rafmacalaba/data-use-mentions-extended huggingface dataset:
countries, we examined procurement laws that can come from national initiatives and ratifications of international treaties such as the Government Procurement Agreement of the World Trade Organization (WTO GPA) and Preferential Trade Agreements (PTAs). We read and coded the texts of these laws to identify instances of measures that can be characterized as protectionist. We classified them into three types commonly regarded as the main ones: lack of equal treatment provision, preferential treatment in favor of domestic bidders (e.g., preference margin), and domestic sourcing (e.g., “made in the country” provisions).

A descriptive analysis of the data reveals that the majority of countries in our sample adopt protectionist policies in their procurement regulations. Of 141 countries, 124 have preferential treatment provisions, and 37 percent of the legal systems in the sample lack a basic national treatment clause, while 60 percent of the countries apply domestic sourcing provisions. There is significant overlapping between the domestic sourcing and preferential treatment provisions: of the 83 countries where the law includes a “made in the country” provision, 81 also provide for preferential treatment in favor of domestic bidders.

We explore the main determinants of variation in protectionism across countries. As expected, we find that countries that participate in international agreements such as the WTO GPA and the EU have a lower protectionism index. That is also true of high-income countries (relative to the other, middle-income countries in the sample), and of countries with better institutions, as measured by the World Governance Indicators.

The main objective of our empirical analysis is to estimate the relationship between protectionist laws and trade openness. To achieve this, we leverage the variation in the adoption of discriminatory laws across countries and identify the correlation with the import share in total expenditure. Data on the value of bilateral imports by industry are sourced from the ITPD-E database, which includes information on domestic sales. By considering the relationship between procurement laws and total trade (whose cross-border procurement component represents a small part), we aim
One complication in this model is that the impact of capacity stress on the market depends on the number of vessels available. The 2021-22 episode caught the industry at the bottom of an investment cycle, where few vessels were available to compensate for the lack of velocity in shipping due to port delays. Since then, more vessels have been put in circulation and the disruptions in the Red Sea happened with more capacity slack than in 2021-22, hence the apparent reduced impact on rates.

Therefore, the connection between rates and stress is non-linear such as: ) Where Slack represents the reserve capacity of the industry at the time of the disruption. RRmmttmmiim mmRR ≃bbmmbbmmqqiim qqmmttmm+ αα∗RRm RR(SSttqqmmb −SSqqmmS Hence rates increase above this threshold of maximum capacity absorption. The RRm RR(RR) = Scattered data points (Figure 12), illustrate that in this regime the relationship is linear. RR i RR≥0, mmqqbbmm 0 The capacity slack prior to a disruption is expected to change slowly over time unlike disruption and stress ΔRRmmttmmiim mmRR (UUSSDD) ≃2300 ∗ΔSSttqqmmb (MMiiq iiMMmm T UU) index, which see fast changes. Unfortunately, there is no good proxy for this threshold: slack is expectedly higher in 2024 than in 2020 due to ship delivered since. The empirical analysis will try explaining the slope α of the connection between stress and rate.

Annex 3 provides a model by which shippers' willingness to pay depends, inversely, on the value of their goods per container. According to this model, stress, and rate changes are proportional. M<sup>mmbb vvmmqmm RRmmqq TUU</sup> Typical value of a container is computed as an average mean across commodities, hence representative of ΔRRmmttmmiim mmRR (UUSSDD) ≃<sup>TTyyRRiiSSmmqq ggM</sup> ∗ΔSSttqqmmb (MMiiq iiMMmm T UU) vviimmgg SSmmRRmmSSiittyy (MM T UU) Typical value of a container is computed as an average mean across commodities, hence representative of lower end values.

With the moving capacity for
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# **8. Concluding Remarks**

Pandemics are unique in that they disrupt human capital accumulation at both school and work. We present an accounting framework to improve our understanding of their impact. Poorer nations are _more_ likely to be insulated from schooling shocks since there is little schooling initially. Despite the lower losses relative to HICs and MICs, LICs are still heavily and durably impacted by the pandemic shock given preexisting vulnerabilities. Richer nations are _less_ likely to be insulated from employment shocks, due to higher returns to experience and higher quantity of schooling.

