rafmacalaba/data-use-mentions-extended huggingface dataset:
default on its debt or to monetize its debt, or be forced to undertake a major fiscal retrenchment to avoid default or monetization.

A starting point for analyzing fiscal sustainability is the current levels and medium-term projections of general government fiscal balances and public debt. It is relatively straightforward to determine the impact of such fiscal balances, if extrapolated forward into the future and combined with some basic macroeconomic projections, on the evolution of public debt levels, with debt sustainability being defined as a situation where public debt as a ratio of GDP is stable or falling and/or does not exceed some critical threshold.<sup>2</sup> Section 2 of this paper examines the general government fiscal balances and public debt levels in Romania and the medium-term projections of these variables. The general government fiscal data include the local governments.

The data captured in the general government budget, however, often provide an incomplete and hence misleading picture of the sustainability of public finances over the long term. This is starkly illustrated by a comparison of current public debt levels with calculations of the longterm net worth of the government in Romania, taking into account the long-term costs of aging. Romania’s outstanding public debt amounted to 32 percent of GDP in 2010, a relatively moderate level. In contrast, Velculescu (2010) estimates the inter-temporal net worth of the Romanian government at negative 252 percent of GDP, based on a finite horizon approach and negative 1,097 percent of GDP based on an infinite horizon approach.

Risks to fiscal sustainability may emanate from activities of the public sector, which are not currently part of the general government budget, but which might eventually impose fiscal liabilities on the budget. These could include contingent liabilities of government, such as loan guarantees, or the quasi fiscal deficits (QFDs) of state owned enterprises. The QFDs of the state owned enterprises are discussed in Section 3.

> 2 An example of such a threshold is the ceiling of 60 percent of GDP for public debt set by the Stability and Growth Pact
Haelermans, C., Jacobs, M., Van Der Velden, R., Van Vugt, L., Van Wetten, S. 2022, Inequality in the Effects of Primary School Closures due to the COVID-19 Pandemic. _American Economic Association Papers and Proceedings_ 112: 303-307.

Hammerstein, S., König, C., Dreisörner T., Frey, A. (2021). Effects of COVID-19-Related School Closures on Student Achievement-A Systematic Review. _Frontiers in Psychology_ .

Jack, R., Halloran, C, Okun, J, Oster, E. 2022. Pandemic Schooling Mode and Student Test Scores. _American Economic Review: Insights_ .

Jakubowski, M., T. Gajderowicz and H.A. Patrinos. (2023). Global Learning Loss in Student Achievement: First Estimates using Comparable Reading Scores. University of Warsaw Working Paper.

Maldonado, J.E., De Witte, K. (2022). The effect of school closures on standardised student test outcomes. _British Educational Research Journal_ 48(1): 49-94.

McKinsey (2022). How COVID-19 caused a global learning crisis. <u>https://www.mckinsey.com/industries/education/our-insights/how-covid-19-caused-a-globallearning-crisis#</u> Patrinos, H.A. (2023). The Longer Students Were Out of School, the Less They Learned. _Journal of School Choice_ 17(2): 161-175.

Patrinos, H., Vegas, E., Carter-Rau, R. (2023). An Analysis of COVID-19 Student Learning Loss. In Jonathan Hamilton (Ed.), _The Oxford Research Encyclopedia of Economics and Finance_ . Oxford: Oxford University Press.

Pier, L., Hough, H. J., Christian, M., Bookman, N., Wilkenfeld, B., Miller, R. (2021). COVID19 and the educational equity crisis: Evidence on learning loss from the CORE data collaborative. Policy Analysis for California Education.

Psacharopoulos, G.
**Table 2.2: Household characteristics and proxy variables**

|Household characteristics|Interactingvariables|
|---|---|
|Abilityof head to access copingstrategies|Head education level,sector of occupation, gender|
|Household composition that allows labour response|Dependencyratio,ratio of able‐bodied|
|Household assets that mitigate shock|Cattle,other livestock, jewelry|
|Other shocks that compromise ability to mitigate<br>shock|Illness, livestock disease, crop damage from pests|
|Access to institutional coping strategies|Distance to market, access to financial products<br>(insurance,credit), public safetynet access.|

Finally, agro‐climactic zone is discussed in a separate section below.

