mobility of seasonal workers from northern provinces, would impact planting of rice in Hubei province, the epicenter of the crisis. See: <u>https://www.reuters.com/article/health-coronavirus-china-rice/china-calls-forexpansion-of-rice-production-amid-measures-to-contain-coronavirus-idUSB9N28G03R</u> and <u>https://www.ft.com/content/cafb828e-6423-11ea-b3f3-fe4680ea68b5.</u>

9 Appendix Figure 1 uses data for China in January and February 2020 and Appendix Figure 2 uses early data for the United States for March 2020 and show the correlation between the change in food exports (year on year) and the unskilled labor shares in total
assistance budget could also be utilized for selected NSO staff to attend training workshops and other international conferences/workshops relevant for the country-specific household survey work program.

It is clear that the budgeted and accrued technical assistance expenditures may vary across countries depending on 1) budget constraints, 2) overall survey design and complexity; 3) NSO capacity for questionnaire and sampling design, prevailing approach to fieldwork management and data quality control, capacity for data curation and analysis, and track record for data dissemination; and 4) donor aspirations for long-term capacity building. The variation may depend also on the fieldwork duration, which is assumed to be 12 months in Table 3, as well as whether the resident advisor will be specific to a given country or could be assigned regionally in circumstances when economies of scale could be achieved by implementing a regional household survey initiative. The latter, for instance, is likely a more sensible approach to technical assistance provision in the context of small island countries.

# **d. Projected growth in survey implementation and technical assistance costs**

Will the costs of survey implementation and technical assistance increase in _real_ terms over the course of the 15-year period (2016-2030)?<sup>18</sup> This is an empirical question for which there is little data that can offer an answer.<sup>19</sup> On the one hand, improvements in data capture, processing, and dissemination can increase the cost-effectiveness of data production. Also, technological improvements and improving training for enumerators might soften scarcity of qualified enumerators, especially in Africa where the survey implementation cost per household is significantly higher than in other regions. If technological improvements and improving training enable average labor to conduct data collection, the survey implementation cost in Africa should decline. On the other hand, survey costs could increase in real terms due to (i) increased complexity of questionnaire instruments and fieldwork protocols to meet demands for more and higher quality data collected at the individual-level on priority topics<sup>20</sup
growing sectors would be located within the TPP-RCEP block, excluding the United States, the European Union, and China.

<!-- Start of picture text --> Figure 10. Sectoral decomposition of exports in 2030<br>(percentage point differences with respect to baseline)<br><!-- End of picture text --> <!-- Start of picture text --> EU-IDN FTAAP TPP-15<br>Agriculture<br>Natural resources / mining<br>Food, beverages, tobacco<br>Textiles<br>Wearing apparel and leather<br>Chemical, rubber, plastic products<br>Metals<br>Transport equipment<br>Electronic equipment<br>Machinery and equipment nec<br>Other manufacturing<br>Utilities<br>Construction<br>Trade and transport<br>Finance and other business services<br>Communication and business services nec<br>Social services<br>-5 0 5 -5 0 5 -5 0<br>USA Other TPP TPP/RCEP joint members China Other RCEP members EU Rest of World<br><!-- End of picture text -->

# Productivity kick

The results described above do not incorporate gains in productivity due to trade liberalization and increased openness as the analysis only captures the reallocation of production to the most efficient sectors. However, several studies (e.g., Topalova, 2004; Amiti and Konings, 2007; Yu, 2015) show that lower import tariffs for final goods increase firms’ productivity by inducing tougher import competition, while cheaper intermediates can improve productivity via learning, variety and quality effects. In particular, Amiti and Konings (2007) find that a 10 percentage point fall in input tariffs leads to a productivity gain of 12 percent for Indonesian manufacturing firms that import their inputs. For Chinese manufacturing firms, Yu (2015) illustrates that a 10 percentage point fall in output (input) tariffs leads to a productivity gain of
consistency of many items and dimensions means that targeted quality enhancement actions often would target different schools using the two different measures. The results underscore how QAS are best implemented as a formative system, a starting point for quality enhancement, and not as a summative system that directly punishes or rewards specific schools, classes, or teachers.