Through the lens of the COVID-19 pandemic, we find that the schooling shock is most significant for the middle- and high-income economies, causing significant economic damage in the long run. Even after 10 years, the world economy will still continue to suffer significant losses due to lost experience in richer nations. This emphasizes the role of experience in the global learning losses due to COVID-19.

Finally, by estimating global returns to education and experience separately for men and women using household surveys for 145 countries, differential effects by gender of the COVID-19 pandemic are explored. Surprisingly, the effect on female relative income is small and short-lived. This turns out to be because differences across genders in returns to human capital are too small to make much difference in transition dynamics. That said, for low income economies, the relative lack of schooling among women does turn out to be a factor worsening the impact of the pandemic somewhat.

Our analysis has several implications for policy. First, the huge losses that we estimated highlights the urgency of reopening schools. As of November 2021, about half of the global student population live in countries where schools are still not fully open. Second, our results show the importance of bridging the digital divide between and within countries and improving online education. Third, recovery from the pandemic may require investments in remedial education for students who experienced schooling loss. Fourth, it suggests that payment protection programs are likely preferable to unemployment insurance, at least to the
25.3<br>20.2|
|Share in new formal|77.1|16.6|
||_b. Brazil_||
|1|20.5<br>42.5|22.4<br>29.9|
|2|25.0<br>27.0|23.9<br>22.0|
|3|25.9<br>19.8|24.7<br>24.1|
|4|28.6<br>10.7|29.1<br>24.0|
|Share in new formal|66.4|25.7|
||_c. Mexico_||
|1|16.3<br>31.2|11.5<br>23.5|
|2|24.6<br>26.3|12.0<br>23.6|
|3|27.9<br>22.1|29.2<br>24.2|
|4|31.3<br>20.4|47.3<br>28.6|
|Share in new formal|58.1|6.5|

_Source_ : Authors’ calculations based on data from the 12-months Survey Panels from Labor Force Surveys. _Note:_ The table reports average yearly transitions to the formal sector from informal wage employment (columns 2 and 3) and self-employment (columns 4 and 5). Columns 2 and 4 classify movers according to their quartile in the wage distribution of origin: informal employees in column 2 and self-employed in column 4. Columns 3 and 5 classify movers according to their quartile in the wage distribution of destination (i.e., the wage distribution of formal workers). The “share in new formal” is defined as the share of those who formalize who were originally informal employees and who were originally self-employed
Disney et al. (2003), and Bernard and Jensen (2007). We include current plant size and size squared to account for possible non-linearities in the relationship with innovation, as well as the initial size at which the plant started operations.<sup>27</sup> Controlling for capital intensity ensures that the effect of product innovation is not picking up instead the effect of capital accumulation through process innovation. We also include a measure of plant sales growth to avoid capturing the effects of „desperate‟ innovators, i.e., plants which switch products in a desperate last attempt to avoid an inevitable closure. This control is particularly important for our analysis of the role of risk for the innovationsurvival link. Regarding industry controls, we include time-varying sales growth, the Herfindahl index of concentration of market shares, and the average innovation rate in the industry following Audretsch and Mahmood (1995), Audretsch (1995), Mata and Portugal (1994), and Strotmann (2007). We compute these measures at a highly disaggregated level, the 6-digit ISIC level.<sup>28</sup> For the Herfindahl index, we include both its level and its squared to allow for possible non-linear effects of competition on plant survival.

# **4. The Effects of Innovation on Plant Survival**

# **4.1 Baseline Results**

Table 3 presents our baseline estimates for the relationship between plant survival and product innovation using the discrete-time hazard models discussed in Section 3: cloglog, probit, and logit with random plant effects. Columns (1)-(3) present the results for the product innovation dummy while columns (4)-(6) present the results for the continuous product innovation variable. The significance of the estimates effects is assessed using heteroskedasticity-robust standard errors. The tables report the marginal effects (or elasticities) of each regressor on the probability of plant exit, evaluated at the means of the independent variables.