# **c. Internal validity**

We keep with the notation of Antilla‐Hughes and Sharma (2014, AHS) who outline succinctly the issues in establishing internal validity when estimating hazard relationships: in our case the internal validity is concerned with the establishment of a causal (or, at least, externally replicable) relationship between crop loss and consumption. In the absence of a randomised experiment assigning some households to better community‐level crop yields than others, we must assume implicitly that (in some specific sense) the crop loss in a given year is exogenous to (uncorrelated with) unobservable household and community characteristics that affect household consumption. We acknowledge that there are some issues around this. For example, if crop yields are highly correlated over time this may lead to lower average consumption in the community, and therefore a bias towards households who are less intrinsically productive living in high crop loss communities. Without a panel dataset we are not able to deal with this and to the extent that other unobservable household and community characteristics that impact consumption are not included in the regression, and _are correlated with the level of the drought shock in a given year_ , we may be capturing biased estimates of the relationship between drought and consumption. We can include average rainfall at
literature is the construction of asset and liability positions for each country vis-à-vis the rest of the world, using cumulative gross capital flows from balance of payments data (Lane and Milesi-Ferretti 2001, 2007). This strand of the literature shows that external assets and liabilities grew rapidly over time until the 2007–08 global financial crisis (GFC) (Gourinchas, Rey, and Govillot 2010; Obstfeld 2012; Lane and Milesi-Ferretti 2018). Since then, the growth in gross external positions overall has stagnated, reflecting a retrenchment of cross-border banking activity and the growing weight of the less integrated South in world GDP.

Third, several studies analyze bilateral international investments, as we do in this paper. Most of these papers focus on a single investment type and use gravity models to estimate the role of various determinants of capital flows and international investments (Portes and Rey 2005; Aviat and Coeurdacier 2007; Stein and Daude 2007; Lane and Milesi-Ferretti 2008; Coeurdacier and Gibaud 2011; Aggarwal, Kearney, and Lucey 2012; Hale and Obstfeld 2016; Hellmanzik and Schmitz 2017; Brei and von Peter 2018). A few papers combine data for different investment types to study the role of information frictions and institutions, investments between different regions before the GFC, and their network structure (Daude and Fratzscher 2008; Milesi-Ferretti, Tamirisa, and Strobbe 2010; Kubelec and Sa 2012).

In this paper, we use rich bilateral data sets with ample coverage across investment types, countries, and years, to analyze the rise of the South in global finance. Relative to studies that rely on the aggregate investment positions of countries vis-à-vis the rest of the world, our bilateral data shed light on the source _and_ destination of international investment. For example, our more granular data allow us to show that South-to-South investments have grown the fastest. We ensure that data on investment types are comprehensive and mutually exclusive. For example, our bilateral data on bank loans and deposits improve over previous studies by stripping out banks’ portfolio holdings, because these are
<br>Year<br>Mon. Indep., non-EMG LDC Exchr. Stab., non-EMG LDC<br>KAOPEN, non-EMG LDC<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text -->

# **Definitions: The index for the extent of monetary independence** (MI); _MI_ =

1  0.5[ _corr_ ( _ii_ , _i j_ )  ( 1)] , where _i_ refers to home countries and _j_ to the base country (the base country is defined as the country that a home country‘s monetary policy is most closely linked with, as defined in Shambaugh (2004).<sup>33</sup> By construction, higher values of the index mean higher monetary policy independence. For the countries and years for which Shambaugh‘s data are available, the base countries from his work are used, and for the others, the base countries are assigned based on IMF‘s _Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER)_ and _CIA Factbook_ . **Exchange rate stability (ERS)** , ERS = Annual standard deviations of monthly exchange rate series between the home country and the base country are calculated and included in the following formula to normalize the index between zero and one: _ERS_  0.01/[0.01  _stdev_ (  (log( _exch_ rate_ ))] .

**Financial openness (KAOPEN): KAOPEN** = A _de jure_ index of capital account openness constructed by Chinn and Ito (ref), normalized between zero and one. Higher values of this index indicate that a country is more open to cross-border capital transactions.

> 32 Source: Aizenman, Chinn and
nancial development may decline if firms reduce their demand for loans because, for example, their production falls as the economy slows down (demand channel). At the same time, a reduction in financial development may be due to the desire of financial institutions to reduce their leverage and exposure when financial conditions worsen (supply channel). We adopt two variables as possible instruments: the lagged value of financial development (assuming that only contemporaneous effects matter) and _Weighted domestic credit to private sector by banks (% of GDP)_ . The latter captures the share of domestic credit to the private sector provided by banks, and it is weighted by the ratio of foreign bank branches to total number of branches in the region. Ongena, Peydr ́o, and Van Horen (2015) uses matched firm-bank level data for Eastern Europe and Turkey to show that foreign-owned banks contracted their lending more during the financial crisis, than locally-funded domestic banks.

The results are summarized in Tables 6 and 7. The instruments appear highly correlated with the dependent variable, but not uncorrelated with the error term as we reject the null that the instruments are valid. The inclusion of the instrumental variables does not change our results.

