rafmacalaba/data-use-mentions-extended huggingface dataset:
In sum, we find evidence of a compositional effect in China’s exports during the Great Recession. The sectors that contributed most to the export collapse are those where vertical specialization is more prevalent.<sup>21</sup>

# _6.1.2. Trade cost effect_

Next, we want to investigate if there is evidence of a trade cost effect in China’s trade during the Great Recession. As we have shown in Section 5, intra-GPN trade is generally more sensitive to trade costs than regular trade. If trade costs have risen significantly during the Great Recession, we therefore should find that processing exports have consistently dropped more than non-processing exports from the first quarter of 2008 to the first quarter of 2009.

To adequately control for compositional effects, we conduct the analysis at the most disaggregated level in China’s trade data – the HS 8-digit level. Specifically, we examine whether, at the HS 8-digit level, there is evidence that the share of China’s processing exports in total exports has significantly declined from the first quarter of 2008 to the first quarter of 2009. The results are presented in Table 9. In line with the trade cost effect, the t-test on equality of means finds that the share of processing exports in total exports was significantly lower in the first quarter of 2009 than a year earlier.

# **[Table 9 about here]**

In sum, we find evidence of a trade cost effect in China’s exports during the Great Recession. After controlling for compositional effects, processing exports have consistently dropped more during the Great Recession than non-processing exports.

# _6.1.3. Bullwhip effect_

Finally, we want to investigate whether there is a bullwhip effect in intra-GPN trade. In other words, we want to analyze if the percentage decline in China’s processing imports exceeded the percentage decline in processing exports during the Great Recession. A first indication of a bullwhip effect is that, despite China’s relatively robust economic growth

> 21
dowry offer comes with the risk of not being able to find a suitable groom for the daughter—a highly undesirable outcome for Indian parents. Moreover, as the _net_ number of daughters—defined as the difference between the number of daughters and the number of sons—increases, there is a significant increase in the difference between dowries paid for daughters and dowries received for sons. Thus, firstborn gender can be considered a quasi-random dowry shock to the household.

However, simply comparing FG and FB families is also not sufficient. Differential returns from (or costs of) a firstborn-son versus a firstborn-daughter may be due to factors other than dowry, such as higher male productivity in agriculture. FG parents may also save more relative to FB parents to support themselves during old age, as the custom of patrilocality implies that daughters (and not sons) move out of the natal home upon marriage. To explicitly take into account the contribution of dowry expectations to savings, we, therefore, interact the FG dummy with the expected dowry variable. This allows us to control for the main effect of FG, that will capture any changes in per capita saving due to firstborn sex that could result from factors unrelated to dowry; for instance, higher fertility among FG families due to the desire for at least one son.

Ideally, we would like to use data on household savings before the birth of the first child and

> 29Anukriti et al. (2016) show that there was no significant change in the sex-composition of first births for any of the socioeconomic groups after ultrasound technology became available. Although the vast majority of Indian families want to have a son if they can only have one child, at a family size of two they prefer having one daughter and one son over having two sons (Jayachandran (2017)). As desired and actual fertility in India are well above one, it is reasonable to assume that parents are not averse to having one girl, despite a strong desire for at least one boy. A formal
unemployment experience of formal and informal workers, by country. 2004-2009**

_Note:_ The sample size of employees without contracts is less than 30 for IT, SE and FI; between 50 and 90 for CZ, RO, SK, HU, EE, BE, FR, DE and CH; between 100 and 745 for other countries. _Source:_ Calculation with ESS data.

> 21 Results of this section are based on ESS data only and for some countries should be interpreted with care due to limited number of observations on informal employees.  See Note to Figure 14  for details.

34
total time taken to make the trip, including time spent stationary at ports, border crossings, and other stops Source **:** Teravaninthorn and Raballand 2009.

According to the 2010 International Logistics Performance Index (LPI),<sup>1</sup> the costs associated with logistics in East Africa are higher than in any other region in world, including West Africa. This is a major change from a few years ago when the LPI in West Africa was considered the worst. In recent years, however, East Africa has been constrained by inadequate infrastructure and customs performance, as well as difficulty in clearing borders (World Bank 2010b). Overall traffic and trade flows in East Africa have also increased without a corresponding improvement in the policy environment. At the country level, Uganda’s LPI ranked at 66 out of 155 countries, a significant improvement since the previous LPI ranking. The low ranking of Rwanda suggests that the country is constrained by logistics (World Bank 2010a).

