rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Data Description**

The data set used in this paper is the British Household Panel Survey (BHPS). The data is collected on households and individuals aged 16 or older, once a year between the months of September and May. It covers a representative sample of the British population during 16 years (1991-2006), providing information on almost 10.000 households over the period. For waves 1 to 8 there are around 10.000 individual observations by year, whereas for waves 9-16 there are on average 15.000 yearly individual observations<sup>4</sup> . This means that the panel is unbalanced: during the sixteen years of the survey, some households and individual leave while others enter the sample.

We keep in the sample individuals aged between 21 and 65 years who are active in the labour force - either working or actively looking for a job. Given that we keep only working age population, the mean age in the sample changes to around 40 years old, as opposed to 45 for the whole survey. Table A.1 in the appendix provides a summary of statistics for the variables in the survey.

The measure of well-being used is derived from the General Health Questionnaire (GHQ). It has been designed by Goldberg (1972), and is widely used by psychiatrists to assess a person’s well-being. The 12 questions asked are provided in the appendix. As presented by Argyle (1989, 2002) the GHQ is one of the most reliable indicators of psychological distress. I use an ”inverted Caseness score”<sup>5</sup> . The distribution of this variable is given in table A.2, in the appendix. It is observable that the distribution is highly skewed to the top, with most of the respondents scoring 12, the highest possible grade. Only a quarter (26 %) scores less than 10. Looking at the distribution of GHQ by employment status, one can note the following two facts. First, the mean of GHQ is significantly lower for unemployed persons, if one compares them to the employed or self-employed population (9,1 for the unemployed vs. 10,4 for the employed and 10,1 for the whole population sample.). Second, the distribution of GHQ has a larger variance in the unemployed group, where 26% of the respondents declare a low well-being (defined as a score inferior to 8), against 13% and 12% for the employed and self-employed. These distributional characteristics are clearly presented in Table 1 and Figure 1, below.

> 4In 1999, 1500 households were added from both Scotland and Wales. In 2001, another 2000 households were added from Northern Ireland. More information can be found on the BHPS at http://www.iser.essex.ac.uk/survey/bhps

> 5There are 12 questions, and the score ranges from 0 to 12. Individuals start with a score of 12, and for each question in which they are fairly or highly stressed, they lose a point. Psychiatrists use the GHQ : individuals with low levels of Caseness are eligible for their treatment (Argyle, 2002).

7
( _match picture_ ) but null effects on language tasks associated with writing the name of everyday items ( _mention objects_ ). For the mathematics section, we similarly find large positive effects (0.125 S.D.) on tasks associated with ordering sequences of numbers from largest to smallest and vice versa ( _order numbers_ ) but null effects on solving addition problems ( _summation_ ) _._ Finally, we find no impact on abstract reasoning. Overall, we do not detect treatment effect variation in the test score results, either by wealth or by parenting practices.<sup>14</sup> As was the case for the EDI outcomes, test scores also seem to have ceiling effects associated with them, which may undermine our ability to detect certain effects.<sup>15</sup> <u>Table 4. Impact on primary school test scores for the younger cohort</u>

|**Outcome**||**All**|**Poor**|**Non-poor**|**Low**<br>**Parentin**<br>**g Score**|**High**<br>**Parenting**<br>**Score**|
|---|---|---|---|---|---|---|
||Coeff.|0.134*|0.029|0.213**|0.179**|0.080|
|Language - match picture|(S.E.)|(0.058)|(0.081)|(0.068)|(0.067)|(0.077)|
|(SD)|Comp. mean|-0.0827|-0.146|-0.0355|-0.163|0.00282|
||Obs.|2,862|1,274|1,588|1,814|1,048|
||Coeff.|-0.027|-0.065|0.006|-0.030|-0.028|
|Language - mention objects|(S.E.)|(0.053)|(0.067)|(0.065)|(0.062)|(0.063)|
|(SD)|Comp. mean|0.0333|-0.108|0.138|-0.0354|0.106|
||Obs.|2,862|1,274|1,588|1,814|1,048|
||Coeff.|0.068|0.059|0.075|0.055|0.082|
|Mth ti SD|(S.E.)|(0.054)|(0.077)|(0.061)|(0.067)|(0.068)|
|a - summaon ()|Comp. mean|-0.0407|-0.166|0.0521|-0.0730|-0.00628|
||Obs.|2,862|1,274|1,588|1,814|1,048|
||Coeff.|0.125*|0.108|0.143*|0.067|0.182*|
|Mth d b SD|(S.E.)|(0.058)|(0.083)|(0.067)|(0.068)|(0.073)|
|a - orer numers ()|Comp. mean|-0.0828|-0.214|0.0143|-0.0905|-0.0745|
||Obs.|2,862|1,274|1,588|1,814|1,048|
||Coeff.|-0.022|-0.012|-0.032|-0.020|-0.023|
|Abtt i SD|(S.E.)|(0.044)|(0.064)|(0.056)|(0.058)|(0.059)|
|srac reasonng ()|Comp. mean|0.0310|-0.102|0.130|-0.0317|0.0977|
||Obs.|2,862|1,274|1,588|1,814|1,048|