# **2 Background**

# **_2.1 What we know about education quality assurance systems_**

QAS (for pre-primary and otherwise) have three main components: (1) quality standards, (2) tools for monitoring whether standards are being met, and (3) quality enhancement actions that follow from the results of the monitoring tool (Raikes, Neuman, and Burton 2019). As a concrete example, consider a pre-primary quality standard that teaching should be play-based. The monitoring tool would then, in this case likely using a classroom observation tool, collect data on whether a play-based pedagogy was being implemented. If the monitoring tool data indicated a teacher was not using play-based pedagogy, a potential quality enhancement action would be providing the teacher training on play-based pedagogy. Although this is the ideal tripartite system, it is important to note that countries may not have all these components in place; for instance in a recent study on pre-primary QAS in Africa, countries usually had at least one component of standards or tools, but only some had all components in place (Raikes, Sayre, and Lima 2021). There is particularly limited comprehensive information globally about quality enhancement components of QAS. While the SABER-ECD database includes 38 countries and looks at monitoring and standards for pre-primary QAS, it does not look at quality enhancement 3
# **3 Econometric Analysis**

In the following section, we examine the role of location for export participation using regression analysis. In particular, we study whether the determinants of exporting are different if firms are located in core versus non-core regions.<sup>8</sup>

## **3.1 Econometric Model**

We follow the theoretical model of exporting of Roberts and Tybout (1997). A firm _i_ ’s export propensity at time _t_ depends on a firm’s expected revenues _R_ and costs _c_ plus sunk entry costs of exporting, _S_ : where _ex_ denotes an export dummy at the firm-level. Sunk entry costs of exporting, _S_ , are 1 if a firm exported in period _t-1_ and 0, otherwise. In other words, a firm exports if expected profits _π_ > 0.

A firm’s expected profits _π_ are affected by firm-level characteristics, regional characteristics, and agglomeration economies which can generate or lower revenues _R_ and/or costs _c_ . Equation (1) translates into: Pr( _exit_  1)  Pr(  _it_   _firmit_  _regrt_  _aggrt_  _S_ (1  _exit_  1)  0) (2) where subscript _r_ designates regions, _firm_ firm-level determinants of exporting, _reg_ regional determinants of exporting, and _agg_ agglomeration economies.

Sunk entry costs of exporting _S_ might explain a large part of export activity. Since our data sample only covers the most recent Enterprise Surveys for each country, we cannot capture sunk entry costs of exports by adding lagged exports as an explanatory firm-level variable. We propose an alternative way to account for sunk entry costs of exporting. We make the following assumptions: (i) Exporters that haven’t exported in the previous period (new exporters) export a share of their output that is below threshold _ζ_ . Ruhl and Willis (2008) show for a sample of Colombian manufacturing firms between 1982 and 1986 that new firms export on average 3 to 4% of sales upon
as compared to other economic activities such as producing for domestic consumption or producing services, which are not captured in the merchandise trade data used to identify export discoveries. More formally, the exogenous returns to export in country _c_ and commodity group _i_ , π _i_ , _c_ was calculated as: where subscript _T0_ is the initial year in the time window in which the discoveries were counted and _T_ is the final year of the period. _X_ represents the real value of exports excluding the contribution of the export discoveries at time _T,_ and lower-case _x_ and _y_ are merchandise exports and GDP per capita. This measure of export returns was calculated separately for each of the ten Leamer commodity categories. Furthermore, as discussed above, it is possible that barriers will have a differential impact across industries. Therefore, our analysis conducted at the country/Leamer commodity group level examines the relationship between the number of discoveries in a particular country/commodity group and export growth in that country/commodity group, as well as how that relationship changes with barriers to entry across countries. In the sample used in the regression analysis only Malaysia had exports that exceeded the value of GDP at the end of the period in 2003, which yielded a very high positive number for this proxy of export profitability, as shown in the table with descriptive statistics in the Appendix. The regression results discussed below, however, were qualitatively similar when this country’s observations were excluded from the sample, but the results regarding the presence of market failures were stronger than those reported below.

In addition to these variables of interest, there are some necessary controls to be included in our test of the model. As discussed above, the process of discovery is closely linked to stages of productive diversification and the changing nature of innovation as countries approach the global technological frontier. We therefore control for this relationship by including GDP per capita and GDP per capita squared. We also allow for the possibility that discovery is driven in part by factor endowments by controlling for endowments as
ect traditional trade have di¤erential impacts on GVC trade? This paper studies the determinants of GVC participation based on empirical evidence stamped from a panel data set covering more than 100 countries over the past three decades. The time period re‡ects the growing international fragmentation of production and the sample includes countries in all geographical regions and at all stages of development. This large cross-country variation makes the data set uniquely suitable to estimate the marginal impact of di¤erent potential determinants on GVC participation and assess their relative importance. While many papers explain the GVC phenomenon from various angles, the strategy of this paper is to provide a uni...ed empirical framework to test jointly the role of the di¤erent determinants highlighted 1
br>°o !<br>1 2 3 4 5 6<br>Mobility across sectors (C1)<br><!-- End of picture text -->

<!-- Start of picture text --> 3|<br>I<br>|<br>I<br>|<br>I<br>|<br>I<br>I<br>I<br>8 \]<br>59 |<br>go I<br>aI1<br>I<br>I<br>I<br>I<br>I<br>r<br>I<br>oI<br>2 3 4 5 6 7<br>Mobility in and out of Unemployment (C2)<br><!-- End of picture text -->
# **_1. Introduction_**

The rhetoric surrounding trade is at odds with actual trade dynamics. In the wake of US-China tensions, reshoring, nearshoring and deglobalization dominate the news. Google search trends show all three terms have experienced maximum search activity since 2020 (Figure 1). But goods trade was at an all-time high in 2022, after years of slow growth. US imports in 2022 were close to 40 percent above pre-COVID levels, suggesting that deglobalization and reshoring were not yet significant.

