rafmacalaba/data-use-mentions-extended huggingface dataset:
D, Table A9 in Appendix A). Finally, we find a stronger effect among countries with lower share of trade, but our estimates are not statistically significant (Panel E, Table A9 in Appendix A).

In this paper, we are also interested in examining the role of information and communication technologies (ICTs). It is reasonable to argue that ICTs, particularly the Internet, may contribute to poverty reduction by providing access to markets, decreasing transaction costs, and increasing income for a significant proportion of people living in developing countries. Therefore, we expect that regions with better internet coverage will be less vulnerable to the effects of higher temperatures. To do this exercise, we exploit the ICT Development index from the International Telecommunication Union as well as the global expansion of mobile network (2G, 3G, and 4G) from Collins Bartholomew with the latter being available at the grid level which allows us to construct a regional index. We then present coefficients on the interaction between our ICT measures and temperature in Table A10 (Appendix A). Across all panels, we find strong and consistent evidence of the role of ICT as the mediator. Specifically, areas with better access to ICT/internet broadband are less vulnerable to the effects of higher temperature.

# **References**

Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions. _Journal of Environmental Economics and Management_ , _103_ , 102360.

Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. _Scientific Data_ , _5_ (1), 1–15.

79
# 3.3. Additional Methodological Parameters

Over-time comparability. ISATs and RSATs have been designed to be comparable since the late 1990s and early 2000s. Thus, the use of these modern assessments enables comparability over time.

Time Intervals. While this is one of the largest and most comprehensive comparable learning outcomes database produced to date, it is still sparse given limited test frequency. In past databases we smoothed time intervals over 5-year periods. This produced continuously spaced intervals, was designed to reduce noise by averaging results within these intervals and was comparable to the Barro-Lee approach for years of schooling. In this update, we have moved away from this approach. We now provide the year of the Reference Test X as documented in official reports. Since the doubloon index is a fixed conversion rate over a given interval, we produce a score for Anchor Test Y in the same year as Reference Test X. This enables greater granularity and precision of the data and enables the users of the database to make trade-offs at their discretion.

Schooling Levels. We construct a score for each grade and pool across grades within each schooling level to produce primary and secondary school scores. We include the default grade for TIMSS as the anchor grades across levels: grade 4 for primary and grade 8 for secondary. If we have data for additional grades, we include the closest grade with a preference for older grades since learning trajectories are steepest in the early years of learning introducing more scope for grade bias at earlier grades. If we have multiple test scores, we include the closest grade, and exclude the rest, rather than averaging. For example, we include PASEC grade 5 scores and exclude grade 2 scores. We include SACMEQ grade 6 scores along with PIRLS grade 4 scores at primary. If the test is designed for an age group (for example, PISA) we code it at the schooling level (for example, secondary for PISA).

Conceptually, the broader categories of primary and secondary scores enable us to categorize learning at critical schooling
NA|NA|NA|NA|NA|
|Investment|3403|6.8%|NA|NA|NA|NA|NA|NA|NA|NA|NA|

_Note: Y refers to production, D is domestic consumption, X is exports, and M is imports in billions of dollars. CO2 is domestic emissions of carbon dioxide in millions of metric tonnes. Int is CO2 intensity in tonnes of CO2 emissions per $1,000 of production. abs is the value in dollars or tonnes as described above. pct is the sector share of the total economy in percentage terms._ rebating options based on empirical data.<sup>14</sup> With the United States as the country for investigation, we aggregate the 65 sectors in GTAP to 11 sectors reflecting the specific requirements of our research question. In the composite data set, we distinguish energy-producing and transforming sectors (coal, gas, crude oil, electricity, and oil refining), energy-intensive and trade-exposed industries (iron and steel, nonferrous metals, nonmetallic minerals, chemicals, pulp and paper), and an aggregate sector (rest of the economy) reflecting the remainder of the economy. In the results below, we group these 11 sectors into two composite segments: an aggregate of emission-intensive and trade-exposed industries (EITE) and an aggregate non-EITE sector (NEITE).<sup>15</sup> Investment and government demand are exogenous and held fixed across the different simulations that we conduct. Key economic variables describing the economy are presented in Table 3.

In our simulation, we introduce each type of policy described above sequentially. In

> 14Of course, the model is a general equilibrium model, whereas the theory focuses on partial equilibrium outcomes, so some potential for discrepancy does exist.