p<0.001***; p<0.01**; p<0.05* Note: Each cell-block is the result of a separate regression. Standard errors clustered at the village level are in parentheses. “Comp. mean” refers to the comparison group mean for the outcome variable. Column (1) regressions control for child characteristics (age, household size, household wealth asset index, parenting practices, mother's education and gender) and baseline child development measures (BMI, cognitive, fine motor, gross motor, and language). Columns (2) and (3) regressions use the same controls as column (1) except they exclude household wealth. Columns (4) and (5) regressions use the same controls as column (1) except they exclude parenting practices.

> 14 Appendix Table 6.

> 15 See Appendix Figure 2.

14
<!-- Start of picture text --> Data Privacy Complaints and Data Breach Notifications Received v. Fines Imposed in<br>EU<br>80,000 100<br>70,000<br>80<br>60,000<br>60<br>50,000<br>40,000 40<br>30,000<br>20<br>20,000<br>0<br>10,000<br>0 -20<br>Complaints Received Since May 2018 2019 Data Breach Notifications<br>Number of Fines Imposed as of June 2019<br>178<br>Number of Fines Imposed<br>Number of Complaints and Data Breaches Received ✢ ✢U.K. ✢ Spain ⌯ ✤ ✢Italy ✢ Ireland ✢ Sweden ✢ ✢ ✢ ✢ Latvia ✤ ✢ ✢ ✤ ✢ ✢ Slovakia Greece ✤ ✢ ✢ ✢ Malta<br>Germany Netherlands France Poland Romania Czech Republic Austria Hungary Denmark Bulgaria Slovenia Croatia Lithuania Portugal Finland Luxembourg Cyprus Estonia Belgium<br><!-- End of picture text --> ✤ _data concluded on October 31, 2019,_ ✢ _data concluded on November 30, 2019,_ ⌯ _data concluded on December 31, 2019._ Despite the large volume of complaints submitted, the number of fines issued in the first two years of the GDPR’s operation has remained low. By February 13, 2021, EU nations (including the UK) had issued 514 fines under the GDPR, totaling € 275,860,338.<sup>179</sup> Spain takes the quantitative lead, having imposed 91 fines to date since the GDPR’s inception, though the Spanish DPA received 18,480 complaints and 1,434 reports of data breaches since May 25, 2018.<sup>180</sup> The United Kingdom and Germany — while having the largest DPAs in terms of both budget and staff compared to other Member States — have imposed just 4 and 28 fines, respectively, at the time of this writing.<sup>181</sup>

> 178 European Data Protection Board, _Individual replies from the data protection supervisory authorities_ (2020); CMS Law, _GDPR Enforcement Tracker_ (2020), https://www.enforcementtracker.com.

> 179 CMS Law, _GDPR Enforcement Tracker_ , 2021, https://www.enforcementtracker.com/?insights.

> 180 CMS Law, _GDPR Enforcement Tracker_ , 2021, https://www.enforcementtracker.com

> 181 _Id_ .