The domestic LPI suggests that average lead times for exports and imports in East Africa are the highest and second-highest in the developing world, respectively. This is also a change from previous estimates. Freight from landlocked countries in East Africa that must cross borders suffers economic and time penalties when compared with coastal countries. Among countries in East Africa, clearance times for landlocked countries are around five times higher than for those with port access. Among logistics operators surveyed as part of the LPI study, 60 to 75 percent of the respondents from East Africa indicate that the port, airport, and road transport rates are exorbitant, while 45 percent of respondents from the rest of the world indicate that the rates are high or very high.

In comparison to West and central Africa, the trucking industry in East Africa is more competitive and mature. Freight transportation rates are determined by market forces rather than government regulations. In Rwanda, deregulation of the trucking industry illustrates the close correlation between high transport prices and cartelization (box 2.1
degree of overlap in skills and duties of the workforce of consolidating firms (Lee, Mauer and Xu, 2018). Higher HCR implies greater potential economies of scale and therefore potential job loss for incumbent workers. We use individual-level skill data to generate a measure of human capital relatedness following an analogous approach to the occupation-based measure used in Lee, Mauer and Xu (2018).

To construct our HCR index, we flag all workers who are present at firm _j_ four years prior to the takeover and earlier (that is, we do not incorporate observations from _t −_ 3 onward). We restrict the sample to firms where we have at least ten observations of employee skills in this period. We then create firm _j_ ’s human capital profile vector _Hj_ , which indicates the number of firm _j_ ’s workers that fall into each of three given skill categories divided by the total number of workers at that firm _j_ : _Hj_ = ( _H_ low _,j, H_ medium _,j, H_ high _,j_ ). For a given takeover with acquirer _A_ and target _T_ , we then construct a time-invariant HCR index defined as The HCR index is increasing in the overlap of skill categories of the two firms; it is equal to zero if the firms employ workers of completely different skill levels and is equal to one if the distribution of workers across skill levels is identical in the two firms. As seen in Figure 8, the distribution of the HCR index is skewed with 83%, 66%, and 28% of events having an index over 0.8, 0.9 and 0.95, respectively.

Do takeovers between firms with higher HCR generate higher rates of job loss? Figure 9 shows the percentage change estimates of retention for events with above and below median HCR. We show results for both overall retention as well as hours worked at the consolidated firm, post-takeover. We see that the highest HCR events have a modestly higher impact on retention than lower HCR events:
allowance program offered some relief against energy poverty for the poor before the reform, there is room for improvement in its effectiveness. Challenges such as low coverage of social assistance programs, stringent eligibility criteria, and a lack of clear administrative rules for the heating allowance program likely contributed to limitations in its effectiveness.

# III. Methodology and Data

## Data

### **This study uses microdata from different sources, including an income survey, a household budget survey, budget data from fiscal accounts and other administrative registries, and estimates of a minimum consumption basket from previous studies.**

### **This study relies primarily on the cross-sectional component of the 2021 Bulgaria SILC (Survey on Income**

**and Living Conditions).** The primary aim of the income and living conditions survey, as part of the European Statistical System, is to furnish timely and comparable data on income distribution and social inclusion. The nationally representative survey delivers insights into current (cross-sectional data) and longitudinal changes in income, poverty levels, and social exclusion. The reference population for EU-SILC comprises all private households and their residents within the country's territory during the data collection period. Individuals in collective households and institutions are generally excluded from the target population. Until 2015, the survey operated on a four-year rotational panel from private households, with a sample size of approximately 7,300 addresses/households distributed across all regions annually. All members aged 16 years or more in the sampled household are surveyed. Household participation spans four consecutive years, with a rotational design that facilitates both cross-sectional (current year data) and longitudinal (data for households participating in the survey for at least two consecutive years) insights. Each year, one rotational group is replaced by another to maintain the continuity of data collection. The survey methodology encompasses various social areas, including basic demographic characteristics of households and their members, monetary indicators reflecting living standards and social stratification, non-monetary indicators related to living standards, economic activity, employment
- _Heavy borrowers_ are countries with cumulative public debt variation greater than 20 percent of GDP from 2012 to 2017. The cumulative variation of public debt for heavy borrowers is, on average, 36.9 percent of GDP and the level of general government gross debt is 77.4 percent of GDP. This group includes the following countries: Angola, Benin, Cabo Verde, Central African Republic, Chad, Congo Rep., Equatorial Guinea, Gabon, Ghana, Mozambique, Namibia, Sierra Leone, South Sudan, Sudan, The Gambia, Togo, Zambia, and Zimbabwe.

