rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>60<br>5 50<br>0 40<br>30<br>-5<br>20<br>-10 10<br>-15 0<br>03/98 03/00 03/02 03/04 03/06 03/08 03/10 CZ IT NL AT FR ES DE<br>Credit-to-GDP gap (2009) (1986) (1984) (1984) (1985) (1990) (1983)<br>Credit-to-GDP gap (actual period estimation)<br>Credit-to-financial-assets gap<br>Source: IMF IFS, authors' calculations<br>Credit-to-assets gap<br><!-- End of picture text --> Source: CNB, authors' calculations As regards simulating possible macroprudential policy in the past, it makes more sense to apply the HP filter recursively: in each past period using only the data that were available in that period. (At the end of 2005, for example, the trend value and therefore also the gap between the observed credit level and the trend is calculated on 1998–2005 data.) This simulates the situation that the macroprudential policy maker would hypothetically have found itself in had it been required to decide whether excessive credit growth was emerging. The calculated credit gaps expressed as a percentage of GDP indicate that the Czech Republic would have found itself in a situation of excessive credit as early as 2004 (Chart 18). However, the aforementioned drawbacks of the HP filter play an even greater role in the calculated gap, as the problem period of 1998–2002 influences the trend.

15
Sources: Notre Dame Global Adaptation Initiative; Penn World Tables; Portulans Institute; UN Population Prospects; World Economic Forum; World Bank; World Development Indicators database. Note: Estimates based on production function approach.

A. Portulans Institute Network Readiness Index estimates preparedness to benefit from emerging technologies and capitalize on the opportunities presented by the digital transformation; higher value indicates better readiness. Unweighted group averages.

B. The Notre Dame Global Adaptation Initiative index reflects vulnerability to climate change and other global challenges in combination with the readiness to improve resilience. A higher value indicates lower vulnerability and/or better readiness. Sample includes 146 EMDEs; last observation 2019.

D.-F. Sample includes 53 EMDEs (14 from SSA). Period average of annual GDP-weighted averages.

# References

Abdychev, A., C. Alonso, E. Alper, D. Desruelle, S. Kothari, Y. Liu, M. Perinet, S. Rehman, A. Schimmelpfennig, and P. Sharma. 2018. The Future of Work in SubSaharan Africa. Washington, DC: International Monetary Fund.

Abreha, K., E. Lartey, T. Mengistae, S. Owusu, and A. Zeufack. 2020. “Africa in Manufacturing Global Value Chains. Cross-Country Patterns in the Dynamics of Linkages.” Policy Research Working Paper 9439, World Bank, Washington, DC.

Acemoglu, D., and P. Restrepo. 2017. “Low-Skill and High-Skill Automation.” MIT Department of Economics Working Paper 17-12, Massachusetts Institute of Technology, Cambridge, MA.

Acerbi, M., M. Heger, H. Naber, and L. Sieghart. 2021. “Middle East and North Africa: Two Opportunities for Rebuilding after COVID-19 in Green and Inclusive Ways.” Arab Voices (blog). November 22, 2021. https://blogs.worldbank.org/arabvoices/middleeast-north-africa-two-opportunities-rebuilding-after-covid-19-green-inclusive.

Acharya, D., and Y. N. Raju. 2020. “Revisiting the Volatility-Growth Relationship: Some Cross-country
include tariffs on final goods. This variable captures foreign competition pressures. Finally, we also include a Herfindahl index at the sectoral level to control for domestic competition. Note that in order to keep the theoretical framework simple and rationalize the effects of input-tariff on foreign technology adoption, the model abstracts from these competition channels. They should be, however, included in the empirical estimation to avoid omitted variable concerns.