Columns (1)-(3
of financial development. This suggests a mean-variance “tradeoff” in quality growth along the development path. Rapid improvements in product quality will be more likely feasible in countries with greater financial depth.

Section 3 evaluates these theoretical predictions using data on disaggregated (HS10) bilateral exports to the United States for 1990-2000. Product quality is proxied by unit values.<sup>3</sup><sup>_,_4</sup> The advantage of using international trade data is that information on exports is available at a much finer level of disaggregation than domestic production, especially for a large sample of countries. The implicit assumption is that the export basket of any country to the United States represents its technological frontier, and any domestic innovations in product quality are reflected in changes in unit values of products in the export basket to the United States. Further, given both our theoretical focus and data limitations, we focus on quality changes in existing products (i.e. the “intensive margin”), rather than the introduction of altogether new products (the “extensive margin”).

Both of the main theoretical predictions discussed above find support in the data. The mean of quality growth and the variance of quality growth are strongly correlated with each other. Countries with greater financial depth exhibit systematically higher mean and higher variance in the growth of product quality.

Our results speak to a number of interrelated issues in the literature on economic growth, technology and convergence across countries. It is well established that countries have not converged in their income levels – despite a range of theoretical mechanisms identified by the literature, such as international technology transfer and trade that could drive convergence. Our findings are consistent with the lack of income convergence across countries because they suggest that the combination of risk associated with technology upgrading and institutional weaknesses that prevent insurance against risk may be a barrier to technological adoption. The implied lack of convergence in product quality is also consistent with the well known finding by Schott (2004) that higher income countries
# **I Introduction**

Intergenerational mobility (IGM) has gained interest in the economic literature among other things thanks to its importance for equity, social cohesion, and economic growth. Its observed correlation with income inequality, commonly named “the Great Gatsby Curve”, has contributed to the desire for understanding IGM given the documented rise of inequality over the last decades in rich countries (see Corak, 2013).

In the case of the developing economies, the Latin America and the Caribbean (LAC) region is of particular interest because of its historically documented high levels of income inequality relative to other regions of the world.<sup>1</sup> However, the scarcity of high-quality data (e.g. long panel data sets or tax records with linked generations) has limited the study of IGM in income. These constraints are also common in other regions (e.g., Africa), so efforts to document IGM on a global scale have taken an alternative path given by the measurement of mobility in education. These measures are of interest in and of themselves, but they are also a proxy for economic status given the close relationship between education and income.

Recent studies have used household and public opinion surveys with retrospective information about parents’ education to document the levels of IGM in education in LAC at the country level (for an example, see Hertz et al., 2007; Narayan et al., 2018; Neidh ̈ofer, Serrano, & Gasparini, 2018). However, analyzing IGM at a more geographically disaggregated level, as argued in Narayan et al. (2018), is valuable because it can help researchers understand the importance of localized patterns and drivers of IGM, as shown for the case of developed countries. Along these lines, Chetty, Hendren, Kline, and Saez (2014) state that the United States can be better described as a collection of societies, some of which are “lands of opportunity” with high rates of mobility across
n=5); all regressions use IPW and sampling weights; column (4) presents the main results; columns (1) and (7) are estimated by trimming the top/bottom of the treatment group by the difference in attrition between the treatment and control, as in Lee (2009); columns (2) to (3) and (6) to (7) replace outcome values for the attritors with +/- 0.x standard deviations of their respective treatment group-wave means as in Kling and Liebman (2004), lower bounds subtract this value from the treatment group and add it to the control group and vice versa for the upper bounds.