> 15While electricity and refined petroleum products are not always considered as highly trade exposed, they are often still singled out for special treatment. The EU has refineries on its carbon leakage list. Canada includes electricity in
distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample. The rivers include the Nile, Niger, Senegal, Zambezi, and Congo together with its tributaries the Ubangi and Kasai.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 19
volume (more than 20%) in 2009. Debt overhang in the tradable sector seems, however, to be an issue for a broader set of countries as well: In Bosnia, Turkey, the Slovak Republic, Serbia, the Czech Republic and Croatia more than a quarter of the export-orientated firms are highly leveraged and all of these countries also experienced huge negative shocks to their tradable sector.

# <u>Foreign Currency Lending</u>

Foreign currency (FX) borrowing is seen as a major threat to financial stability in Emerging Europe. More than 70 percent of all private sector loans in Estonia, Latvia, and Serbia are currently denominated in (or linked to) a foreign currency. The share of FX loans also exceeds that of domestic currency loans in Bulgaria, Hungary, and Romania (EBRD, 2010). With regard to debt overhang, foreign currency borrowing is a key potential driver among firms in the region. The substantial depreciation of several currencies before and during the financial crisis imply that those firms which have taken unhedged FX loans now face a substantially higher debt burden, which may constrain their operations and investment going forward. Whether or not FX lending may seriously curb the economic activity of firms in the region depends on the answers to two questions: (1) What share of the firms in the region hold unhedged foreign currency loans? and (2) Are firms with unhedged FX loans predominantly located in those countries where sharp depreciations have taken place?

Several studies using aggregate cross-country data have suggested that FX borrowing is predominantly driven by retail customers taking unhedged currency bets. Luca and Petrova (2008) show that the share of FX loans is higher in countries with higher interest rate differentials. This finding has been supported by Basso et al. (2010) or for example by Rosenberg and Tirpak (2010). Recent firm-level evidence suggests that FX borrowing in the region is not driven predominantly by unhedged customers taking currency bets to benefit from interest rate differentials. Examining the 2005 wave of BEEPS
**Table 4.1.2 : Variables in the AD-OTH-Master Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**A.**|**AD_CTY_NAME**|Country name of the user initiating the antidumping investigation|
|**B.**|**CASE_ID**|Case identifier used to link observations across different elements of the database<br>[generated by us]|
|**C.**|**CASE_REPCODE**|Related antidumping investigations of multiple countries at the same time over<br>the same product|
|**D.**|**INV_CTY_NAME**|Country name of the foreign country under investigation|
|**E.**|**INV_CTY_CODE**|3-letter UN country code of the foreign country under investigation (see table 2.2)|
|**F.**|**PRODUCT**|Description of the product under investigation|
|**G.**|**INIT_DATE**|Date of initiation of the investigation (MM/DD/YEAR)|

# **5 Countervailing Duty-Using Countries in the Database**

The data for each CVD user country is contained in a Microsoft Excel 2003 Workbook file (posted at ~ <u>http://www.brandeis.edu/ cbown/global_ad/</u> ), within which are typically two spreadsheets of data: 1) a master spreadsheet with basic antidumping case investigation information; and 2) a spreadsheet containing information on the Harmonized System (HS) products under investigation. There are only three countries with detailed countervailing duty data in the database thus far:

**Table 5.1 : Antidumping User Countries with Detailed Data in the Database**

|**Country Code**<br>**
# III. **METHODS**

In the panel dataset we use—the Vietnam Living Standards Survey (VLSS)<sup>4</sup> —we observe most of the outcomes of interest during the first wave (fielded in 1992/93 just before the introduction of VHI), and again in the second wave (fielded in 1997/98). In what follows, _t_ =1 denotes 1992/93 and _t_ =2 denotes 1997/98.