Figure 1: Searches for nearshoring, deglobalization, and reshoring <!-- Start of picture text --> ugust<br> uly<br> pril<br><!-- End of picture text --> Source: Google trends data, downloaded on July 21, 2023.

While trade is thriving, cracks in US-China trade are emerging. In 2018 and 2019, the US imposed tariffs on over 60 percent of imports from China, mostly at the 25 percent level. In 2022, US imports from China in tariffed goods were 14 percent lower than in 2017, while imports from the rest of the world were 48 percent higher for those same products (Figure 2). As a result, China’s share in US imports fell from 21.6 to 16.3 percent between 2017 and 2022 and is now back at the level it was in 2007, before the global financial crisis.

The sizable reduction in China’s share of US imports and increase in total US imports implies that importers are turning to new sources of supply. A couple of specific examples illustrate this shift. Between 2017 and 2022 the US share of imports from China of sewing machines fell by 23 percentage points, while the share from Vietnam increased by 17 percentage points. US import shares from China of advanced technology products like laser printers and ADP input/output units fell by 23 and 40 percentage points, while the 2
14 view, we know that in many of the countries in our sample, the recent wave of reforms has been quite diverse across countries. Thus, to understand why some countries have experienced higher rates of productivity growth during their periods of reform while others have not, it would be necessary to analyze the details of the packages of policy reform that have been implemented.

Moreover, from a theoretical point of view, we should expect different dynamic responses to the reforming of an economy, depending on its specific structural characteristics. For example, the initial stocks of knowledge in each country, the sectoral composition of output, and the degree to which each country has a comparative advantage in relatively more dynamic industries are all plausible explanations of the different responses of TFP growth to economic reforms. A detailed consideration of these factors would certainly contribute to the understanding of the results encountered in this paper. It is, we believe, a promising avenue for future research. However, such an analysis would have to go beyond the use of aggregate data, as in this paper, and would require the use of trade and productivity data at the industry level.
studying the fertility history of the parents. In contrast, in the LSMS surveys, the fertility history information is not as clearly identified as in the DHS, which means that the children are not already linked to their corresponding parents. However, the advantage of the LSMS surveys is that they provide a direct mother and father identifier code, which is used to link the children to their corresponding parents. In addition, the Albanian LSMSs databases are a special case in terms of fertility compared to other LSMS surveys because they contain very detailed information on the family structure of each one of the households, including information about the children no longer living in the household, which makes possible the construction of the entire fertility history of each parent in the databases.<sup>9</sup> Following similar studies in the literature, I limit the analysis to mothers aged between 20 and 49 years old with at least two children whose oldest child was at most 18 years old at the time of the surveys. Firstly, women who are younger than 20 years old are excluded from the analysis because none of them have two children. Since the instrumental variable used in this study is

> 9The 2002 LSMS contains a specific module on fertility, where it is possible to identify the maternity history of every woman in the database. This type of module is not included in the 2005 and 2012 LSMS surveys, but for these cases the Migration module and Sons and Daughters Living Away module are used to construct the fertility history.

9
Disruptions in the supply of inputs due to the pandemic are well documented, although their impact on firms remains to be thoroughly analyzed (see Butt 2021, Pitschner 2022, Meier and Pinto 2020). We capture supply disruptions with a dummy variable equal to 1 if the firm’s supply of inputs, raw materials, or finished goods and materials purchased to resell decreased in the last month (after the outbreak of the pandemic) compared to the same month a year ago (prepandemic). The data source is COV-ES.

Voluntary dropouts from the labor market due to health or family reasons may have resulted in firms facing a labor shortage (see Pitschner 2022, Forsythe et al. 2022). For instance, Forsythe et al. (2022) find that for the US economy, the labor market remained surprisingly tight throughout the entire pandemic, despite the dramatic job losses. A shortage of hard-to-replace skilled workers may be especially worrisome. The COV-ES did not ask firms about skilled shortages. We use a question in the ES that asks firms how severe (on a 0–4 scale) inadequately educated workers is as an obstacle to their current operations (Skills obstacle).