Next, we also explicitly take into account changes in observable firm characteristics that could affect firms’ import patterns. Using the same dataset, Bas and Berthou (2012) have found evidence on a positive correlation between firms’ decision to import capital goods and firms’ capital intensity. We therefore expect that non-importing Indian firms which experienced significant growth in their capital intensity during the period under analysis were more likely to import capital goods. _Xi,t-1_ is a set of firm-level controls such as firms’ capital intensity and the age of the firm. The Prowess dataset contains the year of creation of the firm that allows computing the age of the firm.<sup>29</sup> Table 3 shows the estimation results for equation (I) using a within-firm estimator. These results show the impact of lower input tariffs on the decision to import capital goods. In column (1) the coefficient on the input tariffs is negative and significant at the 1% confidence level, indicating that the drop in input tariffs between 1989 and 1997 increased the probability of importing capital goods. The estimated input tariff coefficient is robust to the inclusion of MFN tariffs for final goods set India (column 2). We also introduce tariffs on capital goods to be sure that the input tariffs are not just capturing the effect of changes in direct tariffs of imported capital equipment products (column 2). Not surprisingly, reductions in tariffs on capital goods enhance the probability of upgrading foreign technology embodied in imported capital goods. More interesting, the inclusion of capital goods tariffs does not pick up the indirect effect of reductions of tariffs on intermediate
Second, we contribute to the literature on the role of trade policy when such a shock takes place, particularly non-tariff measures. Until now, only a few studies have focused on Covid-19 and trade policy, mainly looking at the trade policy responses to the pandemic (Baldwin and Evenett, 2020). We contribute to this literature by focusing on pre-existing trade policy measures and how they amplify the effect of the pandemic on GVC firms. Our use of a novel data set for NTMs, an updated version of that used in Cal`ı et al. (2022), makes such an exercise possible.

The rest of the paper is organized as follows: Section 2 describes the data used, Section 3 analyzes the trends prior to and during the Covid-19 crisis, Section 4 outlines the empirical strategy and results from the regression analysis and Section 5 concludes.

# **2 Data**

The trade data is obtained from Panjiva, which is a private data provider collecting daily highfrequency customs data. We obtain the daily firm-to-firm import and export data for Indonesia from February 2019 to June 2021. The data is checked and cleaned rigorously with respect to firm identities, data completeness, quantity units, product codes, and partner countries, as detailed in Appendix subsection A1. Further, we validated the data quality by comparing the annual aggregates with the World Integrated Trade Solution (WITS) and found that Panjiva’s exports and imports data in 2019 captured about 94% and 79% of WITS, respectively. A summary of the firm-level data is given in Table A1.

4
amount of violence experienced in the regions they fled reported in Figure 6, even with the post-displacement escalation, was very small on average compared to other displaced households. The average number of monthly fatalities after displacement for households from no-violence districts was less than one-third that of other displaced households in Figure 6; and the number of monthly fatalities was approximately one-half that experienced in the months after the latest survey of non-displaced households. Consistent with having a much stronger aversion to a poor security situation, the households that fled regions before violence occurred were also fleeing from what turned out to be much smaller post-displacement violence on average than other displaced households.<sup>20</sup>

# **Section 6. Conclusion**

Using unique displacement data from the ongoing conflict in the Republic of Yemen, we find that violence was strongly associated with displacement. But we also find that there was significant heterogeneity in how different types of violence forced displacement and in the amount of violence experienced before becoming displaced. Importantly, the heterogeneity in the ways different types of violence force displacement is not emphasized in many of the previous studies analyzing the relationship between violence and forced displacement (e.g., Stanley 1987; Engel and Ibanez 2007; Williams 2008; Lozano-Garcia et al. 2010; Bohra-Mishra and Massey 2011; Adhikari 2013; etc.), and the results suggest a potentially sharper difference among the population in in the tolerance of violence than is typically assumed (e.g., World Bank 2017a).

However, there are several caveats to this analysis. First, the present analysis only addresses households with access to mobile phones, leaving a portion of displaced and non-displaced households excluded from the analysis. Although mobile phone penetration is high in the country (even among displaced populations), other survey modalities need to be used to reach the population without access to mobile phones.