# **4 Conclusions**

Foreign banks are important for trade as they provide finances and information to local firms. By focusing on Turkey where few firms use foreign banks to finance trade, we show the importance of the information channel that foreign banks play in promoting exports. We find that the presence of foreign banks will increase the exports of sub-regions in Turkey. The exports will further increase as the footprint or share of the foreign bank branches increases. We also show that there are spillover effects from other foreign banks of countries neighboring the trade partner: the presence of these foreign banks can increase exports to the trade-partner country but as their footprint increases, they can divert trade and reduce exports to the trade partner. The positive relationship between foreign banks and exports does not change with the financial crisis.

These results show
# **1 Introduction**

What is the relationship between international trade and business cycle synchronization? Do Global Value Chains (GVCs) have a specific impact on the correlation of GDP and if so, does it vary by income group? Since the seminal paper by Frankel and Rose (1998), a very large empirical literature has studied the determinant of cross countries’ GDP synchronization, showing that bilateral trade is an important and robust determinant of GDP correlation.<sup>1</sup> The goal of this paper is to assess the link between trade proximity and GDP comovement on a large sample of **150** countries from **1962 to 2011** , including middle and low income countries. We estimate the _trade comovement_ slope (TC-slope) using constructed panel data and controlling for both country-pair and time-window fixed effects, aiming to control for unobserved heterogeneity between countries (e.g distance, common borders, common language) and over time (e.g change in world trade conditions and/or policy).<sup>2</sup> Finally, we provide various robustness checks, including controls related to trade unions, sectoral composition as well as to financial interconnection of the banking sector and foreign direct investment. Broadly speaking, our results are _robust_ to a wide range of specifications and trade indexes and highlight _important disparities_ among country groups.

Our main findings can be classified as follows:

- **Trade proximity** , defined for each country-pair as total trade over total GDP, is correlated with more GDP comovement for the whole sample of countries **and** most of this correlation is explained by **trade in intermediate inputs** as opposed to trade in final goods.

While the association between international trade and business cycle synchronization is not a new fact, the specific role of input trade points toward a dinstinctive role for Global Value Chains.<sup>3</sup>

- **Trade network** , defined as proximity in
informal sector. The coverage of the informal sector in national accounts data varies from country to country (Timmer and de Vries 2009). While all countries make an effort to track the informal sector, obviously the quality of the data can vary greatly. In contrast, employment shares are more likely to include the informal sector, because they are typically obtained from nationally representative household surveys (labor force surveys or population censuses). A failure to account for activity in the informal sector will lead to an underestimate of value-added in activities that are heavily dominated by informality, such as agriculture.

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same road segment, some shipping carriers may drive faster or transport cargos at lower prices than others.

21. The summary statistics are shown in **Table 1** . Our dependent variable is the road transport freight costs in U.S. cents per km. The freight costs include both vehicle transport costs, such as drivers and fuel costs, parking fees and minor repairs, and other trade and administrative costs at intermediate nodes, such as border crossing and traffic police checkpoints. All kinds of expenditures are included (see ADB (2014)). The costs are converted to U.S. cents and normalized to 2010 constant dollars using the U.S. consumer price index. The average freight cost is 4.57 U.S. cents per ton-km with a standard deviation of 2.41. It is distributed in a highly skewed manner around 4 to 5 cents ( **Figure 10** ). If a heavy truck with a maximum capacity of 20 tons is fully loaded, the average shipping cost for 100 km distance would be approximately US$91.

22. The distance, average speed and freight volume data also come from the CAREC CPMM database. Our sample is focused on long-haul cargos. The average distance is 170 km. The average speed is 50 km per hour. Note that the speed may differ across routes even though the freight operator, i.e., driver is the same. On the other hand, the freight volume is generally unchanged across the routes as long as they constitute the same shipment. The average cargo weight is 18.2 tons. As discussed above, most common vehicles in our sample data are 20-ton trucks. Thus, this variable is heavily concentrated to 20 tons or less.

23. The region is diverse in topographic complexity. The average vertical difference in elevation between origin and destination is about 144 meters. About 20 percent are international shipments.

24. Finally, our data comprises a wide variety of commodities
(3)|440 (8)|19 (0)|
|South Africa|4667 (89)|4477 (86)|4286 (82)|2737 (52)|
|Tanzania|2183 (42)|1404 (27)|1672 (32)|214 (4)|
|Zambia|2216 (42)|2127 (41)|538 (10)|353 (7)|
|Zimbabwe|1768(34)|1545(30)|760(15)|290(6)|
|SADC<br>combined|4823(92)|4704(90)|4482(86)|2965(57)|

Notes: Own calculations using HS 6-digit level data obtained from UNcomtrade.

Values for Tanzania are for 2007. Note that the SADC region on the import side includes the SACU members, except for SA who does not declare exports to the other SACU countries. The total number of HS2002 codes in UNcomtrade is 5222. Data is for gross exports and therefore contains re-exports To summarize, intra-SADC trade is low, but this is partly a consequence of low levels of economic development. Once we condition on income levels, SADC countries have experienced an increase in openness comparable to other developing countries. Intra-SADC trade is also found to be relatively high and diversified. Nevertheless, there are some worrying trends. Growth in intra-regional trade has slowed in recent years. Exports from the region continue to decline as a share of world trade. Therefore, in the following sections we look at the role of tariffs and other factors that impede further growth in SADC trade.

