rafmacalaba/data-use-mentions-extended huggingface dataset:
larger share of employment, may be less subject to idiosyncratic distortions, particularly so in advanced countries. Indeed, Appendix B.2 shows that ordinary least squares (OLS) yields very similar estimates to WLS in the least developed countries, whereas it predicts substantially more correlated misallocation in advanced economies, a feature that resonates as implausible.<sup>28</sup> – Figure 2: TFPR TFPQ Elasticity, GDP, and Average Size <!-- Start of picture text --> Kenya Salvador Portugal GhanaPortugalEthioSalvadorpia Kenya<br>Ethiopia Ghana<br>ColombiaIndia<br>India BulgariaChilePeru RomaniaColombia Bulgaria Peru Romania MalaysiaBelgium Chile Hungary<br>Belgium Latvia<br>Hung Latvia M a laysiarySpainItalyFranceFinland SpainItaly FinlandFrance<br>5 6 7 8 9 10 11 12 50 100 150<br>Log GDPpc Av. Size<br>0.5 0.5<br>0.4 0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1<br>slope TFPR−TFPQ 0.1 Elasticity TFPR−TFPQ<br>0.0 −0.1<br><!-- End of picture text --> Note: The left panel of figure illustrates the weighted least squared (WLS) estimate of the regression defined in equation 18, with firm-level employment shares as weights, against the log of income per capita for the year 2014, as reported in the Penn World Table version 9.0. A regression coefficient of 0.15 for the US was subtracted from each country’s estimate to control for misallocation in the efficient benchmark. The measurement of TFPR preserves the parametric assumptions of Hsieh and Klenow [2009], namely an elasticity of substitution equal to 3, and factor shares taken from the US four-digit manufacturing sector. The tails of the TFPR and the TFPQ distribution were trimmed at the bottom and top 5 percent. The right panel
Bilateral import data for 18 MENA countries (all except Iraq and West Bank of Gaza) and 239 partners is from United Nation’s Comtrade<sup>10</sup> . We use data for the period 1990-2009 (20 years) as in the 1980s MENA regionalism was non-existent. Data for Libya is mirrored because Libya does not report to the United Nations system. These differences in data sources for Libya are partly controlled for in our empirical specification by the importer-year and exporter-year fixed effects.

We use total import data, but also data on non-oil imports<sup>11</sup> . In a robustness check we also used data that subtract re-exports from bilateral import data<sup>12</sup> , but the results are almost identical to those reported in the next section.

We use WTO notifications to capture the year of entry into force of the agreement<sup>13</sup> , and these available in the appendix. As already mentioned, we looks at the trade effects of PAFTA, The Gulf Cooperation Council (GCC), AGADIR, COMESA, all Euromed agreements signed by MENA countries, all FTAs with EFTA countries and all FTA with Turkey.<sup>14</sup> We decided not to include dummies for the FTA signed by some MENA countries and the US because these are too recent to meaningfully estimate their impact. We also do not control for the Economic Cooperation Organization (ECO) for three reasons. First, the only MENA country in ECO is Iran. Second, it starts in 1992 and therefore captures almost our entire time variation. Including ECO would request expanding the time-span. Third, and more importantly, it is well known that ECO has been suffering from serious implementation problems, and therefore not much should

> 10 were obtained through the World Bank’s web platform: WITS (World Integrated Trade Solution) 11 We exclude HS27: Mineral fuels, oils & product of their distillation from total imports.