31
# **Export Performance and Trade Facilitation Reform:**

# **Hard and Soft Infrastructure**

## **Alberto Portugal-Perez**

**John S. Wilson**<sup>**1**</sup>

## **The World Bank**

_Keywords: Export performance, trade facilitation, soft and hard infrastructure, gravity JEL Classifications F10, F17_ 1 This paper is part of a research project underway in the World Bank’s Development Research Group on trade costs, facilitation, and economic development. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Without implicating them, the authors thank Bernard Hoekman, Norbert Fiess, Caroline Freund, Pravin Krishna, Daniel Lederman, Jean-Christophe Maur, Jaime de Melo, Marcelo Olarreaga, Roberta Piermartini, and participants in the Research Seminar held at the WTO in Geneva, as well as anonymous referee, for valuable discussions and comments on earlier versions of the paper. The authors also thank Mario Gutierrez and Marco Antonio Martinez del Angel for excellent research assistance.
simultaneously and are of similar orders of magnitude, they mainly increase the equilibrium volume produced without any major movements in equilibrium prices. This reasoning applies to the _agrpweff_ and _indupweff_ scenarios, whereas in _transeff_ the shock takes place only on the supply side, also in this case shifting the curve to the right. In all the scenarios, the shocks lead to significant increases in GDP growth. In base year 2008, non-food manufacturing exports had a higher share of total exports than food industry (40 percent vs. 31 percent). In addition, as the initial export share of production is highest for non-food industries (see Table 1 above), making it easier for these industries to expand exports, the total exports grow fastest under _indupweff_ (see Table 8 and Figure 13). In addition, in contrast to agriculture and food industries (through the input use), non-food industries are not dependent on the inflexible supply of land (factor of production), making the shift of resources to non-food industrial activities smoother. However, when it comes to GDP growth, the _transeff_ scenario outpaces the other two due to its higher total productivity growth rate.

**<mark>Table 8. Export and productivity scenarios: Annual macro growth (%)</mark>**

||**Fi**|**nalyear**||||
|---|---|---|---|---|---|
|**Indicator**|**2012**|**base**|**agrpweff**|**indupweff**|**transeff**|
|**Absorption**|99,595|3.9|5.5|6.1|4.6|
|**Consumption - private**|65,095|4.2|5.7|6.3|4.9|
|**Consumption - government**|13,307|1.5|1.5|1.5|1.5|
|**Fixed investment - private**|19,224|4.5|6.8|7.7|5.6|
|**Fixed investment - government**|1,969|4.1|6.1|7.4|4.8|
|**Exports**|35,280|4.4|7.1|10.7|5.7|
|**Imports**|63,874|3.8|6.7|9.1|4.5|
|**GDP at factor cost**|59,315|4.3|5.6|5.7|5.9|
|**Total factor employment (index)**|n.a.|2.4|2.9|3.1|2.6|
|**Total factor productivity (index)**|n.a.|1.9|2.6|2.6|3.3|
|**Real exchange rate (index)**|n.a.|0.2|-2.9|-4.2|-0.2|
|**Headcountpoverty rate(%)**|21.4|9.5|5.7|5.0|8.0|

Note: Columns other than 2012 show results for 2020. Figures in first column are in million lei. Poverty rate figures in simulation columns show final-year rates.

23
Mitchell, J. P., D. C. Reicosky, E. A. Kueneman, J. Fisher, and D. Beck. 2019. "Conservation agriculture systems." _CABI Reviews 2019_ 1-25.

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23
individual pay records. In the case of Uruguay, the BPS data determine individual access to pension, health, and other benefits. Hence, it is in the worker’s interest that all information is accurately reported by the firm. Moreover, the data are thoroughly checked by government officials. In the case of Brazil, wages are also directly extracted from company records and wage information is requested to the level of centavos (cents). On average, 75 percent of our wage observations include the cents paid.

Goette et al. (2007) note that their estimator may encounter identification problems when inflation is very low, since in that case, DNWR and DRWR are very close in the wage change distribution and are therefore hard to distinguish. We are rarely constrained by this issue and are therefore generally able to obtain accurate estimates because, even in the periods with lowest inflation in Brazil and Uruguay, the inflation rate was relatively high with respect to OECD countries previously studied. Also, and in contrast with previous studies, the rate of inflation observed during the period of study in both Uruguay and Brazil is fairly volatile. It is for this reason that we separately estimate our model year by year. This allows for a flexible specification, wherein both, the parameters of the notional and the different rigidity regimes may vary across years. A potential limitation of our analysis is that it does not consider the possibility of symmetric rigidities; for instance, those associated with the menu costs of changing wages. Our assumption is justified by the analysis of Dickens et al. (2008), who examines individual wage change distributions among job stayers in 17 OECD countries and finds no evidence of menu costs in wage setting. Given the inflation history of the two countries we are studying and the high volatility of inflation during the sample period we believe that menu costs are even less likely to be relevant in our case.