The reader may also wonder whether the changes in household size are due to differential reporting between the treatment and control households. As the surveys were overseen by the TRC, it is possible that the households associated the program with the surveys. However, as explained in Section 2, program eligibility in Turkey is exclusively determined by refugee families’ formal registration status in the program databases. Taking in or reporting more children would not change a household’s eligibility status unless the new child(ren) were officially registered with the receiving family. Moreover, beneficiary households have no incentive to inflate the number of children reported at 6-month follow-up since they have already been declared eligible. Therefore, we deem it unlikely that the changes in household size that we observe are due to differential reporting bias.

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Data and technical documentation are available for download from `https://cds.climate. copernicus.eu` .

# **A.1.5 Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2)**

The second reanalysis data set used in this analysis is MERRA-2, a product of NASA’s Goddard Earth Observing System, version 5 (GEOS-5) assimilation model. Specifically we make use of the variables T2MMEAN from the statD daily statistics collection, and PRECTOTLAND from the Land Surface Diagnostics collection.

Data and technical documentation are available for download from `https://disc.gsfc.nasa. gov/` .

# **A.1.6 Tropical Applications of Meteorology using SATellite data (TAMSAT)**

The TAMSAT rainfall data set is the highest spatial resolution gridded data set used in this analysis. Inputs are similar to other merged gauge and remote sensing products: Meteosat TIR 102
El Salvador Mexico LAC countries<br>.6 .6<br>.4 .4<br>Exporter Exit Rate .2 Exporter Exit Rate .2<br>0 0<br>South Africa Peru Chile Costa Rica Ecuador Guatemala Dominican Rep El Salvador Nicaragua Bangladesh Cambodia Mexico LAC countries South Africa Colombia Peru Chile Costa Rica Ecuador Dominican Rep Guatemala El Salvador Nicaragua Bangladesh Cambodia Mexico LAC countries<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>Survival Rate of New Exporters Survival Rate of New Exporters<br>0 0<br>Chile Costa Rica South Africa Ecuador Peru Guatemala Nicaragua El Salvador Dominican Rep Bangladesh Cambodia Mexico LAC countries Chile Peru Colombia South Africa Costa Rica Ecuador Cambodia El Salvador Guatemala Nicaragua Bangladesh Dominican Rep Mexico LAC countries<br><!-- End of picture text --> Notes: in Panels A, C, and E the averages for Ecuador cover 2006-2007. Colombia does not enter in Panels A, C, and E due to lack of data. Brazil does not enter in the figure due to lack of access to the exporter-level customs data. Agriculture sectors are HS 01-24. Ores and Metals are HS 25-27 and 71-81. Industrial sectors are HS 29-49, 64-70, and 82-97. Textiles and apparel sectors are HS 50-63.

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**The gap of microfinance in OIC is not only demonstrated by its limited scope, but also by the lack of regulation in OIC countries compared to other developing countries.** In MENA region for example, only Egypt, Morocco, Syria, Tunisia, and Yemen have specific legislation for Microfinance institutions (Figure 4). Also, according to the CGAP financial access database (Figure 5), 68% of the covered 37 OIC countries have appointed agencies to promote access to finance in rural areas, while 57% of the countries have dedicated team or unit in charge of the task. The ratios are above the developing countries average, but below the low-income countries average.

**Figure 4. Microfinance regulation and supervision by region**

<!-- Start of picture text --> 100<br>90 20<br>7.7<br>7.8<br>80<br>70<br>30 35.4 Supervised only by other<br>60 42.7<br>regulator<br>50<br>Supervised only by a bank<br>40 regulator<br>30<br>Not regulated<br>50 47.7<br>20 37.9<br>10<br>0<br>MENA (%) Developing All (%)<br>Countries (%)<br><!-- End of picture text --> Source: Peace (2010), M Khaled, CGAP (2010)

**Figure 5. Who is responsible for rural finance promotion?**

<!-- Start of picture text --> 90%<br>79%<br>80%<br>68%<br>70% 62%<br>57%<br>60%<br>47% OIC countries<br>50%<br>36%<br>40% Developing countries<br>
average, the natural growth of NTL levels is such that a 1-unit increase in past NTL value in an IOM settlement always raise the current NTL, as long as past NTL values are smaller than 2.2, which is always true in the Badghis sample.<sup>8</sup>