Let _yit_ be the outcome of interest for individual _i_ at time _t_ . In our empirical analysis _y_ alternates between health status, out-of-pocket payments, nonmedical consumption and utilization of health services. We assume that the change in _yit_ between 1993 and 1998 in the case of the “treated” group (i.e. those enrolled in VHI after it was introduced) can be written<sup>5</sup> :

> where ∆ denotes the change between 1993 and 1998,<sup>_y_</sup> _it_ *<sup>is the counterfactual outcome for VHI</sup>

enrollees, _Git_ is the “gain” or impact of the program, θ _t_ is unobserved and specific to period _t_ , ε _it_ is a white noise measurement error term associated with the outcome variable, and _N_ is the number of individuals who were enrolled in VHI in period 2. For each participant (i.e. insured), we use propensity score matching (PSM) to identify a matched non-participant (cf. Rosenbaum and Rubin 1983).<sup>6</sup> This involves estimating regression (e.g. probit) equations for the participation decision, and defining a nearest neighbor for each participant as the non-participant with the closest propensity score (predicted participation probability). We assume that for the _N_ matched controls we can write: where _uit_ is a white-noise error term. Subtracting eqn (2) from eqn (1) gives the doubledifference or difference-in-differences: which, given _Gi_ 1=
at a given level of GDP per capita, implying an upward shift in the SPEC at the lower end. That can be assessed using Figure 3, which augments Figure 1 to provide the nonparametric SPECs for various years. We see that, if anything, the SPECs have tended to shift downwards over time. Among low-income countries, we see little or no change in the SP share at a given level of GDP per capita except for a drop in average levels between 1995 and 2000. In the middle-upper income range, we actually see a decline after 1995. Across all levels of GDP, the 1995 SPEC is unambiguously higher than those for 2005, 2010, 2015 and 2019, while the 2019 SPEC is unambiguously lower than those for all years except 2000 and 2005. The greater emphasis on SP in development policy discussions in the new Millennium does not appear to have shifted the SPEC upwards.

This finding is not inconsistent with the recent expansion in SP programs in developing countries; it is just that these programs have typically had low coverage (on average) in lowincome countries, as documented in Ravallion (2017). The rise in SP spending as a share of GDP in developing countries is coming from higher GDP rather than a change in development priorities in favor of a higher share of national income going to SP at given GDP.

# **3.3 Data on social protection spending during the pandemic**

Our panel data set on SP spending prior to the pandemic is augmented by a database compiled by a team including two of the present authors and documented in Demirgüç-Kunt et <u>al. (2022). This provides estimates of the expenditures incurred in social protection measures that</u> the government implemented in response to the pandemic over 2020 and 2021 in 154 countries. The data draw on information from the Global Database on Social Protection Responses to COVID-19 (Gentilini et al. 2021), budget data from official documents (including IMF Article IV revisions and other international organizations’ related documents)
Policy Research Working Paper 5867

# **Abstract**

Infrastructure investments are typically long-term. As a result, observed benefits to households and communities may vary considerably over time as short-term outcomes generate or are subsumed by longer-term impacts. This paper uses a new round of household survey as part of a local government engineering department’s rural road improvement project financed by the World Bank in Bangladesh to compare the short-term and longterm effects of rural roads over eight years. A dynamic panel model, estimated by generalized method of moments, is applied to estimate the varying returns to public road investment accounting for time-varying unobserved characteristics. The results show that the substantial effects of roads on such outcomes as per capita expenditure, schooling, and prices as observed in the short run attenuate over time. But the declining returns are not common for all outcomes of interest or all households. Employment in the rural non-farm sector, for example, has risen more rapidly over time, indicating increasing returns to investment. The very poor have failed to sustain the short-term benefits of roads, and yet the gains accrued to the middle-income groups are strengthened over time because of changing sectors of employment, away from agriculture toward non-farm activity. The results also show that initial state dependence—or initial community and household characteristics as well as road quality—matters in estimating the trajectory of road impacts.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at skhandker@worldbank.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
but not by much given that India has by far the largest South Asian diaspora. For regressions, measurement error would typically result in the estimates of network effects being downward biased, but even here this is not clear to the extent that other South Asians more likely to work with India. Significantly, the estimations in Table 10 use specific data on diaspora and paint a similar message, including showing how a higher Indian diaspora strongly correlates with our ethnic Indian measure on oDesk.

9 This match rate rises somewhat when removing records that are either missing names or have non-name entries in the name field (e.g., either the company is listed in the name field or a bogus name like “test”). The four most common surnames linked with the Indian ethnicity are Kumar, Singh, Ahmed, and Sharma.

10 A related limitation, however, is that the oDesk data do not easily link company contacts into larger firms. This structure limits our ability to describe the firm size distribution on oDesk, but for most applications this has limited consequence. For researchers, this structure is operationally quite similar to patent assignee codes/names.

