rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 5: Robustness checks</u>**

||Base|Firm Performance Indicator|Sales growt|h measure|th<br>HGF c|th<br>ut-offs|
|---|---|---|---|---|---|---|
|VARIABLES||Employment|Birch Index|Log diff.|top 10<br>pctle|top 20<br>pctle|
|Linkages Channel|-0.053|-0.041*|-0.017|-0.075|-0.073|-0.088*|
||(0.051)|(0.024)|(0.293)|(0.097)|(0.052)|(0.048)|
|Demonstration Channel|0.006<br>(0.019)|-0.016<br>(0.010)|0.156<br>(0.096)|0.011<br>(0.037)|0.003<br>(0.019)|0.006<br>(0.020)|
|High-growth firm|0.190***<br>(0.021)|0.738***<br>(0.028)|0.679***<br>(0.079)|0.235***<br>(0.044)|0.179***<br>(0.016)|0.162***<br>(0.012)|
|X Linkages Channel|0.568***<br>(0.201)|0.603***<br>(0.173)|1.496**<br>(0.738)|1.428***<br>(0.518)|0.442***<br>(0.148)|0.321***<br>(0.092)|
|X Demonstration Channel|0.109*<br>(0.063)|0.093<br>(0.062)|-0.182<br>(0.227)|-0.030<br>(0.144)|0.077<br>(0.048)|0.019<br>(0.041)|
|Log Age|-0.061***<br>(0.005)|-0.031***<br>(0.003)|0.151***<br>(0.031)|-0.102***<br>(0.014)|-0.057***<br>(0.005)|-0.054***<br>(0.005)|
|Exporter|-0.003<br>(0.010)|0.016***<br>(0.005)|1.083***<br>(0.067)|0.048<br>(0.033)|-0.003<br>(0.010)|-0.004<br>(0.010)|
|Log Labor Productivity|0.083***<br>(0.005)|-0.001<br>(0.002)|1.149***<br>(0.024)|0.173***<br>(0.014)|0.083***<br>(0.005)|0.083***<br>(0.005)|
|Constant|-0.816***<br>(0.059)|0.336***<br>(0.019)|2.421***<br>(0.302)|-1.687***<br>(0.158)|-0.823***<br>(0.060)|-0.991***<br>(0.065)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|33,305|36,893|21,848|33,305|33,305|33,305|
|R-squared|0.165|0.165|0.165|0.165|0.174|0.174|

Note: Standard errors (in parentheses) are clustered at the country-sector level. *** p<0.01, ** p<0.05, * p<0.1 Source: Author’s computation using the WBES.

# **5. Concluding remarks**

Foreign investors bring a plethora of knowledge that has the potential to create positive spillovers to the host economy. These benefits, however, are not guaranteed. The key reason is that MNCs may also generate pro-competitive pressures in output and input markets in the host economy to the detriment of some local incumbents. The relationship between these two effects determines the overall impact of foreign presence on local enterprises. There are two main channels through which foreign knowledge and technology can be transferred to domestic firms. The first is backward linkages between foreign firms and domestic suppliers. The second is the demonstration channel, through which domestic firms imitate and replicate foreign technologies and management practices in their own production processes.

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Table 1: Summary of Countries and Years with Microdata Available