12
who visited households on alternate days to check the quality of entries, clarify any questions, and enter recorded data into computers. Instructions emphasized that households should record daily acquisitions of food and non-food commodities and meals away from home starting from the day after the first visit. Qualitative analyses confirmed a high level of enumerator effort expended to ensure that diary entries were correctly and regularly recorded.

One-third of households within each EA were also administered a recall module on food consumption during the first visit. Specifically, _two_ (diary and recall) measurements are available for household 3, 6 and 9 in each EA, while _one_ (diary) measurement is available for all remaining households. The availability of repeated measurements and the scale of the experiment mark a departure from past research on consumption measurement in developing economies. The recall module asked the market value of the quantities consumed by the household in the 7 days prior to the enumerator’s visit. A list of expenditure groups was selected based on an assessment of their importance in IHSES 2007 diaries. Fieldwork and resource constraints meant that this list was consolidated to include 20 such groups of aggregated food items (we list these groups in Appendix Figure A.2).<sup>8</sup>

> 7One of the important rationales for year-long surveys is the ability to capture seasonality in consumption and livelihoods. Seasonality in consumption is experienced due to differences in the availability of food items and, importantly, due to the month-long observations of Ramadan, and two Eid holidays. To ease fieldwork, a team would visit the 2 EAs from one of the districts in wave 1 (days 1 to 14 of the month) and the 2 EAs from the other district in wave 2 (days 15 to 29) of the month. Moreover, within each wave, the teams alternated the EA visited each day.

> 8The scale of IHSES precluded the use of individual diaries, which is deemed the most reliable instrument in Beegle et al. (2012). Our experiment is interesting because
Section 4, we implement our methods in a large-scale randomized communication campaign, we describe the administrative data used in the analysis, the empirical strategy, and evidence of direct and spillover effects. Section 5 provides some practical recommendations for designing and analyzing partial population experiments. Section 6 concludes.

# **2 Why is Cluster Heterogeneity Important?**

We consider a population where units are grouped into mutually exclusive and independent clusters. Common examples of this type of clustering are students in schools (Miguel and Kremer, 2004; Beuermann et al., 2015), family members in households (Barrera-Osorio et al., 2011; Foos and de Rooij, 2017), job seekers in local labor markets (Cr ́epon et al., 2013), employees in firms or organizations (Duflo and Saez, 2003), or households in neighborhoods, villages or other geographic administrative units (Angelucci and De Giorgi, 2009; Ichino and Sch ̈undeln, 2012; Haushofer and Shapiro, 2016; Gin ́e and Mansuri, 2018). In our application, a local property tax reminder information campaign, the population of interest consists of taxpayers in residential blocks. Within this population, we study an experimental design where treatment assignments can vary both between and within clusters.

Figure 1 shows the distribution of cluster sizes in six partial population experiments, including our analysis sample and five published papers (Cr ́epon et al., 2013; Gin ́e and Mansuri, 2018; Haushofer and Shapiro, 2016; Ichino and Sch ̈undeln, 2012; Imai, Jiang and Malani, 2021). The figure reveals substantial variation in cluster sizes. When cluster sizes are heterogeneous, it is likely that the distribution of outcomes will vary across clusters as well. For instance, one may expect the mean and the variance of the outcome to be different in large clusters compared to small clusters. We refer to the variation in outcome distributions across clusters as _distributional heterogeneity_ .

Intuitively, with heterogeneous clusters, the variance of an
Policy Research Working Paper 10284

# **Abstract**

The COVID-19 pandemic accelerated the digital transformation of businesses. Using a unique global panel dataset, this paper documents the patterns of digital adoption during the pandemic across firms in 57 (mostly developing) countries. The data show the tale of two stories. On one hand, the pandemic drove firms to increase the use of digital platforms and invest in digital solutions. On the other hand, there is evidence that the digital divide increased. There remain substantial gaps between small and large firms as well as across sectors, particularly for new investments in digital solutions. Firms that did not use any digital platform or channel before the pandemic, also lagged in their response to the pandemic, increasing the gap with those that were more digitally ready. Moreover, although the share of online sales across firms for all size groups increased, there is a growing concentration of online sales among top firms. The paper discusses some of the factors associated with this increase in the digital divide and find that changes in digitalization remain even after mobility restrictions have eased. The analysis suggests that the pandemic has accelerated digitalization, but some firms disproportionately benefited from the digital transformation, potentially increasing the digital divide.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at xcirera@worldbank.org or marciocruz@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations,