rafmacalaba/data-use-mentions-extended huggingface dataset:
(2015); Bursztyn et al. (2017, 2019)) and in politics (e.g. Perez-Truglia (2018)). Similarly, Akerlof and Kranton (2000, 2002, 2010) translated theories of social identity into an economics framework to propose that social identity influences economic outcomes when deviating from the prescribed behavior is inherently costly. When individuals derive utility from fitting in with society or with their peers, there may be a social cost of rebelling against prevailing norms. Adolescence, in particular, is a period when the desire to conform to be popular or fit in with peers may be felt acutely.<sup>3</sup> For example, Bursztyn and Jensen (2015) and Bursztyn et al. (2019) show that US students in high school under-invest in education to signal social compatibility to their peers. Similarly, Austen-Smith and Fryer (2005) show that minority US students shy away from educational investments to avoid “acting white” and in this way improve their social standing with peers. When opinions are observable to others, adolescents may try to conform to the group responses due to peer pressure.<sup>4</sup> Our experimental findings have potential implications for understanding different types of normative social influence more generally.

In this paper, we use random variation in individual participation in a program delivered to young adolescents that was designed to challenge restrictive gender norms in Somalia. Early adolescence is viewed as a critical window of opportunity for formulating positive gender attitudes and behaviors as children transition into adulthood (Baird et al., 2019). The gender norms training program called _Choices_ is implemented by the NGO Save the Children and targets adolescents aged 10-14 years.<sup>5</sup> This is the first randomized controlled trial (RCT) experiment to examine the _Choices_ program’s impact.<sup>6</sup> We report results from both individual-level surveys and a lab-in-the-field experiment to estimate the impact of the _Choices_ program on adolescents’ gender
months of fieldwork, only about one‐half of the intended sample of households was interviewed. While the disruptions caused by the conflict have had impact on the data collected, consistency checks suggest that this impact was relatively small. In addition, any introduced sample selection bias due to the conflict is likely to be a downward bias leading to under‐estimation of poverty.

The HFS presents a rare data point in a fragile setting. Only very few similar surveys have managed to collect comprehensive data on welfare and livelihoods in such a complicated and volatile context. Indeed, the HFS documents some staggering levels of deprivation, which are also corroborated by accounts from a multitude of organizations operating in the country. The methodology employed to estimate poverty in the HFS is based on the best available methodologies specifically adapted to the context of fragility. The estimation is also entirely reproducible through the publicly available code and data published in the World Bank MicroData Library.<sup>38</sup> Overall, the HFS provides an extremely detailed picture of welfare and livelihoods for the South Sudanese population between 2015 and 2017. This is especially true when combined with the other three waves conducted between 2015 and 2017, as in the South Sudan Poverty Assessment (World Bank, 2018).

Finally, the satellite imputation, although limited in scope and means, provides an additional glimpse of livelihoods across the country. Although the results are only a proof‐of‐concept, it remains a useful exercise to complement the survey‐based data rather than assuming a national average for inaccessible areas. Much research has already gone into the field of small area estimation, which is likely to benefit enormously from the recent availability of cheaper and more encompassing – geospatial – data sets. Although such models are not likely to replace survey data, as these are needed to train the models, they can be used to supplement data collection and provide information either at more frequent intervals or for hard‐to‐reach areas. One particular area for future research that might be
it gives non-members an incentive to increase trade barriers towards the region. The incentive for members to lower trade barriers echoes the literature on multilateralism (Estevadeordal et al. (2008), Bagwell and Staiger (1999)).

The rest of the paper is organized as follows: the second section presents the data sources and the construction of key variables. The third section analyses the effects of Regional Trade Agreements abstracting from Global Value Chains. Section four extends the analysis to tease out the role played by Global Value Chains in our findings from Section 3. Section 5 discusses the implications of our findings for the debate on regionalism and multilateralism. Section 6 concludes.

# **2 Data**

## **2.1 Data on Trade Flows**

We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014. The data are classified according the 4-digit Standard International Trade Classification (SITC), Revision 2. Only products and commodities are considered. Then, we aggregate these bilateral trade flows at the country-level.

To classify trade flows into final and intermediate goods, we use a concordance table<sup>3</sup> from SITC Rev. 2 to Broad Economic Categories (BEC). We then classify goods into five categories: primary, semi-finished goods, parts and components, capital goods, consumption goods, and a residual category called goods non-specified. We group these categories into intermediate and final goods as follows: intermediate goods are primary goods, semi-finished goods, parts and components; and final goods are consumption goods and capital goods.