# 4 Main Results

## 4.1 Empirical Implementation

In this section, we discuss some relevant details regarding the empirical implementation of the model and provide a first assessment of its functioning. We estimate an individual wage change model and the corresponding rigidity parameters year by year. It is 14
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considers both the “downstream” impact of AIDS on poverty and inequality, as well as the “upstream” impact of the poverty and inequality on the epidemic itself (Piot, Greener, and Russell 2007). Often, these analyses rely on cross-country data, which suffer from the problems seen in bivariate correlations. Moreover, analysis of economic growth and HIV/AIDS tends to examine the impact of HIV/AIDS _on_ economic growth (growth in GDP per capita), using cross-country regressions, neoclassical growth models, or computable general equilibrium models, rather than the impact of poverty or economic status on national estimates of the prevalence of HIV/AIDS (Haacker 2004).

A number of compelling arguments have been made that would support the notion that poverty causes AIDS. A simplistic reason underpinning this view is that health and disease exposure are usually positively correlated with poverty: richer people live longer, are in better health, and are less exposed to the deadliest diseases in low-income countries (diarrheal diseases, malaria, and so forth). This argument, however, is flawed because HIV/AIDS is contracted very differently from other contagious diseases. In fact, it is associated with behaviors and characteristics that are often associated with higher income (lower poverty), such as more concurrent partners, geographic mobility, and urbanization. Glick (2007) characterizes these traits as those that are a direct function of wealth (for example, increased demand for partners) and those that are correlated with wealth (such as residence and population density). Even if there were evidence that the effect of wealth on HIV is not direct but indirect, increases in wealth or income that can be affected by policies cannot easily be disassociated from the “correlation” effect. Greater opportunities for income earning may result in more mobility and urbanization, both of which are associated with HIV.

From a policy perspective, then, the weakly positive SES-HIV/AIDS link seems at odds with “pro-poor” efforts with respect to health policy – that is, targeting diseases and morbidities that afflict the poor. Efforts to direct health spending to ARV treatment can shift the benefit incidence of health spending _away_ from the poor and to the rich. One may speculate that continued perception that HIV/AIDS is a disease of the poor makes HIV/AIDS spending more politically feasible. A more complicated argument would be that even if HIV/AIDS spending is targeting more wealthy populations (or not otherwise pro-poor), prospects for economic growth and poverty reduction in the overall economy 10
19 positive effects from productivity spillovers among suppliers are transmitted to the wider host economy.<sup>10</sup> Another example is a study using data from the 2010 Vietnam Investor Survey by the United Nations Industrial Development Organisation (UNIDO) (Tusha, Jordaan, and Seric 2018). For a large sample of foreign and domestic firms, this data set contains information on output and inputs; the sourcing and selling behavior of the firms; the extent and intensity of inter-firm linkages; and the number, nationality, and location of buyers and suppliers. Tusha, Jordaan, and Seric (2018) find that MNC affiliates are more likely to create business relationships with other MNC affiliates located in Vietnam, thereby lowering the scope for productivity spillovers among local suppliers. At the same time, local firms that do have MNC affiliates as client firms are significantly more likely to receive support, indicating that foreign firms are a source of technology dissemination to their local suppliers. When analyzing productivity effects from supplying to foreign firms, the estimations show that these effects materialize only from having foreign client firms that are located in Vietnam and not from foreign client firms located outside the host economy. This indicates the importance of the physical presence of MNC affiliates in the host economy for meaningful technology dissemination and productivity spillovers.

Three other recent studies present findings that indicate that the impact of MNC affiliates can extend beyond productivity spillovers. Javorcik, Lo Turco, and Maggioni (2018) use firm-level data for Turkey that includes information on the products that the firms produce. Their estimations show that domestic firms that operate in industries that supply inputs to MNC affiliates experience increases in the complexity of their products, as captured by the complexity indicator by Hidalgo and Hausman (2009). This suggests that technology dissemination and support from MNC affiliates enables local suppliers to introduce new and more complex products. Using the product sophistication index by Hausman, Wang, and Rodrik (2007), Eck and Huber (2016) obtain similar evidence for India. For Romania, Bajgar and Javorcik (2019) combine firm-level data on output and inputs with customs data containing information on annual exports at the firm-product-destination level, allowing them to calculate indicators of within-product quality improvements. Their estimations reveal that both forward and backward linkages of MNC affiliates generate significant product quality improvements among domestic firms.

# **4. Backward Linkages and Productivity Spillovers: The Scope for Policy Making**

10 Other studies that report findings of such wider dissemination effects include Kee (2015) and Jordaan (2017).