rafmacalaba/data-use-mentions-extended huggingface dataset:
.<sup>12</sup> This suggests that while pursuing export opportunities within the former Soviet Union may be of some benefit to lagging regions, high standards of living require the ability to compete globally.

Wood exports have a strong geographical orientation (see Figure 35), with the main destinations being near exporting federal regions, perhaps because transport costs as a share of the value of the goods are high. Even though the nonCU-CIS and the EU-28 are the two most targeted destinations for most regions in this product category, China is also a relatively big consumer of wood in that it is the most important destination in this product category for roughly twice as many of the regions for whom it is the main destination in the case of aggregate exports. These regions are also among those reporting the largest per capita values of wood exports.

The EU-28 is the main partner for Russia’s exports of metals from 33 federal regions—far above the 15 for whom the nonCU-CIS in the most important destination for metals, and the 13 for whom Rest of Asia is most important. The largest per capita exports of metals are quite evenly distributed across Russian territory, with notable clusters of federal regions reporting relatively high per capita values in the West (targeting mostly the EU-28 and nonCU-CIS); the Center (targeting mainly the EU-28 and in some cases Rest of Asia); and the Far East (targeting mainly Rest of Asia, and to a lesser extent China).

> 11 The definition of minerals includes oil.

> 12 In these three product categories, the majority of leading and middle-range regions still have the EU28 as their main export destination; lagging regions target the EU28 to an equal or slightly larger extent than the nonEACUCIS. Thus, even when the EU28 is the main destination for exports of certain products of a majority of lagging federal regions, there is still a relatively strong orientation toward the nonEACU-CIS, which is still the second most frequently reported main export destination and is close to the first.

15
by people working on a given plot: while in the weekly visit group, the person-plot average of total season hours was 39.5, this number jumped to 121.3 and 146.3 in recall NPS and recall ALT, respectively. Total hours worked per person-plot are 3.0 and 3.7 times higher in the recall surveys than in our preferred benchmark, the weekly visit estimates.<sup>17</sup> These results show considerable recall bias in seasonwide person-plot hours, driven primarily by error in the least granular time unit reported (days in the case of the recall NPS and weeks in the case of the recall ALT).

# **Aggregation of hours and the competing sources of recall bias**

Does the stark difference in reported hours we find by person-plot persist at all levels? Aggregating from the labor at the person-plot level to that at the household level entails introducing data on both the number of household members working in agriculture and the number of household plots on which farming takes place. If both of these new components are recorded with the same accuracy in the recall data as in the weekly data, then the degree of overreporting in hours per person-plot (presented in table 3, panel A) and in hours aggregated by household will be roughly the same. Table 3, panels B–D show this aggregation, presenting statistics on the person level (that is, all labor performed by a given person on any plot; panel B), the plot level (that is, all labor performed on a given plot by any person; panel C), and the household level (panel D). The large difference between the weekly and recall surveys virtually disappears in aggregation.

This arises because of considerable underreporting by recall households of the number of plots cultivated and the number of people farming. Table 7 shows that an average of 1.5 (or roughly 33 percent) fewer household members report they worked in farming in the recall NPS and the recall ALT
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ late 1990s and early 2000s (see below for description of key pieces of legislation). From 2009 to 2013, exports grew at an annual rate of 89 percent, with exports accounting for 80% of ICT hardware production. Export of final ICT goods grew most rapidly, overtaking intermediate goods in 2010. On the import side, final and intermediate goods imports grew at rates of 35 percent and 47 percent from 2009 to 2013. Vietnam’s trade deficit in intermediate goods has grown considerably in recent years, reaching $9.8 billion in 2013 (see Table 5), reflecting Vietnam’s emerging role in the ICT GVC as a hardware assembly hub. This role is also reflected in soaring final goods exports, which reached $15.7 billion surplus in 2013.

**Table 5. Vietnam Hardware Trade Balance, 2009-2013, USD millions**

||2009|2010|2011|2012|2013|
|---|---|---|---|---|---|
|**Intermediate Goods**|-$1,416|-$1,230|-$2,950|-$6,052|-$9,839|
|**Final Goods**|-$2,262|-$982|$2,853|$8,185|$15,746|
|**Total**|-$3,678|-$2,211|-$97|$2,133|$5,907|

_Source:_ UN Comtrade A closer look at the trade statistics reveals that Vietnam’s hardware trade is concentrated in a few narrow product groups (See Figure 2). Eighty-one percent of imports are in intermediate electronic components such as integrated circuits and LEDs, while final goods imports are concentrated in communications equipment such the base stations needed for rapid
conducted our analysis of expenditure at the household level—and average household size may vary across deciles, particularly in Africa—average total expenditure at the household level may not increase as expected when moving across the household per capita expenditure distribution.