> 20As mentioned above, it is also possible that some households are able to better predict future violence than others. However, it is unlikely that
|0.035|
|2004<br>0.058|0.008|0.039|
|2006<br>0.019|0.010|0.031|
|2008<br>0.017|0.014|0.051|
|2010<br>0.018|.|.|
|2012<br>0.014|.|.|
|**B2. Distribution over firm size (Asset quantiles)**|||
|0-25%<br>0.136|0.008|0.041|
|26%-50%<br>0.250|0.011|0.042|
|51%-75%<br>0.346|0.011|0.038|
|76%-100%<br>0.614|0.011|0.032|
|**B3. Distribution over firm ownerships: Total R&D expen**|**diture/sales**||
|Year<br>Private firm|SOE|Foreign firms|
|2001|0.001|0.001|
|2002<br>0.067|.|.|
|2004<br>0.075|.|.|
|2006<br>0.021|0.003|0.001|
|2008<br>0.020|0.002|0.001|
|2010<br>0.018|0.002|0.001|
|2012<br>0.016|0.002|0.002|

_Data sources for Panel A: National Surveys of Privately-Owned Enterprises in China; CSMAR database on Chinese listed firms. The numbers is based on simple average of the sample. Firm asset and capital intensity are adjusted for inflation and expressed in 1990 RMB. Data sources for panel B: National Surveys of Privately Owned
lead to the underestimation of the positive effects of input tariff reductions for non-exporters, and overestimation of the effect for exporters.<sup>4</sup> We rely on firm-level data on 410 firms listed in the stock exchange (publicly listed firms) in nonbanking sectors in Pakistan over the period 2012-2017. Publicly listed firms are spread across 11 sectors, with a larger presence in the textile sector, and are among the largest in the economy, accounting for 13 percent of Pakistan’s GDP in 2017. We complement this data set with information on foreign ownership status at the firm level, and tariffs and FDI at sector level. We adopt a standard approach in the literature to estimate productivity and measure changes in input tariffs and upstream FDI presence. Yet, while the literature has investigated the role of upstream tariffs and FDI separately, this paper accounts for both forms of integration upstream and, at the same time, controls for output tariffs and FDI in a firm’s own sector. This allows us to isolate the effect of input tariffs from other trade liberalization reforms, and to provide a comprehensive analysis of the effect of reducing frictions in upstream markets on the performance of firms in downstream markets.

We present three main findings. First, firm-level productivity has been stagnant – indeed, firms’ average productivity has not grown since 2015. This is in line with what macro data suggest and aggregate gains have happened through a better allocation of resources. Second, we find that internationally linked firms – that is, those that are foreign-owned or exporters - perform better than domestic-owned or domestic-oriented firms. For foreign-owned firms we find suggestive evidence pointing to the productivity premium being explained both by a self-selection element – foreign investors cherry picking more productive firms, but also by foreign-owned firms increasing productivity after acquisition. For exporting firms, we find suggestive evidence pointing to the productivity premium being mostly explained by increases in productivity after starting exporting. Third, protection upstream, in the form of import duties on intermediate inputs or restrictions de jure
>-country risk indicator : based on<br>a weighted average of 9 indices,<br>of which political risk and<br>economic performance each<br>account for 25% of the weighting<br>(CL)<br>_Result: significant (at 10% level_<br>_of significance)_<br>-GDP per capita (US$ per capita)<br>(CL)<br>_Result: insignificant_<br>-aid/GDP ratio<br>(CL)<br>_Result: insignificant_<br>-commercial energy use per<br>capita (MWh per capita)<br>(SE)<br>-annual growth rate of<br>commercial energy use per capita<br>(SE)|-regional dummies for<br>Latin America, Africa,<br>Asia, etc.<br>(CL)<br>_Result: very significant_|Cross-section data on the 115<br>developing countries<br>surveyed in ESMAP (1999).<br>The dependent variable is<br>from the study. The source of<br>other data (policy, risk, and<br>energy use data) is not<br>specified.|
|**Drillisch and**<br>**Riechmann**<br>**(1998)**|_H0: There is a correlation_<br>_between energy dependency_<br>_and choice of liberalisation_<br>_model._|-liberalisation index,<br>(RM)<br>composed by the following<br>elements:|-prices (US$),<br>(PI)<br>_Result: insignificant_|-“UK/ commonwealth”<br>dummy<br>(CL)<br>_Result: insignificant_|Data on countries: Australia,<br>Germany, Denmark,
There are many straightforward indicators of behavior that are plausibly related to repayment of credit. For example, a responsible borrower may carefully manage their balance over time so usage is more smooth. An individual whose usage repeats on a monthly cycle may be more likely to have a salaried income. Or, an individual whose calls to others are returned may have stronger social connections that allow them to better follow through on entrepreneurial opportunities.