> 12 Hence results are
_._ 01. Standard errors in parentheses are clustered at the district level for Columns 1-4 and at the individual level for Columns 5-9. Columns 1 and 2 estimate the specification _Employedi_ = _α_ + _βV accinationi_ + _φControlsi_ + _Month FE_ + _District FE_ + _εi_ where Vaccination refers to either the individual being vaccinated with at least 1 dose or the number of doses, and the dependent variable is a dummy variable indicating whether the person is employed or not. In Columns 3 and 4, the dependent variable is first-differenced, so that _β_ estimates the effect of vaccination on changes in employment status; ∆ _Employedit_ = _α_ + _βV accinationi_ + _φControlsi_ + _Month FE_ + _District FE_ + _εi_ . In Columns 5-7 we allow both employment and vaccination to vary over time to partial out individual fixed effects and estimate _Employedit_ = _α_ + _βV accinationit_ + _Individual FEi_ + _Wave FEt_ + _εit_ . Finally, in Columns 8- 9 we take individual vaccination status as fixed over time and interact with all survey-waves: _Employedit_ = _α_ + _βV accinationi∗Wavet_ + _φControlit_ + _Individual FEi_ + _Wave FEt_ + _εit_ .

Columns 1 and 2 of Table 1 present the OLS estimates of the correlation between individual vaccination and employment status using cross-sectional data between SeptemberDecember 2021 (wave 24). Getting vaccinated with at least 1 dose is associated with a 9.6 pp increase in a person’s likelihood of being employed (Column 1). In Column 2, when we compare the marginal effects of 1 dose (6.8 pp) and 2 doses (13 pp), the 6.2 pp difference is significant at 99% confidence levels suggesting that completing the full vaccination course has stronger effects on employment outcomes. In Columns 3 and 4, we estimate the same specification, but use the first-differenced employment status instead. This allows the coefficient on vaccination status to measure the effect on changes in employment status in wave 24 from employment status in the previous wave. Coefficient estimates reduce substantially to a 0.6 pp increase in the likelihood
mercial units account for about 90% of the non-residential in our sample study. Figure 2b also indicate that hotels (tourism sector) were heavily affected by the pandemic as electricity consumption declined significantly during the three months lockdown period. While consumption improved after the lockdown was lifted, it still remained lower than pre-pandemic levels. This result is in line with economic data from several countries indicating the devastating impact of the pandemic on the tourism sector’s contribution to GDP<sup>19</sup> due to SAH and restrictions on international travels (Mulder, 2020; Abbas et al., 2021).

## _Rural-Urban differences_

Finally, we explore the differences in the impact of COVID on electricity consumption between rural and urban households. The average effects are shown in Table 4. The DID estimates suggest electricity consumption among urban households increased by about 5.3% (column 2) compared to a 3.2% (column 4) increase among rural households. To trace the dynamics in the effects, we show estimates of the monthly changes in electricity consumption in Figure 3 (see Table 5 for details<sup>20</sup> ). The results in Figure 3 show an increased level of consumption among both rural and urban households, albeit the rate of increase was consistently higher among urban households for the majority of the period.

Taken together, the analysis of the Rwandan electricity data suggests a modest increase in electricity consumption associated with the COVID-19 pandemic, albeit, non-residential electricity consumption experienced large declines. This partly reflects the differing responses to the impact of the pandemic across various sectors.

# **5.2 Ghana**

We now turn our attention to the Ghana data. Similar to the analysis in Section 5.1, we begin by looking at the average effects of the pandemic on electricity consumption in Ghana, estimated separately for the various user groups, as shown in Table 6. The DID estimate in column 2 suggests that overall, relative to the pre-COVID lockdown period, electricity
(0.362)|(0.464)|(0.542)|(0.580)|(2.124)|(2.180)|(2.804)|(3.302)|(2.452)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|EA|ID|ID|ID|ID|EA|ID|ID|ID|ID|
|Controls|NO|NO|YES|YES|YES|NO|NO|YES|YES|YES|
|R-squared|0.163|0.001|0.006|0.009|0.026|0.155|0.002|0.007|0.010|0.019|
|# of EAs|433|433|433|290|143|433|433|433|290|143|
|N|33741|28869|28609|20944|7665|33740|28868|28608|20943|7665|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on alternative measures of societal cooperation: a binary variable indicating an individual’s participation (the first five columns) and number of households an individual has worked for (the last five columns) within the labor sharing arrangements. Individual sample weights from ESS2 applied. Columns labeled (1) to (3) report results for all individuals while columns labeled (4) and (5) report results for rural and urban individuals, respectively. All columns control for round FEs. Further, columns labeled (1) control for EA FEs while all other columns control for individual (ID) FEs. Columns labeled (3) to (5) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request.
Equation (1) can be re-written as: is the employment coefficient, i.e., the number of workers required to produce one unit of output. Thus, given industrial output, the impacts of exports on labor demand are likely to be larger, the higher is the labor-intensity of the industrial composition in province _p_ . Overall, the Export Index is roughly interpreted as the proportion of workers affected by exports in each province.

