rafmacalaba/data-use-mentions-extended huggingface dataset:
2019 that it will close its “Speedfactories” in Germany and the United States may, on the one hand, assuage some of the fears the earlier headlines raised. On the other hand, the fact that these automated production lines will instead be moved to China and Vietnam where 90% of Adidas’ suppliers are currently located may yet ground the ‘flying geese’. This is not an isolated example. Foxconn—the Taiwanese firm known for producing Apple and Samsung products in China’s Jiangsu province—recently replaced 60,000 factory workers with industrial robots ( _South China Morning Post_ 2016). In fact, China has installed more industrial robots than any other country and is rapidly automating to address declining wage competitiveness. This is important given that China’s rising wages and production upgrading had created expectations of an en masse migration of light manufacturing activities to poorer economies with lower labor costs, such as those in Sub-Saharan Africa.

More systematic evidence on automation and offshoring is few and far between. Based on firmlevel data for 3,313 manufacturing companies across seven European countries, Kinkel, Jager and Zanker (2015) find that firms using industrial robots in their manufacturing processes are less likely to offshore production activities outside Europe. Similarly, Artuc, Christiaensen, and Winkler (2019) show that an increase of one robot per 1,000 workers in the United States—about twice the increase observed between 2004–2014—lowers growth in exports per worker from Mexico to the United States by 6.7 percent. Artuc, Bastos and Rijkers (2019) show that a 10percentage point increase in robot density in developed countries is associated with a 6.1 percentage point increase in their imports from less developed countries and a 11.8 percentage point increase in their exports to these countries, such that net imports from the South within the 2
Some other factors have similar effects in both country groupings. A stronger effect of the recent financial crisis is associated with lower future mobility expectations. The belief that need in society is the product of injustice (the reference category being “inevitable part of modern life”) also is associated with lower expectations vis-a-vis future upward mobility.

The estimates on other variables (not reported to conserve space) suggest that in both groups of countries expectations of future upward mobility diminish with age, are higher in urban areas and for those who are not married. In the EU they are also higher at higher levels of education, whereas outside the EU they are higher for those currently employed (whereas there are no differences across education levels). Men and women have similar aspirations of upward mobility once other characteristics and beliefs are held constant.

The fact that connections do not help when they are both deemed to be important and available in the EU is an interesting finding that deserves further investigation. One possible reason for this could be due to the fact that it may be less common - and possibly less socially acceptable - to rely on informal connections in the European Union. One would certainly expect reliance on informal institutions to be more prevalent when formal institutions are weaker. It is possible to test this conjecture with LiTS data. The respondents who claimed to have connections were asked how likely they would be to rely on these connections. In the EU sample 18 percent of respondents reported that it was “not at all likely” that they would resort to personal connections if they had them (29 percent in Western European countries), whereas in the non-EU Transition Economies only 10 percent of respondents reported that they would not resort to connections if they had them (8 percent in CIS countries). The distribution of responses for each country is provided in Figure 4. To investigate this further, I split the group who think that connections are vital and report having – them) into two categories those who say that they will very likely or definitely
11 this single-year increase in 1986, and of the corresponding 40% proportional poverty decline reported in Table 2, are _not_ credible. Since there were no methodological changes to the survey, the explanation for this discrepancy – which is unique in the Brazilian time series, as an inspection of Tables 1 and 2 will attest – is unlikely to be found in statistical problems like those which have recently featured in the literature on poverty in India. More likely, it reflects the disconnect between monetary incomes and welfare that resulted from the widespread rationing that became prevalent throughout the Brazilian economy in late 1986. Rationing arose in most consumer-goods sectors as continued monetary growth made the price freezes, on which the failed Cruzado stabilization plan hinged, unsustainable. Under widespread rationing, of course, real monetary incomes (calculated with respect to the prevailing frozen prices) are no longer a reliable guide to welfare, or to poverty, since goods are not necessarily available to meet demand.