From raw transaction records, we extract approximately 5,500 behavioral indicators. Determining how to extract relevant behaviors from unstructured transaction records is the critical step in standard 3
following the banking literature, we can capture the exposure of countries to a bank soft budget constraint problem by exploiting information on banks’ independence and on the regulation and monitoring to which banks are subject. Banks’ soft budget constraint attitudes can be more pronounced in settings where banks are less independent in their loan-granting decisions and more susceptible to the influence of external parties such as the government, which could put pressure on banks to rollover loans to exporters (Allen and Gale, 2001). Soft budget constraint problems may also be more severe in countries in which bank managers are less subject to close supervisory scrutiny on their decisions, implying more freedom of bank managers to evergreen loans to borrowers (Arestis, de Paula and de Paula, 2008; Lardy, 1998). Bank managers may indeed have the incentive to conceal the status of loan portfolios by evergreening loans (Niinimaki, 2007).

To perform the tests of this section, we integrate the data sources used in the baseline analysis with data from the World Bank Banking Supervision Survey, which collects 15
GLS transformation.

As described in Cogley and Sargent (2005), conditional on Γ, _λit_ and _h_<sup> ̃</sup> _it_ , the elements of _A_<sup> ̄</sup> are coefficients in linear regressions involving the residuals of the panel VAR. Therefore, their conditional posterior is standard.

Given _h_<sup> ̃</sup> _it_ , equation 3 is simply a panel VAR with fixed effects. As we employ conjugate priors for _B_<sup> ̄</sup> and _Q_ , their conditional posteriors are well known and easily sampled from. Geweke (1993) shows that the conditional posterior of _λit_ is a Gamma distribution and this set of parameters is easily drawn. We use a random walk Metropolis Hastings algorithm to draw from conditional posterior of the degrees of freedom parameters _νK,λ_ .

With a draw of Γ _, A,_<sup> ̄</sup> _B, Q_<sup> ̄</sup> and _λit_ in hand, equations (1) to (3) constitute non-linear state space model for each country. To draw from the conditional posterior of _h_<sup> ̃</sup> _it_ we use the particle Gibbs sampler of Andrieu et al. (2010a) and Lindsten et al. (2014).

We use 21,000 iterations and retain every 2<sup>_nd_</sup> draw after a burn-in period of 1,000 draws. In the technical appendix (figure A2.1) we show that the estimated inefficiency factors are low, providing evidence in favor of convergence of the algorithm.

# **4 Data**

This section, along with Appendix A3, provide the definitions, transformations, and sources of the data used in the baseline model (and extensions). Data for 54 advanced economies and EMDEs are used in the baseline model from 1980Q1 to 2022Q4 (see table A3.1). The main variables are described in table 1 and the baseline
*Mozambique**|Predomina<br>hydrogener|ntly<br>ation|Predominantly thermal<br>generation|Other<br>developing<br>regions|
|Residential at 100 kWh/month|6.8|10.7||15.7||
|Commercial at 900 kWh/month|8.0|12.9||19.0|5.0 – 10.0|
|Industrial at 100 kVA|6.5|9.3||13.0||

_Source:_ Eberhard and others 2009; derived from AICD electricity database (www.infrastructureafrica.org/aicd/tools/data). Other sources include: access data coming from Demographic and Health Surveys 1997 and 2003; utility data from AICD electricity database (www.infrastructureafrica.org/aicd/tools/data). Data referring to outages is coming from the 2007 Enterprise Survey. _Note:_ [1] The total consumption was 474 kWh: 29 kWh domestic, 396 industry, and 48 other. — = Not available.

31
Vietnam focus on food and foodstuff goods, while consumption of other goods and services, such as household equipment, energy, health and education are often disregarded or confined to broad aggregates such as “other” or “non-food” expenditure.