|Country|Label|Total Observations|Obs./Year|Years|
|---|---|---|---|---|
|Argentina|_Arg_|582,592|64,732|2004-2012|
|Bolivia|_Bol_|130,054|14,450|2001-2002, 2005-2009, 2011-2012|
|Brazil|_Bra_|2,932,482|183,280|1995-1999, 2001-2009, 2011-2012|
|Chile|_Chl_|875,430|125,061|1996, 1998, 2000, 2003, 2006, 2009, 2011|
|Colombia|_Col_|676,177|135,235|2008-2012|
|Costa Rica|_Cri_|76,318|25,439|2010-2012|
|Dominican Republic|_Dom_|185,374|16,852|2002-2012|
|Ecuador|_Ecu_|369,451|36,945|2003-2012|
|Guatemala|_Gtm_|100,989|33,663|2000, 2006, 2011|
|Honduras|_Hnd_|269,539|44,923|2006-2011|
|Mexico|_Mex_|492,068|70,295|1996, 1998, 2000, 2002, 2008, 2010, 2012|
|Nicaragua|_Nic_|19,603|19,603|2005|
|Panama|_Pan_|182,524|30,420|2007-2012|
|Peru|_Per_|669,352|74,372|2004-2012|
|Paraguay|_Pry_|145,748|12,145|1995, 1997, 1999, 2001, 2003-2005, 2007-2011|
|El Salvador|_Slv_|350,962|38,995|2004-2012|
|Uruguay|_Ury_|429,685|61,383|2006-2012|
|Total Obs.||8,488,348|||

_Notes_ : Information is based on SEDLAC (World Bank and CEDLAS). Only individuals in the labor force are counted as observations.

try.<sup>4</sup> A summary of the countries along with the years used in this study are shown in Table 1.

All observations in this study represent an individual who is in the labor force, _i.e._ either employed or looking for employment. If an individual is not in the labor force, then we cannot make an inference about their welfare as that may be their preferred state. If a worker is in the labor force, but unemployed, then we can infer that a job would positively influence their welfare. Therefore, we keep unemployed individuals in the sample when we are looking at job quality. Brazil dominates the sample with just under 35% of the observations, whereas Nicaragua represents less than 0.25% of the sample.

The summary statistics are shown in Table 2. Unless otherwise noted, the vari-

> 4As discussed later, only years that reported at least 5 dimensions of job quality were used in this study. This resulted in 9.88% of the observations being trimmed from the sample, equal to 34 country-years and 930,591 observations.

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|Age|-0.0286**|-0.0289**|-0.0237*|-0.0317***|
|---|---|---|---|---|
|Age squared|(0.0122)<br>0.000380**|(0.0132)<br>0.000340*|(0.0125)<br>0.000332*|(0.0121)<br>0.000403**|
||(0.000181)|(0.000187)|(0.000183)|(0.000179)|
|Father literacy|-0.0817|-0.151|-0.0958|-0.0657|
||(0.149)|(0.160)|(0.155)|(0.147)|
|Mother literacy|-0.260*|-0.296*|-0.290*|-0.214|
||(0.152)|(0.161)|(0.159)|(0.152)|
|Improved sanitation|-0.690|-0.692|-0.673|-0.708|
||(0.528)|(0.528)|(0.522)|(0.520)|
|Distance to main road|0.0159|0.0421|0.0590|0.0154|
||(0.0576)|(0.0644)|(0.0645)|(0.0569)|
|Distance to market|0.110*|0.149|0.200**|0.112*|
||(0.0607)|(0.102)|(0.0837)|(0.0599)|
|Distance to main road squared|-0.00183|-0.00195|-0.00274**|-0.00181|
||(0.00112)|(0.00119)|(0.00133)|(0.00110)|
|Distance to market squared|-0.000279|-0.000403|-0.000553**|-0.000285|
||(0.000219)|(0.000336)|(0.000280)|(0.000216)|
|Teff prices||0.0247<br>(0.0266)|||
|Wheat prices|||-0.0239<br>(0.0297)||
|Maize prices||||-0.0716**<br>(0.0322)|
|Constant|-3.835|-6.964|-8.304**|-3.481|
||(2.727)|(4.949)|(4.038)|(2.701)|
|Observations|1,949|1,833|1,870|1,934|
|R-squared|0.020|0.022|0.025|0.027|
|Number of individuals|1,022|986|1,004|1,022|
|N|1949|1833|1870|1934|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Contemporaneous crop prices are statistically insignificant when we use wasting as the dependent variable except for maize prices where the sign of the coefficient is negative.

# **6. Discussion of the Results**

This study investigated the association between food prices and child undernutrition in Ethiopia. Our results suggest that rising crop prices are associated with improved scores for child undernutrition of children aged 6-59 months. The largest effects on child undernutrition are observed for teff prices compared to maize and wheat prices. For example, a 10 percent increase in the price of teff leads to an improvement in height-for-age scores of children by 2.8 percent. Similarly, a 10 percent increase in the prices of maize and wheat leads to an 13
**Table 14 Mobility Across Labor Force States: Lithuania against Selected Countries**

||Country|Period|Shorrock'sindex(*100)|
|---|---|---|---|
|Bulgaria||1995-96|36.7|
|Lithuania||2000-01|31.0|
|Poland||1997-98|31.3|
|Russia||1995-96|38.5|
|Slovakia||1999Q1-Q4|17.6|
|USA||1992-93|61.6|

Note: the Shorrock's index is defined as S=(n-tr(P))/(n-1) and is proportional to the fraction of persons who changed their status within a given penod.

where: n denotes the number of states and tr(P) is the trace of the transition matrix P.

S takes the value of 0 when nobody changed their status, and the value of n/n-I when everybody changed their status, Sources: Bulgaria: Garibaldi et al. (2001) Lithuania. Labor Force Survey, May 2000 and May 2001 Poland: Bell (2001); Russia: Bell (2001); Slovakia. World Bank (2001c) USA: Boeri (1998), Bank staff calculations The unemployment problem in Lithuania is magnified by large numbers of new entrants to the labor market. The yearly transition rate from inactivity into unemployment accounts for 4.8 percent, and is substantially higher than in other transition economies. For example, in Poland this rate was 2.2 percent in 1997/78 (Bell 2001), about half that observed in Lithuania.

A vast majority (86 percent) of the unemployed in Lithuania has previous labor market experience. This proportion is quite typical of transition economies and indicates that the labor market is in the state of flux, that people lose jobs mainly because of enterprise restructuring. It is interesting to contrast this with situation prevailing in some transition economies with the most rigid labor markets. For example in Macedonia, which as other former Yugoslav republics inherited an extremely high level of employment protection, the proportion is reversed: a majority of the unemployed are new entrants (mostly school leavers) whom low labor turnover prevents from finding a job. The above figure suggests that (despite high youth unemployment, which is a common phenomenon) barriers to entry are not a dominant labor market problem in Lithuania. It is largely enterprise restructuring and associated lay-offs which contribute to unemployment.

44
successful projects, as for these the need to understand what could have been done better is most relevant (Table 3).

# **Table 3: In-depth review sample**

**_Selection of ICRs reviewed – frequency of political economy factors assessed as key constraints_**

||**Total **|DPLs|ILs|
|---|---|---|---|
|Rated moderately<br>satisfactoryor better|**10**|--|10|
|Rated moderately<br>unsatisfactoryor worse|**18**|10|8|
|Operations rated MUS or<br>worse for which pol econ<br>factors are significant|**14****_(78%)_**|6_(60%)_|8_(100%)_|

For operations rated _moderately unsatisfactory or worse_ , political economy challenges are described as key obstacles with very high frequency in Implementation Completion Reports. For the 18 less successful operations reviewed, more than 75 percent of ICRs providing ex-post assessments of these operations point to political economy factors as key obstacles. This includes all of the poorly performing Investment Lending operations.<sup>32</sup> As to the specific political economy factors inhibiting project success, the ex-post reports highlight:

- underestimation of reform resistance (whether open resistance e.g. from trade unions, or more covert resistance within the bureaucracy or from vested interests);

- overestimation of governments‘ reform commitment – including its ability to take on vested interests; and

- failure to engage with stakeholders outside the acting government. This left several operations moribund when governments changed.

While Implementation Completion Reports and IEG assessments confirm that political economy challenges emerge and are a major source of project under-performance, they offer rather less clarity with regards to what has made successful operations feasible even in challenging environments. Positive political economy drivers or successful strategies for managing political economy challenges receive less attention. Issues of potential opportunities in post-conflict environments as well as the question how in some instances teams seem to have been able to successfully manage political economy disincentives to reforms do not receive much explicit attention.

> 32 Of each type of operation, slightly over half of the operations were reviewed and compared to ten operations that performed moderately satisfactory or better (representing one third of the well-rated group). This was judged to be an adequate way to check for differences between well and less well performing loans. The inclusion of additional ILs or DPLs performing well could be considered; however, the additional insights to be gained are likely to be limited (while there would be some gain in robustness). The main _other_ critique raised was that projects were overly complex in the areas they were seeking to address or with regard to implementing arrangements.

30
biases the estimates upwards, whereas the ZINB recognizes that the zeros may come from different stochastic processes (participation or quantity).

For the on-site model, TRNB, the expected number of trips far exceeds the demand estimated by the household survey. This seems reasonable since we are comparing casual versus avid users of the site. However, the expected number of trips is even higher after accounting for ES (TRNBES). At first glance this may seem counterintuitive, but recall that expected trip demand is calculated as E( _yi_ | **x** _i_ ) = λ _i_ + 1 + α _i_ λ _i_ ), and note that the only substantial difference between the estimated parameters of TRNB and TRNBES is the value of the over-dispersion parameter, α (see Table 3). Thus it is the overdispersion that is driving this result. This finding is similar to that found by Englin and Shonkwiler (1995), where expected trip demand is 1% higher for their sample-based ‘restricted negative binomial model’ (analogous to our TRNBES model) and 63% higher for their population-based trip demand. Martinez-Espineira and Amoako-Tuffour (2005) also find an 18% higher expected trip demand in their ES model.

Estimated household consumers surplus was $8.82 per trip whereas for the on-site sample CS was calculated as $8.73 without compensating for ES and $8.21 per trip with ES. Although all three results are close, it is rather surprising to find the closest estimate to be between the TRNB and ZINB models. One would initially expect the TRNBES to be the closest if ES were present in the on-site sample. The most plausible explanation is rooted in the very reason why one argues for ES adjustment; if adjustments for ES yield only small differences in expected demand or consumer surplus, this suggests that those surveyed at Lake Sevan possess characteristics similar to those in the household sample. This implies that either the TRNB or TRNBES model is sufficient for estimation. This can be more clearly seen if one views the mean function λ, and the similarity of estimated characteristics between the TRNB and TRNBES models (especially the similarity between the estimated coefficient on travel cost, β _p_ ; which is the denominator in the CS calculation, - _λi_ / β _p_ . Ovaskainen _et al_ . (2001) and Englin _et al_ . (2003) also find similar results where the ES adjustment had little effect on coefficient and benefit estimates.

16
where Similarly, θ _2_ is obtained by substituting (16) into (15). Finally, with the parameters θ _1_ andθ _2_ recovered, the price elasticities will be calculated from (14).

This single good model can be extended in a multi-good case, so to estimate cross-price elasticity. To do so, ones needs to add additional price terms to the budget share equation (equation 3) and model quality under the separability assumption. Describing the whole method is out of the scope of this paper as the algebra for this exercise would require the introduction of a good deal of new notation. The interested reader can refer to Deaton, 1997.<sup>10</sup>

# **4. Income, Quality and Price Elasticities for Mexican Households**

Income, quality and price elasticities are produced for 10 product groups: wheat, maize, alcohol and tobacco, meat, legumes, dairy products, oils and fats, vegetables, fruits and sugar. As seen in the second section, the importance of these product groups is illustrated by the fact that they represent about one-third of the total expenditure basket of the average Mexican household, and more than 40% of the total expenditure of the households in the first income quintile. The estimated elasticities are presented for the totality of the households, differentiated by income quintile and by rural and urban areas. Bootstrapped<sup>11</sup> standard errors for the price elasticities are estimated by making 500

> 10 Deaton goes into this topic from equation 5.56 to equation 5.102, in chapter 5.3.