As the price freeze became unsustainable, and black markets proliferated, the Cruzado Plan was abandoned, and an upsurge in inflation in 1987 restored equilibrium prices. The results can be seen in the “return to normalcy” of median, mean and poverty indicators for 1987, in Tables 1 and 2. Crucially, during September 1986 – the reference month for the PNAD survey – the price freeze (which was decreed on February 28<sup>th</sup> , 1986, and effectively abandoned with the “Cruzado II” announcement of November 21<sup>st</sup> of the same year) was still firmly in place, but rationing and black markets were already commonplace.<sup>18</sup> In sum, while we are confident that the time-series for poverty and inequality presented in Tables 1 and 2 are reasonably accurate for all other years, they clearly overstate mean incomes and understate poverty for 1986. Since there is no obvious reason why rationing should be distributionally neutral, the inequality numbers for 1986 must also be treated with
6.95|2.55|2.00|8.59|6.95|6.95|
|N|9,677|9,908|9,913|7,826|8,020|9,539|9,913|9,913|
||||N. No R|egions of H|ighest Famin|e Concentration|||
|�<br>Years of Schoolingiz|0.114|0.030|-0.010|0.203|-0.095|-0.203|-0.041|-0.054|
|y|(0.021)|(0.020)|(0.024)|(0.174)|(0.154)|(0.111)|(0.036)|(0.032)|
||[0.000]|[0.128]|[0.694]|[0.244]|[0.534]|[0.069]|[0.262]|[0.092]|
|First Stage F-Statistic|9.14|9.19|9.26|3.73|3.51|6.53|9.26|9.26|
|N|9,951|10,155|10,161|7,871|8,064|9,826|10,161|10,161|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : BMI = body mass index. The dependent variable is described at the top of each of the eight columns. In columns 1–3, 7, and 8 it is an indicator that equals 1 if true; in columns 4 and 5 it is a standardized value of the described outcome;
aged 15 or older have at least one parent who went to school.*4 Parental education has a positive and significant impact on welfare.

82 This contrasts to evidence from developed countries of a U-shaped relationship between happiness and age (Blanchflower and Oswald (2000)).

33 Using British household data from 1991 to 1997, Clark and Oswald (2002) find that widowhood brings a degree of unhappiness that would take on average an extra 170,000 pounds per annum to offset. 34 Educational attainments of the parent generation is abysmally low, a legacy of the Portuguese colonial period up to 1975. The percentage of individuals without primary education rises from 61 percent for those aged 33 to 42 years, to 81 percent for those aged 43 to 52 years, and to 92 percent for those 53 years or older.
specifications that include an interaction term of whether an individual was also recently exposed to a prior violent event. These specifications, in turn, allow us to estimate the marginal effect of exposure to a violent event conditional on other, recent exposure.

We find that labor allocation decisions of individuals respond to exposure to violence, and these effects seem to operate through a “shadow of violence” mechanism. Our main results show that both men and women exposed to herder-related violence (i.e., an event occurring within 10 km. within the previous month), and that have also been previously exposed to this violence (i.e., a previous event occurring within the previous six months), shift toward informal work in the post-planting season at both the extensive and intensive margins and shift away from informal work in the post-harvest season at the extensive margin. This shift toward informal work in the post-planting season seems to act as a substitute activity for agricultural work for men in households exposed to herder-related violence and that have also been previously exposed to this violence. Agricultural work does not decline for women; instead, the total number of hours worked increases if women live within households exposed to herder-related violence and that have also been previously exposed. For these women, the increase in total hours worked is predominantly allocated to self-employment or household-enterprise work.

To further explore the “shadow of violence” mechanism, we investigate heterogeneity across both time and space by exploiting the spike in violence just before the 2018-2019 GHS survey rounds and within Nigerian states implementing open-grazing bans, which have been attributed as a contributor to increased violence in the conflict. We find that the shift toward informal work and away from agricultural work is larger within ban states and during the 2018-2019 survey round, especially during the post-harvest season. Finally, to investigate possible welfare implications of the measured labor responses after exposure to herder-related violence, we also estimate additional “knock-on” or lagged effects that materialize during the post-harvest period of the 2018-2019 GHS survey and are associated with exposure
d|0.014|24.3 /2|44.4/4|47.1|56.7|7,073|6,086||

Notes: All data are for 2003, unless otherwise specified; 1/ Excluding Slovak Republic; 2/ Excluding Luxembourg; /3 2001 data; 4/ Excludes France (no secondary ed. data available); 2002 data used for the Netherlands. /a Upper secondary and post-secondary non-tertiary education - levels 3-4 (ISCED 1997); /b ILO Methodology: Economically active population divided by total population over 15. /c Falcetti et al. EBRD 2004. Countries are rated from 0 to 2, 2 being the highest. /d Simple average. Sources: EUROSTAT, WDI 2004.

Furthermore, the cost of labor as measured in terms of the average gross monthly wage is very low by regional standards. In fact, it was well below the levels not only in CEEC-8, i.e., new EU members, but also in SEE countries. Albania’s average gross monthly, the second lowest, was 11 percent higher in 2003 and 23 percent higher in 2004. Romania’s average wage was 30 percent above that of Bulgaria’s in 2004. Had there been no other constraints, this alone would have made Bulgaria extremely attractive for labor intensive activity.

In all, like other CEEC-10, Bulgaria has the potential to specialize in labor intensive products, with the possible bias in favor of skilled labor intensive goods, and, to a lesser extent, natural resource intensive products embracing mostly agricultural goods. On might also add to this list the capital-intensive goods, albeit with a caveat. The experience of transition countries shows significant contribution of FDI taking advantage of relatively cheap and highly skilled labor thus reducing relative shortage of capital.