## **2.2 Data on Trade Agreements**

The data on trade agreements comes from a World Bank database<sup>4</sup> . We use the “bilateral observations” file where each observation is a country-pair agreement for one year. There are 189 countries. The period covered is 1958-2015, during which 279

> 3The concordance table from SITC Rev2
# Figure 11. Estimated contributions to trade costs

A. Model-based contributions to differences in overall trade costs B. Model-based contributions to differences in manufacturing trade costs <!-- Start of picture text --> Log differences Connectivity and logistics Log differences Connectivity and logistics<br>Trade policy Trade policy<br>0.4 Geographic and cultural factors 0.4 Geographic and cultural factors<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>-0.1 -0.1<br>-0.2 -0.2<br>2018 versus 2008 EMDEs versus advanced 2018 versus 2008 EMDEs versus advanced<br>economies economies<br><!-- End of picture text --> Sources: Comtrade (database); World Bank. Note: EMDEs = emerging market and developing economies.

A.B. Difference in predicted contributions to predicted logarithm of overall trade costs in 2008 and 2018 (A) or in advanced economies and EMDEs (B). Computed using coefficient estimates for each variable and the following realizations for each indicator included in the regression: trade-weighted averages for all countries in the sample in 2018 minus equivalent values for 2008; and trade-weighted averages for EMDEs minus equivalent values for advanced economies in 2018. Trade policy includes tariffs and membership in regional trade agreements; geographic and cultural factors include distance, common border, and common language; border regulation includes connectivity and logistics include liner shipping connectivity index and logistics performance index.
frequency and more extensive range of horizons than survey-based measures.<sup>1</sup> The following section reviews the theoretical and empirical literature on the formation and anchoring of inflation expectations. Despite a lack of consensus on the theoretical framework that best captures the behavior of inflation expectations, the empirical literature has concluded that an inflation targeting regime helps improve the anchoring of expectations in both advanced economies and EMDEs.

The next section examines trends in long-term (five-year-ahead) inflation expectations in advanced economies and EMDEs, from the 1990s to the present. It then assesses the anchoring of inflation expectations. It finds that although expectations have become more firmly anchored during the past decade in both advanced economies and EMDEs, this has been less evident in EMDEs than in advanced economies. The section also reports that inflation expectations in EMDEs are more sensitive to both global and domestic shocks than are inflation expectations in advanced economies, although sensitivity to global shocks has fallen in both groups of economies and sensitivity to domestic shocks has fallen in EMDEs.

The subsequent section identifies the main factors that determine the anchoring of inflation expectations. It presents evidence that inflation expectations are better anchored in both advanced economies and EMDEs when the central bank employs an inflation targeting regime and is highly transparent. For EMDEs, low public debt and a high degree of trade openness are also associated with better anchoring of expectations, while the use of a fixed exchange rate regime is associated with weaker anchoring of expectations. These results suggest that the institutions and framework of monetary policy, the macroeconomic environment (including fiscal policy), and structural characteristics all matter for the anchoring of long-term inflation expectations in EMDEs.

The penultimate section presents case studies on the experience of inflation targeting in Brazil, Chile, and Poland. The conclusions of the case studies are in line with the empirical findings. In Brazil, less than ideal fiscal conditions and worsening central bank transparency during part of the inflation targeting period may have impeded the anchoring of expectations. By contrast, the combination of high central bank credibility
financial services industry. On the other side of the spectrum (not shown in the figure), sectors such as furniture, construction, sale of motor vehicles and wearing apparel are shown to be least data‐ intense. The middle‐range of sectors using software intensively is a mix of modern and traditional sectors such as transport services and various manufacturing industries such as basic metals.<sup>4</sup>