Next, we account for the labor productivity of the firm prior to the pandemic using ES. It equals the log of total sales of the firm (in 2009 USD) in the last fiscal year divided by the number of full-time workers at the end of the fiscal year covered by the ES. Several studies have found a positive link between a firm’s pre-pandemic productivity and performance during the pandemic in terms of sales, employment, and survival (see Bloom et al. 2023, Bruhn et al. 2023, Muzi et al. 2022, Fernández-Cerezo et al. 2022, Kozeniauskas et al. 2020). Unlike earlier recessions, COVID19 resulted in a larger drop in women’s relative to men’s employment (Bundervoet et al. 2022, Adams-Prassl et al. 2020, Russell and Sun 2020, Bluedorn et al.
as do basic infrastructure assets such as access to electricity and safe drinking water and basic sanitation. High fertility and rapid population growth constrain the growth-poverty relationship through increasing the age dependency ratio. The process of structural transformation whereby labor shifts from agriculture to services and industry, and from rural to urban areas, is associated with a greater welfare-enhancing effect of aggregate economic growth. Finally, the extent to which economic growth benefits households is weakened by exposure to violence and conflict. The findings of our analysis complement those of Thorbecke and Ouyang (2022), who argued for “pro-growth poverty reduction strategies”, as direct interventions in poverty reduction were found to result in faster growth, which in its turn results in faster poverty reduction. Our results suggest another channel, that direct interventions that are good for the poor (basic education and literacy, access to electricity, access to drinking water and sanitation, etc.) also amplify the passthrough between aggregate economic growth and household welfare, sharing the benefits of economic growth more widely.

# 4. Conclusions

In this paper we revisit the growth elasticity of poverty based on an updated database of successive and comparable growth spells covering the period 1981-2021. We focus on three main questions: (i) does SSA, the continent with the highest poverty rate and also the slowest progress on poverty reduction, have a GEP that is significantly lower relative to the rest of the world, (ii) has the GEP changed over time (that is, has economic growth on average become more or less poverty-reducing over time), and (iii) are there

> 28 Certain human capital variables like mortality rates and intergenerational mobility had significant p-values, but these disappeared once using Anderson’s q values.

13
estimates the effect of the two treatment types (10- and 20-sessions of the CBT workshop) controlling only for school fixed effects. Columns (2) and (6) show the results for the baseline specification discussed above, with full set of controls. In Columns (3) and (7) we present the results combining the two treatment types. Since only those students who appeared in the school records in April 2017 were invited to the survey measuring socio-emotional skills, there might have been self-selection. For this reason, in columns (4) and (8) we present the results accounting for the possible self-selection into the survey for applying two-stage Heckman procedures, with teacher fixed effects as exclusion restriction. The underlying rationale is that specific teachers might have been more active than others in keeping children in school until the survey was taken.

**Table 3: ITT on Socio-emotional skills**

||(1)<br>St|(2)<br>d Bartlett sc|(3)<br>ore - PLAN|(4)<br>EA|(5)<br>St|(6)<br>d Bartlett s|(7)<br>core - Ba|(8)<br>rOn|
|---|---|---|---|---|---|---|---|---|
|10 Sessions|0.178***<br>(0.067)|0.165**<br>(0.068)|||0.051<br>(0.059)|0.047<br>(0.060)|||
|20 Sessions|0.166**<br>(0.074)|0.175**<br>(0.074)|||-0.035<br>
by 16 percentage points. The findings of a study by Minten, Korn, and Stifel (2013) for northwestern Ethiopia point to the link between higher transport costs and agricultural input prices. Another study by Stifel, Minten, and Koru (2016) suggests that rural feeder roads have internal rates of return of 12–35 percent.

Another collection of literature to which this study is related is about the impacts of disaster shocks on welfare and poverty, and the role that better connectivity can play in alleviating these impacts. As an agrarian economy dependent on smallholder rain-fed agriculture—located in one of the world’s most drought-prone regions—Ethiopia has been historically susceptible to drought and, despite recent economic growth, well-being remains highly vulnerable to drought. Several previous studies have analyzed the impacts of droughts on consumption (Gao and Mills 2018; Dercon, Hoddinott, and Woldehanna 2005; Fuje 2017), poverty trap (Carter et al. 2007), long-term nutrition outcomes (Dercon and Porter 2014), and asset and wealth and inequality (Little et al. 2006; Thiede 2014). Recently, research focusing on Ethiopia has also shown that better connectivity can help alleviate the negative effects of drought. Hill and Fuje (2018) examine the effects of droughts on grain prices in Ethiopia, and find that this effect was substantially weaker in the post-2007 period compared to earlier periods, a finding that can partly be explained by better market access through road investments. Focusing on the 2015/16 El Niño drought, Hirvonen et al. (2017) find that the drought negatively affected child nutrition in places that were poorly connected, but not in betterconnected places. This study contributes to this line of literature by shedding new light on the role of rural connectivity in potentially alleviating the impact of adverse weather droughts.

The rest of this report is structured as follows. Section 2 reviews the recent placement of rural roads in Ethiopia. Section 3 describes the data: the Ethiopia road database and the ESS. Section 4 explains the