> 11 The bootstrap involves repeated re-estimation of a parameter using random samples with replacement from the original data. The idea behind this technique is to treat the sample as if it were the whole population. The statistic of interest can then be calculated from each of these samples. The average of the statistics over all of these samples is known as the bootstrap estimator. With repeated estimates of the same

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13

# Table 2

# Welfare Costs of Rationing (Variant<sup>1)</sup>

|Rationing<br>Rate a/|Consumer<br>Imports|Consumer and Intermediate Imports<br>High Trade b/<br>Low Trade b/<br>Elasticities<br>Elasticities<br>(1.5)<br>(0.5)|
|---|---|---|
|Column|1|2<br>3|
|.9|0.0|0.4<br>1.0<br> <br>|
|.8|0.4|1.7<br>4.9<br> <br>|
|.7|0.8|4.1<br>11.9<br>|
|.6|1.6|7.7<br>NS|
|.5|2.7|13.0<br>NS|

Notes: Variant 1 assumes CRTS<sup>across all sectors. Welfare costs measured by</sup> the equivalent variation expressed<sup>as a percentage of pre-rationing</sup> national income (e.g. 1.0 is one<sup>percent of national income).</sup>

# NS: No solution (the algorithm<sup>failed to converge).</sup>

- a/ Expresses constrained imports as<sup>proportion of unrationed import levels.</sup> b/ High (low) trade elasticities assume across-the-board price elasticities of import demand and export supply<sup>of 1.5 (0.5).</sup>
An important issue during the current pandemic has been the relationship between Covid-19 and global value chains, and particularly whether GVCs absorb or transmit Covid-19 shocks (Baldwin and Tomiura, 2020; Javorcik, 2020; Miroudot, 2020). We find that while GVC participation increased an exporter’s vulnerability to foreign shocks, it reduced vulnerability to domestic shocks. The disruption of production in input source countries more adversely affected export growth in sectors that relied more strongly on imported inputs from these source countries. Similarly, a disruption of production in an exporter’s partner countries more adversely affected its export growth in sectors with high shares of imported inputs. But the negative impact of a disruption in domestic production in exporting countries themselves was mitigated by a sector’s higher reliance on imported inputs in export production.

We then perform two robustness tests and extend our analysis in two directions. First, we use growth in industrial production rather than changes in mobility as a measure of the Covid-19 shocks and find that all findings remain unaltered. Second, we estimate the regressions for the sub-periods February to April 2020 and April to June 2020, to separate the two phases of the first wave of Covid-19. As intuition would dictate, we find that the mitigating effects of remote labor mattered more during the more severe lockdown of the first three months. Similarly, during these early months exports were also most affected by shocks in upstream countries. Third, we include China in the sample of exporters. All results are confirmed except that we now find that supply shocks in third countries provided opportunites to expand exports. As a final extension, we use mirror export data, to expand the set of exporters to 64 developed and developing countries. While we find that remoteness plays a smaller role in explaining differences in trade patterns across sectors, perhaps due to the weaker diffusion of internet in developing economies, all results continue to hold.

Our study relates to the growing body of literature on the economic effects of Covid-19 (Baldwin and Weder di Mauro, 2020). In particular, this paper is closely linked to recent studies assessing the impact of Covid-19 on international trade and GVCs, and on supply and demand shocks<sup>3</sup> more broadly. Several organizations have published estimates regarding the potential trade and GDP effects of Covid-19 using computable general equilibrium (CGE) models. While all studies predict a substantial drop in output and international trade flows, results are sensitive to changes in underlying assumptions, such as the length of the lockdown measures.<sup>4</sup> A recent study relies on a newly developed economic disaster model to assess the supply chain effects of different Covid-19 control measures across countries and sectors, emphasizing

> 4 Such studies include ECB (2020), Maliszewska et al. (2020), and WTO (2020).

3 For example Rio-Chanona et al. (2020) for the United States.

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5