# **B** **_._** _Factors content of Bulgaria’s exports in comparative perspective_

Patterns in trade in goods reflect differences in comparative advantage as determined
institution’s loss of control of the electricity tariff-setting process in Argentina in 2001. This regulation was kept outside the control of the regulator to adjust for short-term social concerns rather than efficiency. Similarly, the use of energy tariff-setting processes by the Aznar administration in the 1990s to control inflation kept the regulator at a distance from of one of its main responsibilities.

> 54 Guasch (2004) shows a strong correlation between contract renegotiation and lack of a regulatory body in Latin America. However, a large share of the contract renegotiations occurred in countries where an independent regulator was in place. For instance, in Argentina, new contracts have been directly negotiated between the executive and private companies— even if this was the legal role of the regulator.

> 55 There has been great pressure for more transparency and publicity of political actions over the last decade, expressed in Internet web sites that publish laws and decrees, facts about government procurement and comparison of prices, more comprehensive government statistics, and hence, more information about governance performance.

19
season) and six hours a day (summer) and would be prepared to pay 0.018 to 0.031 US$/kWh for uninterrupted power supply, or 20% more than the actual prices (Ghosh _et al._ , 2017).

In Ethiopia, manufacturing firms of comparable size are willing to pay US$ 0.0046 per kWh to reduce the monthly number of outages by one, which corresponds to 16% of the current kWh electricity price. In order to decrease average outage duration by one hour, firms would be prepared to pay US$ 0.0092 per kWh. From these numbers, the authors infer that firms’ compensating variation for a scenario without power outages would be about three times the current cost of electricity (Carlsson, Demeke and Martinsson, 2018). In Ghana, firms affected by significant power rationing between 2012 and 2015 were estimated to be willing to pay a 16% higher monthly electricity bill to avert future power outages (Abeberese, Ackah and Asuming, 2017). These results further illustrate the substantive differences in power supply quality across countries and the heterogeneity in firms’ vulnerability to power outages.

# **Higher‐quality water infrastructure matters for firms’ production and productivity**

While the better availability of data has caused studies to focus on the effects of unreliable electricity supply, water supply infrastructure also plays an important role for production. In agriculture, the relationship between water availability – determined by weather and irrigation technology – and agricultural production has been clearly established (Damania _et al._ , 2017).

Looking beyond the agricultural sector, Iimi (2011) finds that if all water supply disruptions could be removed in Europe and Central Asia, firms would on average be able to reduce their costs by 0.5%. This effect can be expected to be significantly larger in developing countries with less reliable water infrastructure.

Indeed, Islam and Hyland (2018) use Enterprise Survey data for 103 countries and analyze the effects of water supply disruptions on
food security measure from a one percent change in price.) We use the average price of domestic wheat flour since wheat is the primary staple of

> 29 Some studies use household size to calculate per capita amounts. In Afghanistan the custom of sharing meals with guests makes it important to account for guests eating meals from the household cooking pot. The effective household size also incorporates information on household members eating outside the home. 30 Spices, and ‘other’ foods do not contribute to total calories. USDA sources were used for a few items that were not available in the FAO tables.

> 31 See Weismann _et al._ (2009) for an evaluation of the effectiveness of the food consumption score in measuring household food security. See WFP (2008) for a general discussion of FCS analysis, as well as recent evidence from several developing countries.

> 32 Sanogo (2009)

> 33 Weights for food groups range from 0.5 to 4 based on nutrient density. Condiments receive zero nutritional weight. Frequencies are truncated at 7 for each food group.

12
# **3.2 Data**

The empirical approach uses the data from the sixth wave of the World Value Survey (WVS) on four African countries: Algeria, Ghana, Morocco, and Nigeria.<sup>10</sup> In the survey, respondents are interviewed on their attitudes concerning economic and social contemporary issues, family, religion, and work. The WVS is widely used in political economics, sociology and economy in many papers, including Knack and Keefer (1997), Zak and Knack (2001), and Nunn and Wantchecon (2011). Moreover, using the experimental data of the _Reader’s Digest_ ,<sup>11</sup> Knack and Keefer (1997) provide empirical proof of the validity of these data. Indeed, they show the existence of a strong correlation between the measure of trust in the WVS and the number of wallets lost and returned with their content intact in many countries.

Figure 1 depicts the distribution of tax morale by country. As the figure shows, the majority of respondents declare high levels of tax morale, while a minority state lower tax morale. In Ghana and Morocco, the share of respondents stating that cheating on taxes is never justified is close to 80 percent, while the same proportion is about 60 and 40 percent in Algeria and Nigeria respectively. This highlights heterogeneity in attitudes toward taxes both between and within countries. Figure 2 shows the relationship between tax morale and trust in the data graphically. It plots the average trust in public institutions and the neighborhood against tax morale. As expected, both trust in public institutions and the neighborhood are positively correlated with tax morale, with a more pronounced relationship for trust in public institutions.