# 4.2. Impact of displacement flows on NTL growth

New population flows settling in the location are likely to impose deviation from the NTL growth, e.g., through settlements’ expansion, growing services needs, or the modification in the economic activity. High inflows of the displaced population are associated with a growing need for basic electricity services such as indoor and street lighting. In addition, they could directly impact settlements’ economic activity, e.g., by modifying the repartition of the labor force throughout the different sectors or through a direct increase in the settlements’ population. As NTL levels are traditionally used to monitor human activities, see e.g., Li et al. (2018), important influxes of persons are expected to impact the evolution of NTL levels. This note therefore does not assume a fixed growth rate of NTL but rather compares growth rates across settlements and provides insights on why some settlements have higher growth rates than others - reflecting an additional inflow of migrants but also increased economic activity.

A linear regression at the settlement level allows us to check for correlation between NTL growth and overall displacement flows between 2018 and 2020.<sup>9</sup> The following regression looks at how the NTL growth across time is affected by _Net Inflow_ , the cumulative net inflow of persons in settlement over the 2018-2020 period. The regression controls for the initial population in 2017, the share of community income derived from agricultural and livestock activities in late 2020, time seasonality ( μ the time fixed effect) and the fixed effect for district _j_ . For visibility’s sake, let’s label _X := Net Inflow_ , the regression is hence: tt jj
Cameroon to –5.0 percent in Liberia. In contrast, informality increased in Uganda by an annualized 4.1 percent and in Zambia by an annualized 0.9 percent. The composition of the informal sector changed in different ways across countries.<sup>45</sup> These short-term trends may be happening within longer-term patterns of changes in the structure of employment with a growth in wage employment and declines in self-employment.<sup>46</sup>

> 45 Kiaga and Leung (2020) also find non-monotonic movements toward formality over a long-period time series.

> 46 A cross country analysis of 59 countries over 16 years found that as countries become less poor, certain types of formal and informal jobs types are displaced by formal sector wage employment. The  share of wage employment steadily increased with GDP per capita for low and middle-income countries, due almost solely to increases in formal wage employment, which accounted for about 20 percent of all employment types in

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<mark>welfare standard widely accepted to ensure a minimum standard of well-being, and (iii) the estimation methodology.</mark>

# <mark> The Reference Group</mark>

<mark>The ‘reference group’ represents a group of households whose expenditure patterns provide an adequate description of what is required to</mark> _<mark>escape</mark>_ <mark>poverty in the given country context. Thus, it typically includes households that are neither among the poorest nor the wealthiest in a country, ideally reflecting the needs of those who are ‘around’ the existing poverty level. One way to ensure this is to select from among households in the lower ‘middle’ of the distribution of expenditure. In practice, this implies excluding households in the bottom decile and sometimes the bottom quintile of the expenditure distribution. Similarly, it is not recommended in practice to include households that are much above the median expenditure level. That said, the choice of a reference group is ultimately subjective and relies heavily on the subjective notions of poverty in the country.</mark> <mark>In Rwanda, the 2001 poverty line was based on the expenditure patterns of households in the bottom three quintiles of the distribution of consumption expenditure. This was done because poverty headcount in 2001 was between 60-65 percent. Similarly, in 2014 NISR used households in the bottom two quintiles as the reference group—as the poverty rate from the last available survey at the time (EICV3) was around 45 percent.</mark> <mark>Including the bottom decile or sometimes the bottom quintile in the reference group is tricky as the tail has a lot more noise than the lower middle of the distribution. As mentioned earlier, it is best to choose a reference group that is around the poverty line, reducing sensitivity to measurement error in extreme values.</mark>

- <mark>The Normative Standard – a Caloric Threshold</mark>

<mark>