**Figure 18. Public Debt in Sub-Saharan Africa, by intensity of debt accumulation**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Light borrowers Moderate borrowers Heavy borrowers<br>2012 Level 2012-17 Change<br>Percent of GDP (weighted average)<br><!-- End of picture text --> _Source: International Monetary Fund, World Economic Outlook Database._

**Figure 18** shows the evolution of public debt stocks over the past five years across the different groups of SSA countries classified by their debt intensity. This figure corroborates the conjecture that there is a great deal of heterogeneity in the pace of public debt accumulation over the past five years. On average, heavy borrowers already had the largest public debt stock among SSA countries but relatively contained at about 40 percent of GDP in 2012. At the same time, the group of heavy borrowers experienced the largest accumulation of public debt from 2012 to 2017. Public debt stocks of heavy borrowers in the region almost doubled by 2017 (i.e. the average level of public debt amounted to 77.4 percent of GDP).

There is variability across countries in both the level and pace of accumulation of public debt within the group of
# **1. Introduction and Background**

Understanding peoples’ productive activities over the course of the year is important both to document how people earn their livelihoods and also to ensure that poverty programs are well designed and well-targeted. Measuring employment is especially challenging in settings where productive activities are informal, with irregular intensity of participation, and seasonal, where much of the effort and earnings are concentrated in specific periods of the year (Feuerbacher et al. 2020). These characteristics are particularly relevant in rural labor markets in low-income countries that rely heavily on employment in the agriculture sector (Fink, Jack, and Masiye 2020; World Bank, 2017).

The prevailing survey methodology literature considers shorter recall periods to be more accurate, often relying on a standard seven-day recall window (Durazo et al. 2021). However, relying on reported labor activities over the most recent seven days is likely to miss individuals’ full range of labor contributions and work activities over a full year, especially in highly seasonal and informal labor markets. To address this gap, standard data collection practices typically also include questions about productive activities of each household member over the past year.<sup>1</sup> While existing evidence is mixed, use of such a long window may lead to losses in data and data quality.<sup>2</sup> In this paper, we use a survey experiment to test the effect of recall windows on reported annual work behaviors, contrasting reported labor participation using a long (annual) recall method to those resulting from a set of quarterly interviews that follow a similar structure and set of questions, but over shorter, 90-day, recall windows.

A commonly used format for annual recall labor questions in multitopic household surveys asks the household’s primary respondent to report on their labor activities over the preceding year, including type of activity, periods worked, and intensity of participation. These questions are asked about their main activity over the year as well as any secondary activity. While
Review 99(1)_ , 528–543.

- Todaro, M. P. and L. Maruszko (1987). Illegal Migration and US Immigration Reform: A Conceptual Framework. _Population and Development Review 13(1)_ , 101–114.

- Treisman, D. (2007). What Have we Learned About the Causes of Corruption from Ten Years of Cross-national Empirical Research? _Annual Review of Political Science 10_ , 211–244.

- Wilhelm, P. G. (2002). International Validation of the Corruption Perceptions Index: Implications for Business Ethics and Entrepreneurship Education. _Journal of Business Ethics 35_ , 177–189.

11
Ln(GDP per capita) –mean(Ln(GDP per capita))|-0.210***|-0.126*|-0.129|
||(0.076)|(0.074)|(0.180)|
|Observations|50,843|48,133|2,710|

Note: ***p < 0.01, **p < 0.05, *p < 0.1. Standard errors in parentheses. Standardized weights are used that give each country equal weight. The standard errors are clustered at a sector × country level. Measures for GVCB share and GVCF share are standardized. All regressions include controls for technology (computer use, computer use squared), FDI, skills, education, age, gender, sector FE, and sector FE interacted with GDP per capita.

Source: Authors’ calculations based on PIAAC, STEP, CULS (tasks), World Bank (GDP), EORA data and Borin and Mancini (2015, 2019) (GVC participation measures).