The employment and output data are taken from the annual _Enterprise Survey_ data (GSO). The surveys cover all the registered “enterprises” in Vietnam but exclude household enterprises and agriculture and forestry cooperatives. In estimating the change in labor demand, the _Enterprise Survey_ data have two advantages over the VHLSS 2002 and 2004. First, whereas the _Enterprise Survey_ data are likely to include migrants, the calculation based on the VHLSS may underestimate the expansion of employment induced by exports, by excluding migrants who have no or temporary registration status.<sup>33</sup> Second, the pre-BTA comparable employment and production data, which are needed to construct an instrumental variable, are readily available in the _Enterprise Survey_ data (for the year 2000 in my analysis).

The data for the size of the economically active population aged 15 years and over by province were extracted from the GSO website, since the latter data are not available in either the VHLSS or the _Enterprise Survey_ data for the year 2000. The trade data for Vietnam were extracted from the U.N. Comtrade system at the Harmonized System (HS) (1996) 6-digit level and aggregated at

> 33 When I use the VHLSS 2002 and 2004, the pattern of the change in industrial employment across sectors is less pronounced relative to the pattern resulting from use of the _Enterprise Survey_ data.

19
Economic theory suggests that individual attitudes would be stable over the life‐cycle or become more pro‐immigration since, for instance, older individuals are less likely to work and thereby less exposed to immigrants’ competition in the labor market. In contrast, most of the empirical literature on this subject finds that older individuals are more averse toward immigration than their younger peers (see, for instance, Mayda, 2006; Facchini and Mayda, 2009; Card et al, 2012; O’Rourke and Sinnot, 2006). Nevertheless, the _age effect_ regarding preferences on immigration reported in most empirical studies includes both a true age effect as well as a cohort effect. To our knowledge, the only paper that attempts to separate these effects is Calahorrano (2011). Using panel data for Germany between 1999 and 2008, she finds that immigration concerns decrease over the life‐cycle. At the same time, older cohorts are found to be more averse to immigration in Germany. Relative to other issues, immigration concerns are found to actually decrease over the life‐cycle. These results are in sharp contrast to those that come from a specification that does not control for year of birth.

To disentangle the age and cohort effects on attitudes toward immigrants, we use an approach similar to Calahorrano (2011). However, given the lack of comparable panel data surveys for a large group of countries, we use pooled cross‐sections from the European Social Survey (ESS) for the period 2002‐2012. Using these repeated cross‐sections, we track birth cohorts over 10 years, which allows disentangling the effects of age, cohort and time on attitudes toward migrants. We first confirm the stylized fact found in the literature that older individuals are more averse than their younger peers with regards to immigration. However, when controlling for birth cohort, our estimates become more consistent with the predictions of economic theory, as the estimated age effect becomes either positive or not statistically different from 4
nonetheless control for possible confounding factors in this case as well. Both policies are associated with changes in local misallocation. This provides an important check on our computed measures of misallocation. This also shows how misallocation may be caused by policies that create stronger frictions in input markets.

In the analysis below, we return to regression (5) and explore a broader range of policies including land and labor reforms over 1985-1997 and the liberalization of industry licences, trade, and foreign direct investment over the same period.