# **Service area (SA)**

Following Angel-Urdinola and Wodon (2007a), households are considered to have potential access to a network if they are located in a service area ( _SAh_ ),<sup>9</sup> which takes the value of 1 if other households in their neighborhood reported having water connections in a household survey. We assume that households in a _SAh_ have the option of connecting to the piped water network present in their neighborhood. The neighborhood is proxied by the enumeration area where the household is located, according to household surveys. However, this assumption may not always be correct. If a neighborhood covers a large geographical area, or if the presence of an adjacent water main varies by household, the survey figures may overestimate the actual number of households with potential access to the piped water network.

We can define the ratio of all households that have potential access ( S h=1 1(S h<sup>= 1)</sup> ]/ ) and the equivalent ratio for the poor, which is defined as the number of poor households HH HH<sup>= ∑</sup> that have potential access divided by the total number of poor households ( S HH [∑h=1 1(S h<sup>= 1)</sup> ). PP<sup>=</sup> PP ]/PP

# **Connected (C)**

We set this variable to 1 if a household is connected to and drawing from the water network, and to zero otherwise. This is determined by the main source of drinking water, as self-reported in household surveys. We then define , as the share of households that are connected to the network among those
Rica Botswana Dominican Republic Colombia China Ecuador Thailand Peru Iraq Iran Turkey West Bank and Gaza West Bank and Gaza Belarus Malaysia Jordan Russia Albania Guatemala Paraguay Indonesia eSwatini Lesotho Zambia Tunisia Kenya Ukraine Honduras El Salvador Mongolia Bolivia India Nicaragua Moldova Egypt Tanzania Ghana Sri Lanka Comoros Cambodia Ivory Coast Uganda Burkina Faso Togo Mali Ethiopia Niger Gambia Tajikistan Guinea<br><!-- End of picture text --> Note: Pensions treated as government transfers in China, Georgia, Guinea, Mali, Mongolia, Niger, and West Bank and Gaza.

## **8.2 Poverty**

**We compare fiscal policy effects on poverty using the 2011 Purchasing Power Parity (PPP) international poverty lines.**<sup>76</sup> Estimates for the economies in each income group use the poverty line appropriate to that income level: US$5.50 per day for high income and upper middle income, US$3.20 per day for lower middle income, or US$1.90 per day for low income. We measure poverty impact by calculating the change

> 76 The International poverty lines have been updated to 2017 PPP but the results are not yet available in the CEQ database. The $5.50 in 2011 (PPP) line is the closest of the international poverty lines to the official national poverty line in the West Bank and Gaza.

53
_,_ˆ</sup><sup>_σ_</sup> _μ_<sup>2) whichcorrespondsto the methodused in ELLfor all municipalities</sup> regardless of whether or not they are in the sample. Thus, EB makes more use of the survey data for predictions corresponding to sampled areas. Note that EB methods only ensure that the area level means of the dependent variable are Empirical Best Linear Unbiased Predictors (EBLUP); it does not guarantee the same for poverty which is a non-linear parameter. Hence, the importance of replicating the welfare distribution.

One major aspect of concern for small area estimation is the estimation of noise. A statistical agency in a given country may seek to undertake a small area estimation application because the quality of the estimates derived from survey data may not be sufficiently precise or estimates may not even be possible since the area was not sampled. Hence, a big concern surrounding small area estimation is the estimation of MSEs for each area’s estimate. Estimates of noise are obtained via bootstrap replications where the model’s assumptions are exploited in order to obtain an estimated MSE (González-Manteiga et al., 2008).<sup>20</sup> This aspect is one of the biggest differences between poverty maps based on small area estimation and those based on machine learning. Statistical agencies across the globe will assess an estimate’s noise before deciding to publish it or not, thus the importance of such estimates.

The key assumption of the unit-level model is that the distribution of the explanatory variables is similar between the survey used for estimation and the census used for prediction. This assumption is generally violated if the census is outdated – which is often the case in developing countries – and in this event the application of the unit-level model will lead to similarly outdated poverty maps (Lange et al., 2018). Importantly, machine-learning approaches to poverty mapping are commonly motivated by the fact that censuses are outdated and are thus viewed as a way to produce timely poverty
and their interaction on the wheat market. The economic agents in the model include producers, consumers, and holders of stocks. Producers base their planting decisions on expected prices at harvest time. Stock holders make decisions on how much to store from one year to the next based on the price they expect next year, the price of grain this year, and the cost of storage. International trade is represented by spatial arbitrage conditions. Current policies are incorporated by identifying patterns that enable them to be represented as simple rules, defined as functions of world and domestic conditions. The model is calibrated using 2012 data on India’s wheat market and solved numerically.