# **3.3 Data Policy Index for Digital Innovation**

<mark>The second term of our data linkage variable is the data policy index, which is based on a quantifiable set of country‐specific regulatory policies which are expected to have a restrictive impact on digital innovation. These restrictive policies relate to the use and transfer of data,</mark> IPR, intermediate liability, content access, as well as regulatory policies regarding the telecommunication marke <mark>t. We draw on Ferracane</mark> _<mark>et al.</mark>_ <mark>(2018a) and ECIPE’s Digital Trade Estimates (DTE) database to develop and construct this index.</mark><sup>5</sup> <mark>The policies used for the analysis are those considered to create a regulatory cost burden for firms relying on data for their innovation activities. The criteria for listing a certain policy measure as a restriction in the database are the following: (i) it creates a more restrictive regime for online versus offline users of data; (ii) it implies a different treatment between domestic and foreign users of data; and (iii) it is applied in a manner considered disproportionately burdensome to achieve a certain policy objective.</mark> The data policy index is composed of 6 different categories, each containing a set of policy restrictions related to a specific digital policy field which are: Intellectual property rights (IPR), cross‐ border data flows (CBDF), domestic use and processing of data (
developed countries<sup>11</sup> and assumed linearly related to daily GDP per capita. It is likely that those estimates are overestimated due to the wealth effect and the fact that WTP usually decreases with the reliability of the water distribution system (Hensher et al., 2005). Second, the countries included in the ES correspond to the middle of the global GDP distribution: the 123 countries cover 49% of the global GDP, preventing an accurate extrapolation of the global total annual cost of water outages for households. Third, the ES reports water outages experienced by firms and not households. Depending on the country’s regulation, households or firms may be served following an order of priority in case of a water supply disruption what would suggest that they do not experience the same outages.

# 3. The impact of transport disruptions

## 3.1. Transport disruption and congestion affect all cities

Road congestion is a growing issue in cities. INRIX, a transport-data company found that a driver spent on average 36 hours in gridlock in 2013 in French, British, German and American metropolitan areas (Cebr, 2014a). The time lost to congestion increases threefold to 111 hours when additional planning time is included.<sup>12</sup> The study used GPS data from some 300 millions of cars and devices to measure when and where traffic slowed to a crawl along 5 million miles of roads, spanning 1,360 cities in 38 countries.<sup>13</sup> Capital cities in developing countries suffer the most from traffic because roads and public-transit systems have not kept pace with population growth. In Thailand, drivers loose an average of 56 hours a year to congestion at peak travel times; Indonesia and Colombia come second and third with 51 and 49 hours. Similarly, the worst place to drive in Europe is Russia, which accounts for five of the continent’s ten most congested cities.

Weather events have an adverse effect on travel time for households by affecting
distribution, workers are divided into 17 different sectors of economic activity (see Table 2 below).

In terms of institutional factors, we distinguish three characteristics of the job. Workers are classified as formal employees if they have a job that is properly registered in their work-card or “ _carteira de trabalho_ ,” which provides workers with various benefits including rights to pensions, unemployment insurance and severance payments. Those employees whose employers have not registered their job in the “carteira de trabalho” are considered informal employees, and we also include in the analysis own-account or selfemployed workers (“ _conta própria_ ”) as a separate category. Self-employment in developing countries is often considered as an indicator of informality, although it also encompasses workers engaged in liberal professions and some entrepreneurs with no employees of their

> 8 Except for the rural areas of Acre, Amapá, Amazonas, Pará, Rondonia and Roraima states, which correspond to the Amazon rain forest. These areas, which according to census data account for 2.3 per cent of the Brazilian population, were excluded from the survey before 2003. To preserve sample comparability, we exclude these areas from our sample in all years.

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and unevenly distributed, causing resentment among less favored civil servants. To the extent that they moderate the effect of salary erosion, however, allowances may, in the short term, be a necessary evil. In any case, allowances are a big, permanent part of the pay system in many countries and it is misleading not to take them into account. Although some research has been carried out in the Bank exploring various facets of non-wage benefit systems, Bank operational work has generally paid only minimal attention to this issue.

The degree to which any of the above mechanisms can attract and keep higher level personnel in government depends largely on the available alternatives. In Guyana where professionals may make one-fourth the salaries of their counterparts in neighboring countries, the prospects for attracting qualified high-level personnel are dismal, especially given the weak state of the economy. In other countries, however, non-material incentives such as prestige, patriotism, or access to social and political connections may motivate well-qualified individuals to take low-paying jobs in government, assuming minimally acceptable conditions for subsistence.

# D. OPERATIONAL TECHNIQUES

This section delineates the main techniques used in Bank operations to carry out the substantive approaches discussed above. (As a reminder, this discussion includes initiatives that may or may not have been directly in a Bank program. Some activities discussed were the result of government action (some with IMF support) and thus may have precedod and perhaps laid the foundation for the Bank's operational intervention.)

# PreXeauisites to Reform: Data Collection. CoMnuterization. Civil Service Census, Functional Review

Some Bank operational activities are prerequisites to further reforms. These are usually related to the need to collect and access necessary information to get control of the personnel system. They include data collection and information management system improvements, civil service censuses, functional reviews of whole governments or selected ministries, diagnostic studies of overall employment problems, and other specific aspects of employment and pay policy.