In Table 2, I present some individual-level descriptive statistics. The standard errors are presented in parenthesis. The first columns show the summary statistics for each country while the last column gives the statistics for the full sample. Respondents in Algeria report a lower level of tax morale (7.12
p <_ 0 _._ 01 _,_<sup>_∗∗∗_</sup> _p <_ 0 _._ 001

control mean of approximately 3,544 TSh, an increase in about 15 percent. Those receiving the reciprocity message gave substantially more: nearly 700 TSh. This is significantly greater than reminder messages, although not significantly greater than social pressure messages at conventional levels of inference.

In Table 5 we use our subsample of taxpayers for whom we have bill data to unpack what proportion of their final tax bill has been paid. Column (1) replicates column (6) of Table 3, then column (2) restricts the sample to the bill subsample, showing that the effect sizes are similar across the two samples. Columns (3) investigates the impact of the three treatments on the proportion of the total tax bill (the 2017/18 bill and any arrears) paid by the taxpayer. During the time period we consider the control group has paid roughly 10 percent of their total bill on average, and we find that treated taxpayers pay an additional 1.6-1.9 percent of their bill on average. Only 8.4 percent of control group taxpayers paid off their entire bill. Those receiving a reminder or a reciprocity message were 1.6-2 percentage points more likely to fully pay. Again, reciprocity performed better than the other message types, although not significantly so.

To check whether the timing of the messages across groups affected the impacts, we include analysis of our main results – whether citizens paid any part of their bill and the amount that they paid – for the subgroup that received messages on June 26 or later (Appendix Figure A1). Our pattern of results is consistent in this smaller sample. The impact on payment rates is higher for those who received any treatment, and the point estimate is highest for the 10
26|0.26|0.25|
|Median/Mean dependent|7.22|7.84|7.84|62.50|57.69|57.69|107.33|107.43|107.43|
|Sample|All|Has skills|Has skills|All|Has skills|Has skills|All|Has skills|Has skills|
|||measures|measures||measures|measures||measures|measures|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

* p<.10  ** p<.05  *** p<.01

_Notes_ . This table reports estimates of the relationships between schooling, skills, and earnings for women in Pakistan. The dependent variable "Monthly earnings" in columns (4) to (6) is the raw monthly earnings while the dependent variable "Monthly earnings (TC)" in columns (1) to (3) and (7) to (8) is top coded at 100,000 PKR per month (961.5 USD). The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the paper test and the computer adaptive test on tablet (excluding items that less than 50 respondents answered and those that less than 5% or more than 95% of respondents got it right). The socioemotional skills index is computed using principal factor analysis on the Big-Five items, Grit items, BART, and GoNoGo scores. The sample for column (1) is all women in the sample who are not currently enrolled, that is 2,104 women. For columns (2) and (3), the sample
some suggestions for progress based on observations made by the research team. Section 4 examines existing indicators measuring data accessibility and discusses results from section 3 <mark>visà-vis</mark> the Open Data Access Indicator (ODIN). Section 5 presents the new microdata access indicator for MENA while section 6 concludes.

# 2. <mark>Transparency and trust in statistics</mark>

<mark>Public trust in official statistics is anchored in professional independence and impartiality of statisticians, their use of scientific methods and equal access for all to official statistical information. T</mark> o operationalize these ideas, the international statistics community has adopted a professional code comprising of ten principles, the _Fundamental Principle of Official Statistics,_ and a set of “ _Good Practices_ ”. Together they emphasize accessibility, impartiality, transparency, accuracy, relevance, cost-effectiveness, confidentiality, professionalism, coordination, and cooperation. At times, the Principles and Practices have conflicting requirements. Confidentiality, for instance, captured in Principle no 6 necessitates measures to <mark>prevent the direct or indirect disclosure of data on persons, households, businesses, and other individual respondents. As this could be interpreted as a prohibition to release source data, statisticians also commit themselves to “</mark> _<mark>a</mark> framework describing methods and procedures to provide sets of anonymous micro-data for further analysis by bona fide researchers, maintaining the requirements of confidentiality_ .”<sup>4</sup> In this way the Good Practices forge a compromise between confidentiality on the one hand, and transparency and access on the other.

Access to microdata is typically offered in two ways. Some agencies make anonymized microdata directly available to the public. India’s statistical agency for instance, the <mark>Ministry of Statistics and Programme Implementation</mark> MOSPI, has a long history of running national sample surveys dating back to the 1950s when they were initiated by Professor <mark>Mahalanobis, the father of Indian statistics