Alternative stabilization policies are designed based on the maximization of a social welfare function that includes a quadratic loss function in prices. In addition to a fully optimal policy, which gives welfare-maximizing levels of storage and trade in all situations, the use of simple rules of intervention is also considered. Past research suggests that simple rules might yield a large share of the benefits of an optimal policy (Gouel 2013c) while being easier to design and to 5
**Table 2. Status of survey data (with year collected) in MENA on NSOs website and other public channels**

|**Economy/data**<br>**category**|**Establish**<br>**ment**<br>**survey**|**Price**<br>**survey**|**Consumption**<br>**survey**|**Labor**<br>**force**<br>**survey**|**Health survey**|**Populatio**<br>**n census**|**Economic**<br>**census**|**Total data**<br>**categories with**<br>**recently**<br>**collected data**<br>**per country**|
|---|---|---|---|---|---|---|---|---|
|Algeria|No|Yes (2021)|No|No|No|No|Yes (2011)|2/7|
|Bahrain|No|Yes (2021)|Yes (2014/15)|No|Yes (2018)|Yes (2020)|No|4/7|
|Djibouti|No|Yes (2021)|Yes (2017/18)|No|No|Yes (2011)|No|3/7|
|Egypt, Arab Rep.|No|Yes (2021)|Yes (2017/18)|Yes (2020)|Yes (2014) *|Yes (2017)|Yes (2017)|6/7|
|Iran, Islamic Rep.|No|Yes (2020)|Yes
educated background were more likely to die. This confirms the claims made by historians that the educated and the urban population were especially targeted. Infant mortality was also at very high levels during the period. Disability rates from landmines or other weapons are also high for males who were exposed to risk during the Khmer Rouge period.

The very high and selective mortality had a major impact on the population structure of Cambodia. Fertility and marriage rates were very low under the Khmer Rouge but rebounded immediately after the regime collapsed. Because of the shortage of eligible males, the age difference between partners tended to decline.

The Khmer Rouge period had long-term impacts on the educational attainment of the Cambodian population. The mortality rates were higher among the educated population and the education system collapsed during the period, so that individuals that were of schooling age during the interval now have a lower educational attainment than the preceding and subsequent birth cohorts.

In further research, I hope to be able use a geographical variation in the rate at which schools have been closed and reconstructed in order to exploit the exogenous shock to education levels that occurred under the Khmer Rouge without having to rely only on birth cohort effects. Indeed, this study has shown that, because of the different impact of the genocide on each birth cohort and gender, the negative shock on schooling attainment experienced by teenagers during the period of the genocide would not, as such, satisfy the conditions to be a valid instrument. I will also further investigate the fertility dynamics and the matching processes in the marriage market during and after the genocide.

Finally, this paper shows that the use of the siblings’ mortality and birth history data available in the Cambodia Demographic and Health Survey can be a very interesting source to analyze the magnitude and long-term impacts of a past mortality crisis. I am planning to use this approach for other countries that experienced a large mortality crisis in the past and for which similar data have been collected.

13
# GOVERNMENT SPENDING MULTIPLIERS IN DEVELOPING COUNTRIES: EVIDENCE FROM LENDING BY OFFICIAL CREDITORS

Aart Kraay The World Bank

## **JEL Classification Codes:** E62, O23

**Keywords:** Government spending multipliers, fiscal policy

_____________________________________________________________________________________

> 1818 H Street NW, Washington, DC 20433, USA, akraay@worldbank.org.  I am grateful to Alexandra Jarotschkin for outstanding research assistance, to Luis Serven and seminar participants at the International Monetary Fund for helpful comments, and to Ibrahim Levent, Nanasamudd Chhim, Evis Rucaj, and Shelley Lai Fu for their guidance with the Debtor Reporting System database.  Financial support from the Knowledge for Change Program of the World Bank is gratefully acknowledged.  The views expressed here are the author's, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **2. Data**

To analyze issuance activity around the world, we use comprehensive, transaction-level data on corporate bonds and syndicated loans issued in domestic and international markets between 1991 and 2014. The data come from Refinitiv’s (previously Thomson Reuters’) Security Data Corporation (SDC) Platinum, which provides information on the issuance characteristics of publicly and privately placed bonds and syndicated loans.<sup>3</sup> We focus the analysis on the non-financial corporate sector and exclude financial sector issuances (those conducted by firms with a Standard Industrial Classification or SIC code between 6,000 and 6,800).<sup>4</sup> We also exclude asset-backed issuances and public-sector issuances, comprising issuances by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. Moreover, we exclude economies considered to be offshore financial centers.<sup>5</sup> To estimate the amount raised per issuance in each market, we aggregate transactions per firm at the daily level, such that issuances with different tranches within the same market are considered single issuances. We use information on issuances of common and preferred equity to compare the size of debt markets. All reported values are in constant 2011 U.S. dollars.