The study of Niimi (2005) is probably the best known paper on Vietnamese consumption elasticities, not only because it estimates an AIDS using the first two waves of the VLSS (1992-93 and 1997-98), but also because it addresses a key issue in demand analysis, namely comparing different methods to account for quality effects and measurement errors in the context of Vietnam. This issue concerns demand analyses using household surveys. Such surveys are the most frequently used sources of data, as they are nationally representative and have detailed consumption questionnaires. However, in the large majority of the cases household surveys do not collect household-level goods’ prices. To obviate this lack of information, researchers calculate a unit value as the quantities consumed of a given good divided by the good’s total expenditure. Unit values are largely used in demand systems as proxies for household prices, but they can generate biased estimations.

Deaton (1988, 1990) discussed and addressed in details the potential bias of unit values, identifying two main sources. First, bias can be due to unobserved quality substitution. Commodities are subject to aggregation and unit values reflect the quality of diversified goods within the commodity aggregation. For example, the commodity group “rice” might be composed by a mix of high-quality and more expensive sweet Jasmine rice and lower-quality and cheaper glutinous rice. Households can respond differently to an increase in the price of Jasmine rice: they can decide to reduce the quantity consumed, or they can switch to glutinous rice in order to maintain the same total calorie intake. But if the two are aggregated this trade-off is not observed. Second, quantities and expenditures are likely to be measured with errors, therefore also unit values derived from the ratio between the two are likely to be measured with errors.

Deaton (1988
employment and capital stock data. They can serve as additional controls to gauge the true effect of GVC integration.

For the estimation, we merge the industry coverage of the OECD ICIO database with the WIOD database to allow for comparability and robustness checks, which leaves us with 29 industries. In addition, we exclude nontradable industries, so that the sample consists of 20 industries, of which 2 are primary industries, 13 manufacturing industries, and 5 service industries. For a description on how the variables are constructed, see appendix 5.

# **_Policy Variables_**

To get a comprehensive picture of the role that policy plays in economic upgrading through GVC participation, we analyze seven different categories. These areas can be divided into policies targeting investment and trade flows (categories 1 to 3), the business climate including financial and labor markets (categories 4 to 6), and the quality of input factors (category 7).

_Infrastructure_ has four main dimensions: roads, ports, rail lines, and airports. We proxy for the two latter using the World Development Indicators’ (WDI) _rail_ line coverage (kilometers of rail lines per person)<sup>5</sup> and _airfreight_ volume (value of air cargo as percentage of GDP); the former two are measures from the World Economic Forum’s (WEF) assessment of _road_ and _port_ infrastructure for the Global Competitiveness Report. The report is based on business executives' perceptions of their country's port or road facilities. Data are from the WEF's Executive Opinion Survey and only available from 2006 on, but no indicators with larger country and time coverage are available. WEF scores range from 1 to 7, with 7 being the optimal value. Finally, for a subset of OECD countries in the WIOD sample, we look at investment in infrastructure ( _infrainvest_ ) as percentage of GDP available from OECD Stat.

_Connectivity_ looks at procedures and controls governing the movement of goods and services across and within national borders, as well as
# Figure 13. LAC: Potential growth prospects

A. Potential growth <!-- Start of picture text --> A. Potential growth B. Potential growth per capita<br>Percent Percent Potential growth<br>5 2000-10 2011-21 Actual growth<br>2022-30 2000-21 3 2000-2021 potential growth<br>4 2<br>3 1<br>2 0<br>1<br>0<br>LAC South Mexico and Caribbean LAC<br>America C. America<br>C. Working age population in LAC D. Contributions to potential growth<br>Percent of total population Percent TFP Capital Labor Potential growth<br>70 8<br>66 6<br>4<br>62<br>2<br>58 0<br>54<br>50<br>LAC EMDE commodity EMDEs<br>exporters<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br>2000-10 2011-21 2022-30<br>1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN World Population Prospects; World Bank. Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-19.

A. D. Period averages of annual GDP-weighted averages. Estimates based on production function approach. A. LAC subregions as in Figure 12.

C. Projections based on median fertility and mortality scenario, and medium international migration, per the definition of projection scenarios in the World Population Prospects published by the United Nations Department of Economic and Social Affairs.

D. Sample includes 53 EMDEs, of which 16 are from LAC, and 30 commodity-exporters.