The second estimation performed by Duranton _et al._ (2015) is a regression of output misallocation on factor misallocation: where the dependent variable is output misallocation in district _d_ and the three explanatory variables are misallocation indices for the three inputs, employment ( _L_ ), land and buildings ( _T_ ), and other fixed assets ( _K_ ). The first main result associated with this regression in Duranton _et al._ (2015) is that output misallocation is strongly associated with input misallocation. A one unit increase in factor misallocation typically leads to about a one unit increase in output misallocation in Indian districts. Below we provide a new way to interpret this result by showing how output misallocation can be expressed as a function of factor misallocation. When establishments operate with constant returns to scale, a one-for-one increase in output misallocation should follow from an increase in misallocation for all factors. Below, we verify this result further using data for services and manufacturing data used at a level of aggregation different from Duranton _et al._ (2015). The second main result of Duranton _et al._ (2015) is that the misallocation of land and buildings plays a particularly important role to explain the misallocation of output in manufacturing. We confirm in part this result obtained for districts when using instead specific industries at the state level. We also provide results for services. This time, the fundamental driver of output misallocation is the misallocation of employment, while the misallocation of land and buildings plays little to no role.

7
literature. In line with recent innovations in the industrialized country literature, we generate proxies for both the level of amenities and costs of living and find their influence to be statistically significant. Further, contrary to much of the literature either in the industrialized or developing world noted above, our specification allows

> 2 Using data gathered in 25 Mexican communities, Massey and Espinosa (1997) create histories of migrants to the US and find initial migration to the US to be negatively related to the wage rate and the proportion of women in manufacturing.  Jones (2000) examines 17 emigrant municipalities and argues in favor of an association between proximity to maquiladoras and employment growth and, in turn, declining US migration.

2
, Indonesia, Malaysia, Philippines and Thailand), as well as in the Middle East and Northern Africa, including the Arab Republic of Egypt, Oman and Tunisia. This pattern is consistent with the recent observation that East Asian economies attract high inflows of investment into services sectors (Mode 3) in spite of high policy restrictiveness, including ownership limitations (Barattieri et al., 2016).

## **Figure 3: Overall STRI by economy and level of income68 economies, 2016**<sup>**14**</sup>

<!-- Start of picture text --> IND IDN<br>MMR CHN<br>PAN<br>PHL EGY THA MYS OMN<br>VNM TUN LUX<br>KEN BGD PAK NGA UKR LKAPERZAFCOLBRADOMCRI MEXARGURY CHLKAZTURLVAGRCRUSHUNPOLPRTLTUESTSVKSVNCZEISRESPITAKORNZLFRAGBRFINBELCANDEUAUTAUSISLDNKSWENLDCHT USAHKGCHEIRLNOR SGP<br>JPN<br>ECU<br>8 9 10 11<br>Log GDP/capita 2016<br>80<br>60<br>40<br>STRI 2016<br>20<br>0<br><!-- End of picture text -->

> 13 The analysis is based on data for 68 economies.  Data for additional economies (Jordan, CEFTA Parties and other African economies) are currently being collected and processed for future inclusion in the STPD.

> 14 It encompasses 1,534 economy-subsector observations from across 68 economies and 23 subsectors.

12
Summary statistics of business environment indicators at the establishment level for 2003 are presented in the first column of Table 3.<sup>10</sup> Of all sampled establishments, roughly 15 percent have at least 10 percent government ownership, 68 percent participate in export markets, 68 percent have and use internet, 41 percent have a formal training program, while only 21 percent report facing a foreign competitor. The mean interest rate on loans is around 14 percent, with an average share of investment financed externally of 33 percent. The average rate of losses from crime is low, under 1 percent of sales, and the mean share of electricity sourced from private generators is around 13 percent. In addition to these WBES indicators, the outcome variables based on the Census data are the log of output per worker (its mean across clusters is 9.22, or $10,071) and the log of value added per worker (its mean across clusters is 8.45, or $4,687).