The data we use for the paper consist of 56,826 listed and unlisted non-financial firms and 184,496 daily debt issuances: 84,761 bond issuances and 99,735 syndicated loan issuances (Table 1). We organize the data as an unbalanced firm-level panel of daily observations with positive issuances during 1991-2014. Days with no issuance for any given firm—that is, with no demand for credit—are not included in the sample.

> 3 SDC Platinum is one of the most widely used databases on research exploring transaction-level issuance data. Some prominent studies such as Henderson et al. (2006), Kim and Weisbach (2008), and Bruno and Shin (2017) use the same database.

>
a number of prior efforts with different names, that since 2005 have been standardized and use the same questionnaire and methodology, while maintaining their brand name in their respective regions, e.g. Regional Program on Enterprise Development (RPED) in Africa; Business Environment and Enterprise Performance (BEEPS) in Eastern Europe and Central Asia. Investment Climate Surveys were conducted prior to 2005, using largely the same questionnaire but with sample generally restricted to manufacturing firms.

> 4 The Enterprise Surveys (including their precursors World Business Environment Survey, BEEPS, RPED) have been used to show the importance of finance, corruption and property rights (Beck et al. 2005; Ayyagari, DemirgüçKunt and  Maksimovic 2008; Gatti and Fisman 2006; Cai et al 2006; Fisman and Svensson, 2007), the relationship between business environment and firm growth, (Dollar, Hallward-Driemeier and Mengistae, 2005; Haltiwanger and Schweiger 2005; Dollar et al, 2006; Hallward-Driemeier Wallsten and Xu 2006; Aterido, Hallward-Driemeier and Pages, forthcoming; Eifert et al 2008; Fernandes and Pakes 2008;); firm innovation(Almeida and Fernandes 2008; Ayyagari , Demirgüç-Kunt and Maksimovic 2010); informality (La Porta and Shleifer, 2008); labor protections (Pierre and Scarpetta 2006; Almeida and Carneiro 2009).  Two survey articles provide reviews of the literature drawing primarily on Enterprise Survey like data: Bigsten and Soderbom, 2006 and Xu 2010.

> 5 Which imply that a ―country‖ may or may not be a meaningful definition of ―place‖ for describing climates. Certainly, one cannot imagine asserting that the USA, which ranges from Maine to Florida and Alaska to Hawaii, has a meaningful ―climate‖ in the way that a small country like Jamaica might.

3
social distancing measures. EMDEs may be less well able to conduct remote learning, while large negative income shocks have also been found to increase school dropout rates in EMDEs (World Bank, 2020b). Finally, progress in improving institutional quality has stagnated in many EMDEs: measures of government effectiveness (Worldwide Governance Indicators) have not improved on average since the 1990s.

# **6 Conclusions and policy implications**

This chapter is the first comprehensive study of long-term labor productivity convergence trends to take account of the EMDE productivity growth surge since 2000. It implements a range of methodologies, including newer techniques for estimating club convergence with a sample that reaches into the post-crisis period. In doing so, it highlights a shift in the pace of productivity convergence since 2000 among a subset of EMDEs. Specifically, the chapter documents the following findings:

**Main findings.** On average since 2010, labor productivity in EMDEs was just 16 percent of the advanced economy average and, in LICs, it was just 2 percent. While there was only limited evidence of broad-based productivity convergence until 2000, subsequently on average, EMDE economies are now closing the gap with advanced economies following a broad-based surge in EMDE productivity growth. However, the pickup in productivity growth in EMDEs is unlikely to reduce productivity disparities materially for EMDEs on average—productivity gaps were declining by just 0.5 percent annually in the post-crisis period, and the pace has recently begun to decline. Even at the peak rate of convergence, for the average EMDE, to reduce the productivity gap with advanced economies by half would take nearly 140 years.

The results in this chapter suggest that weak or non-existent average rates of convergence to the productivity frontier in EMDEs in part reflects the presence of multiple attraction points for different groups of EMDEs. Over the past five decades, multiple methodologies find that countries have fallen into distinct convergence clubs in which productivity moved towards a similar long-term productivity level. In contrast to previous studies, problems associated with PPP-adjusted output levels are avoided,
the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use.

These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for reasons beyond the control of the debtor, i.e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country. Within-country risk falls significantly in peacetime. The effect is also economically meaningful - about one quarter of a standard deviation in the case of short term risks. In columns (2) and (5) we show the specification in which we add a dummy for the first five years of recovery and fragile peace. The coefficient on fragile peace is positive and of similar size in both cases. Mid-term risk is evaluated significantly higher in periods that are followed by conflict. In columns (3) and (6) we include the fitted values gained from a regression of fragility on refugees and political exclusion. Again the fitted values predict higher risk evaluations by ONDD. The estimate is not very precise but quantitatively large both for short- and mid-term evaluations.