In business services, we find a negative association between backward GVC participation and workers’ RTI (Table 2, Panel B, column 1). Due to the positive interaction term with GDP per capita, this effect is weaker in countries at higher development levels: it disappears in countries with GDP per capita twice the sample average, comparable to the United States, and becomes positive in countries with even higher GDP per capita (e.g., 10
US antidumping data (Blonigen, 2000), thus eliminating a substantial entry barrier to researchers interested in examination of the US process.<sup>7</sup> The global antidumping database project described in this paper seeks to emulate the basics of Blonigen’s data collection efforts and complement the data generated for the United States by establishing a relatively comprehensive, publicly available database on other countries’ use of antidumping. The purpose of this paper is to briefly describe the collected data, so as to provide researchers a starting point for examining other (non-US) users of the controversial trade policy. Our theory is that providing such data will eliminate some entry barriers to research in this area. We hope that additional entry by researchers will result in improved quality of empirical research on the global use of antidumping, like the Blonigen data collection effort has done for the United States’ use of the policy. A last goal would be that results of scientific research would lead to additional transparency and understanding as to what is actually taking place under the guise of antidumping laws and policy around the world.

With respect to coverage, table 1.1 illustrates the countries that are included in the detailed portion of our database, as a subset of antidumping users (WTO members) over the 1995-2004 period. While close to 50 countries have now adopted antidumping laws and initiated antidumping investigations (Zanardi, 2004), it is still the case that most of the use of antidumping – as measured, for example, by total number of investigations or measures imposed – is highly concentrated into around a dozen countries. Thus while the current database contains detailed antidumping use data for (only) 15 of these policy-imposing countries, these countries collectively make up a substantial fraction of the policy’s use – e.g., these countries were responsible for 85% of the antidumping investigations and 87% of antidumping measures imposed by all WTO members over the 1995-2004 period.

> 7 Thanks go to Bruce Blonigen for his collecting and original conception of
3 in high-income economies, compared to 2 _._ 2 percent in middle-income economies and 1 _._ 5 percent in low-income economies.<sup>2</sup> After ten years, when the direct impact of the shock has worn off, the welfare impact is 1 _._ 2 percent in high-income countries, compared to 1 _._ 2 percent in middle-income countries and 0 _._ 7 percent in low-income countries.<sup>3</sup> Finally, at the beginning, the employment shock accounts for 84, 70 and 75 percent of the welfare cost going forward in high-income, middle-income and lowincome countries respectively (73 percent for the world). There is still, however, some enduring cost from lost experience, particularly in high-income countries. After 10 years, experience accounts for 52, 21 and 27 percent of the enduring cost in high-income, middle-income and low-income countries respectively (27 percent for the world).

Lastly, the policy literature suggests COVID-19 had a disproportionate impact on female workers.<sup>4</sup> To explore this issue, we repeat our quantitative exercise separately for men and women. We accomplish this through several steps.

First, we find lower returns to experience for women, especially in richer nations. While this may be surprising given the lower rates of discrimination in richer nations, returns to experience are lower in poorer nations, which constrains the degree to which returns for men and women can differ in absolute terms. In contrast, returns to education are higher for women, possibly because education is a stronger signal of productivity for employers when experience is discounted due to discrimination. Second, we use these returns along with data on the schooling and experience distributions for men and women to compute the impact of the shock on the income of men and women separately. We find that differences are small, peaking on impact at 2 _._ 5% in high and middle income countries, and at just 1 _._ 7
Misreporting of diary entries affects equation (1) by introducing a disturbance term on the right-hand side.

# **2.3 Recall Interviews**

Many household surveys implemented in the developing world ask respondents to recall quantities in a preselected list of food items consumed over a fixed period and their associated market value. In our case study, the recall period is seven days, ending with the interview (Lyberg and Kasprzyk, 2004, Beegle et al., 2012, and Crossley and Winter, 2014 discuss the effects of changing this time window). Information is collected in two steps. The respondent is first asked if the items in the list were consumed during the past week, going vertically down in the survey form from the first item to the last. For the items for which there is a positive response, the quantity consumed and the unit of measurement are reported. The respondent then must backcast the monetary value of this quantity by reporting estimated expenditures (if market purchased) or market values (if self-produced, received as a gift, or received in barter). The monetary equivalent of recalled consumption (quantity consumed times implicit price per unit, or unit value) is the quantity we consider for the measurement _Y_<sup>_r_</sup> .

Why should one expect differences between _Y_<sup>_∗_</sup> and recalled consumption _Y_<sup>_r_</sup> ? As discussed above, consumption in the week before the interview need not coincide with usual consumption. Arguments can be made in favor of using a longer reference period for recall questions, or asking directly about “usual week” consumption. However, limited cognitive abilities and the difficulty of recalling the timing of events may challenge respondents’ ability to perform the computations. Estimating the monetary equivalent of the quantities consumed from non-market sources adds an additional layer of difficulty. Errors may arise because the respondent solves a prediction problem rather than reporting a noisy measurement. Households may form their answers using the information