Figure 5 presents evidence of large labor productivity heterogeneity across industrial clusters. Although it would be desirable to explore these dispersions based on establishment-level data at the most disaggregated product level while controlling for differences in quality and market power, the results are nonetheless suggestive. Across industrial clusters, the 90<sup>th</sup> percentile in output per worker is, on average, ten times that of the 10<sup>th</sup> percentile. The industrial sectors with the greatest dispersion in productivity are food/beverages/tobacco and non-metallic mineral products. Dispersion across locations is as prominent if not more so—with an average ratio of output per worker for the 90<sup>th</sup> relative to the 10<sup>th</sup> percentiles greater than 20 in the North Central and Central provinces. These numbers are likely an understatement of the actual firm-level dispersion in productivity, given that they are based on averages across firms within a given location
|Y <sup>s</sup><br>j|Y <sup>1</sup><br>J|· · ·|Y <sup>S</sup><br>J|· · ·||||

# Measurement

The main challenge facing the measurement of GVC arises from the fact that customs data, the standard source for international trade flows, provides information on where the transacted good or service was produced, but not on how it was produced, i.e., which countries contributed value to it. Similarly, customs data record where the transacted good is flowing to, but not how it will be used, i.e., whether it will be fully consumed (absorbed) in the importing country, or whether it will be re-exported after the importing country adds value to it. With the goal of tracing value-added trade flows across countries, a body of work has combined information from customs offi ces together with national Input-Output tables to construct global Input-Output tables. The most widely used world Input-Output tables are: (a) the World Input-Output Database (WIOD), a collaborative project led by researchers at the University of Groningen; (b) the OECD TiVA database, and (c) the Eora Global Supply Chain Database, constructed by a team of researchers at the University of Sydney. At a very broad level, this collaborative projects can be thought of as a “scaled up” version of product-level studies (such as Dedrick, Kraemer and Linden’s 2010 well-known case study of the iPod), attempting to break out the distribution of the financial value embedded in a product across the many participants in its supply chain. Figure 1 provides a schematic version of one such WIOT.<sup>2</sup> With these global Input-Output tables at hand, it is then straightforward to devise alternative measures to document the extent to which production process have become globalized in recent years, and the extent to which various
aims to address these imbalances. For example the revenue generation will be shifted away from cargo owners (from 60 percent to about 35 percent), onto shipping lines (from 18 percent to roughly 36 percent) over a ten‐year period.<sup>14</sup>

# _3.3 Reliability constraints_

Setting aside access and pricing issues, of much bigger concern to users of the freight transport network is the reliability of the service. This is increasingly important in a world of global value chains, where firms are part of international production networks and must slot into tightly defined delivery schedules. In this respect, performance of the rail service – including the rail and intermodal terminal infrastructure – and ports falls far short of what is needed to facilitate export competitiveness.

<u>Reliability concerns can trump price advantages of rail</u> It is important to distinguish between the heavy haul lines – which are considered world class – and the remainder of the system which is in relatively poor condition. The lack of reliability of rail transport was consistently mentioned by firms as the main reason for using road transport. Frequent train delays and cancellations prompted customers to switch to road haulage, especially for transportation of high‐ value goods, and when timing is important. This shift puts severe pressure on the road network. A recent survey of logistics professionals found that the competence and quality of rail services are much poorer in South Africa compared to Australia, Canada, India, China, Germany, the United States and Russia (World Bank, 2014a).<sup>15</sup> <mark>An analysis of the train transit and turnaround times indicates that the main problem remains with the operation of the intermodal rail freight terminals, both at the port and inland. This includes both the long time it takes for customers to receive goods by rail as well as poor tracking and information systems. Customers demand to know exactly where their goods are at any time, and most road haulers offer this service.