11 Our data do not indicate whether side arrangments form between companies and workers. We suspect, but cannot verify, that the number of cases where an employer asks a pre-arranged contact to enlist on oDesk in order to employ them is low due to the fees that oDesk charges. It is more likely that successful employment relationships move offline and into side arrangements to circumvent oDesk fees. This would potentially impact our analysis to the extent that the likelihood of moving offline was greater for diaspora-based connections. We have not seen evidence to suspect that side arrangements have an ethnic bias to them.

9
When evaluating marginal expansions in access to school, the relevant quantities are the returns and costs for the marginal student, not the returns and costs for the average student. In spite of the importance of this topic, there are hardly any estimates of average and marginal returns to schooling in developing countries. Two exceptions using Chinese data are Heckman and Li (2004) and Wang, Fleisher, Li and Li (2011).

Estimating the extent to which returns vary across individuals is also important if we want to understand schooling choices, and their relationship with labor market performance. Studying who is more likely to attend school, and documenting how returns differ between those who attain high and low levels of education, and across individuals more generally, is essential for learning about the main incentives and constraints to school attendance.

We estimate a semi-parametric selection model of upper secondary school attendance and wages using the method of local instrumental variables (Heckman and Vytlacil, 2005). Our data comes from the Indonesia Family Life Survey. Carneiro, Heckman and Vytlacil (2011) use a similar model to estimate the returns to college in the US. Although they examine a different country in a different time period, and a different level of schooling, they also find that the returns to college vary widely across individuals in the US, and that the return to college for the marginal student is well below the return to college for the average student (see also Carneiro and Lee, 2009, 2011).<sup>2</sup> These papers document, across very different environments, how important it is to account for heterogeneity in the returns to schooling. They also show how it is possible to take exactly the same data which is used to estimate a measure of the return to education by instrumental variables methods (IV), and extract much more information from it (allowing us to characterize the heterogeneity in returns across individuals). This can be done using fairly standard parametric methods for estimating selection models, or using a more
policies on its aggregate imports. It indicates the degree of domestic inefficiency caused by the trade regime. It is calculated as an equivalent uniform tariff of a country’s tariff schedule that would keep domestic import levels constant. Product level tariffs are weighted by import shares and expressed as a tariff rate. I introduce several estimation methods to explain how MA-TTRI and TTRI distort export performance of countries and show how investment climate interact with this relationship.

Dollar et al. (2006) show that a favorable business environment works in the direction of decreasing the sunk costs of exporting and eventually leads to higher participation in export markets. Such an environment can also increase export volumes of countries by reducing the distortions of the restrictive market access policies. Ability of a country to improve its foreign market access is limited to making multilateral and bilateral trade agreements. Such policy actions could be less practical and less convenient for governments relative to the policy changes for improving business environment. In this study, I focus on several investment climate measures. The choice of these indicators was motivated by the existing studies in the literature. As it was briefly discussed in the introduction, there are plenty of empirical studies that show institutions, infrastructure, trade facilitation, and regulations affect export performance of countries. To show how these aspects of investment climate affect export performance I choose the following indicators: regulatory quality, financial development, business entry regulations, exports facilitation, quality of infrastructure, and property rights.

> 5 The TTRI index used here is for the most favored nation which is the most common used tariff measure in the literature.

4
WPS3773

# **Global Logistics Indicators, Supply Chain Metrics, and Bilateral Trade Patterns**

Warren H. Hausman Hau L. Lee Uma Subramanian

## **_Abstract_**

Past research into determinants of international trade highlighted the importance of the basic spatial gravity model augmented by additional variables representing sources of friction. Studies modeled many sources of friction using various proxies, including indices based on expert judgment in some cases. This paper focuses on logistics friction and draws on a data set recently compiled by the World Bank with specific quantitative metrics of logistics performance in terms of time, cost, and variability in time. It finds that the new variables that relate directly to logistics performance have a statistically significant relationship with the level of bilateral trade. It also finds that a single logistics index can capture virtually all of the explanatory power of multiple logistics indicators. The findings should spur public and private agencies that have direct or indirect power over logistics performance to focus attention on reducing sources of friction so as to improve their country’s ability to compete in today’s global economy. Moreover, since the logistics metrics are directly related to operational performance, countries can use these metrics to target actions to improve logistics and monitor their progress.

### World Bank Policy Research Working Paper 3773, November 2005

_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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _______________ Warren H. Hausman and Hau L. Lee are at Stanford University, and Uma Subramanian