# Civil Service Censuses

Civil service censuses are often viewed as a preliminary step to generating
because of data processing errors). We also highlighted that there is a lack of publicly available survey data with income questions for some LMICs, particularly in Africa.

We then discussed solutions to the challenges encountered when using household surveys that use survey data and no external data. The first set of solutions are those that can be undertaken before a survey starts. These include oversampling high income or wealth individuals, better fieldwork protocols to minimize unit and item non-response, and questions to elicit at least some information when a respondent does not want to give their exact income or wealth.

The second set of solutions are those that use only household survey data and are used ex-post. These can be broadly classified as either reweighting or replacement methods and include reweighting for unit non-response, imputation for item non-response and replacing the top of the distribution using a parametric distribution estimated from the survey itself, usually the Pareto distribution.

The third set of solutions combines survey data with administrative data, usually tax data, to improve measurement at the top, where researchers believe incomes are not well captured. These can also be broadly classified as replacement or reweighting methods. They include replacing top incomes in surveys with incomes from tax data above a certain threshold or reweighting so that the survey income or wealth distribution matches that in administrative data. Most solutions using administrative data use a combination of reweighting and replacement.

The research we have surveyed has resulted in a much better understanding of the challenges when measuring the upper tail of the income and wealth distributions and improved estimates of the incomes or wealth in the upper tail. But we have outlined gaps and thus areas in which future research should concentrate.

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same direction as before, but are somewhat attenuated because of the inclusion of other regressors associated with firm size. The results on firm age, industry, and region are little changed when compared to the results of Model 1 on the EcC data. With regard to the new variables added, we see that collective ownership as opposed to sole proprietorship slightly and significantly raises the odds of formality. Higher labor productivity is associated with substantially higher odds of formality, as is higher capital intensity. As expected, having unpaid family workers reduces the odds of formality, but having wage workers increases it. The proportion of female workers in the firm’s workforce is not associated with the odds of formality.

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Eichengreen and Mauro 2000). What this means in the context of East Asia is that the OCA criteria by themselves do not provide the overriding basis for determining the type of exchange rate arrangement to be chosen, although they may be useful for giving a quantitative sense of the reasonableness of one type of exchange rate regime or another in a particular situation.

# **2. Integration through Trade and FDI**

In terms of trade shares, East Asia is almost equally connected with Japan, the United States and the European Union (Table 3). During 1990-98, for exports, the United States was by far the most important market for principal Asian exporters, such as Malaysia, the Philippines, Singapore, Thailand, Hong Kong, Korea and Taiwan, although Japan was more important for Vietnam, China and the resource exporting countries of Brunei and Indonesia. For imports, Japan was the most important source country, except in Brunei (for which the European Union was the most important). For total trade (exports plus imports), Japan was the largest trading partner for Indonesia (26 percent), Thailand (23 percent), Malaysia (19 percent) and China (18 " It should be noted that, theoretically, closer trade ties could result in either tighter or looser correlation of national business cycles. Cycles could become more idiosyncratic, if countries become more specialized. On the other hand, if demand shocks are dominant, or there are more common (supply and/or demand) shocks, or intra-industry trade accounts for most trade, cycles may become more similar. Concentrating their attention on the relationship between trade links and business cycle correlation, Frankel and Rose (1998) analyzed the panel data of 21 industrial countries over 30 years to obtain a strong positive relationship, i.e., closer international trade links result in more closely correlated business cycles across countries. This result is robust with respect to the choice of a particular measure of bilateral trade intensity (e.g., imports
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# **Figure A4.5: India - Gross Government Debt as Share of GDP**

<!-- Start of picture text --> 55<br>50<br>45<br>40<br>35<br>30<br>25<br>20 States Centre<br>15<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>% of GDP<br><!-- End of picture text --> Note: The amount of onlending from centre to states is netted out from the data of centre. Source: Reserve Bank of India
a.|n.a.|0.675|0.806|n.a.|
|(log)|||(5.156)|(3.556)||
|Female wage relative to|n.a.|n.a.|n.a.|n.a.|-0.836|
|malewage(log)<br>|||||(-3.301)|
|R<sup>2</sup>|0.336|0.467|0.811|0.692|0.687|
|SEE|0.164|0.145|0.093|0.111|0.108|
|N|20|18|18|19|19|

_Notes_ : The dependent variable is the share of total person days of employment on MGNREGS going to women. The rationing rate is the share of those who wanted work who did not get it. The headcount index is the percent of population below the poverty line. The t-ratios in parentheses are based on White standard errors.

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