rafmacalaba/datause-encoder-data huggingface dataset:
- <u>Under‐5 mortality rates:  The UN Child Mortality Estimates program reports 90 percent</u> uncertainty intervals for under‐5 mortality rates.  These uncertainty intervals reflect imprecision in the primary data sources (e.g. vital registries, household surveys, etc.) as well as imprecision attributable to the smoothing mechanism that is used to generate annual estimates of these rates.  For the median country in 2017, the 90 percent uncertainty interval is equal to 0.01 or a range of 1 percentage point, while the median estimate of under‐5 mortality is 2 percent.  For countries with higher estimated mortality rates, the uncertainty intervals can be larger:  for example, the 75<sup>th</sup> (90<sup>th</sup> ) percentile of uncertainty intervals are 3.2 (5.3) percentage points wide.

- <u>Harmonized learning outcomes (HLOs):  As described above in Section A2.2, the calculation of</u> HLOs involves the application of a test x subject x grade‐specific conversion factor to the country‐level average test score in its original units.  This means that there are two distinct sources of uncertainty in the HLO calculation: (a) uncertainty around the country‐level average scores in their original units, as reflected in the reported standard error around the country‐ level average , and (b) uncertainty in the calculation of the conversion factor.  The HLO database quantifies the combination of these two sources of uncertainty through bootstrapping. Specifically, 1000 random draws are taken from the distribution of the test x subject x grade‐ specific original score at the country level, assuming that the country‐level mean (across students) score is normally distributed.  Then the HLOs are calculated using the 1000 samples of original scores, and the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the resulting bootstrapped HLOs are reported as upper and lower bounds.  The HLOs used in the HCI are further aggregated to the country‐year level as described in Section A2.2.  This aggregation is done using the reported HLO estimates at the test x subject x grade level, and then repeated using the lower and upper bounds of the test x subject x grade‐level scores.  The median HLO score as used in the HCI in 2017 is 424 TIMSS‐equivalent points, and the median range of the uncertainty interval is fairly narrow at 12 points.  However, this range is larger for testing programs such as PASEC and SACMEQ which have few “doubloon” observations on which the conversion factor is based, so that uncertainty coming from variation in the conversion factor is larger.

- <u>Adult Survival Rates:  Adult survival rates (ASR) are compiled by the UN Population Division</u> using a similar process to the under‐5 mortality rates described above.  While there is uncertainty in the primary estimates of mortality as well as the process for data modeling, UNPD does not report uncertainty intervals.  Instead, uncertainty intervals produced in the IHME Global Burden of Disease modelling process for ASR are used.  The point estimates for ASR in the

50
For example, by one assessment, _Jefes_ is claimed to have accounted for the entire reduction in unemployment that was observed in the year following the crisis, which happened to roughly equal the increase in _Jefes_ registrations over the same period (INDEC, 2002; World Bank, 2003).

Such claims often rest on transparently weak foundations. Anecdotes of abuse may well attract attention but may not be a sound basis for generalization. And claims about (positive and negative) impact often ignore behavioral responses. For example, it is unlikely that a program such as _Jefes_ would not affect labor force participation choices. Then it is unlikely that all participants would have otherwise been unemployed. Similarly, one will clearly overestimate the impact on poverty if one ignores the foregone earnings of workfare participants, who are unlikely to be entirely idle in the absence of the program.

Fortunately, we are in an unusually good situation to rigorously study the impacts of the _Jefes_ program, given that large household surveys were done just before the crisis, in October 2001, and one year later, in October 2002, and that the latter survey identified _Jefes_ participants. Furthermore, one third of the October 2001 sample were followed up in the later survey round. This paper uses these survey data and the tools of non-experimental program evaluation to address the following (related) questions about the _Jefes_ program:

- Who got assistance?  Were the program’s eligibility criteria enforced?

- How did participants respond to the program, such as through labor supply and household composition?  Did participants come solely from the ranks of the unemployed?

- What was the impact on the incomes of participating households?   What share of the income loss due to the crisis was recovered through the program?

- What was the distributional impact?

- What was the impact of the program on aggregate unemployment and poverty?

4
The time series on trade volume (the ratio of exports and imports to GDP) is not consistent with the view that trade expansion was poverty reducing in the short term; Ravallion and Chen (2007) find that the correlation between changes in trade volume and changes in the log headcount index of poverty was 0.00! Nor are changes in trade volume—both current values and lagged up to two-years—significant when one includes controls for inflation, key relative prices and government spending.<sup>20</sup> Other evidence, using different data and methods, also suggests that trade reform had had relatively little impact on poverty or inequality. The household level impacts of the tariff changes from 1995 onwards (in the lead up to accession to the WTO) were studied by Chen and Ravallion (2004).<sup>21</sup> They found a positive impact of these trade reforms on mean household income, but virtually no change in aggregate inequality and only slightly lower aggregate poverty. It may well be that longer-term impacts are more positive (such as through growthpromoting access to new technologies and knowledge).

Lin and Liu (2008) argue that it is not trade expansion _per se_ that has driven poverty reduction in China but rather the fact that the overall development policies have fostered labor absorption by non-farm sectors, which is seen to play a role independently of agricultural growth and external trade flows.<sup>22</sup> They show that poverty incidence tended to be lower in provinces and dates where the manufacturing sector was relatively labor intensive (relative to the rest of the political economy); this is interpreted as the adverse effect of a “development strategy” that favors comparative advantage in labor-intensive manufacturing. They also find that poverty was lower in provinces and dates at which provincial external trade volume was higher, which was also taken to reflect differences in development strategy.<sup>23</sup> China’s initial conditions at the outset of the reform process were especially conducive to rapid poverty reduction through labor-absorbing manufacturing sector growth. This strategy

> 20 Trade volume may well be endogenous in this test, although correcting for the bias need not imply that a more important role for trade, since trade volume may well be negatively correlated with the residuals; other (omitted) growth-promoting policies simultaneously increased trade and reduced (absolute) poverty.

> 21 The price and wage changes were those estimated by Ianchovichina and Martin (2004) using a Computable General Equilibrium model.

> 22 This echoes longstanding arguments on the importance of exploiting the comparative advantage of lowincome countries in labor-intensive production; see, for example, World Bank (1990) and Schiff and Valdes (1992).

> 23 Though note that much of this is probably inter-provincial trade, not foreign trade.  The key identifying assumption made by Lin and Liu is that being a coastal province only affects poverty via its effect on trade volume. It could well be that the trade volume variable in this study (when instrumented by the coastal province dummy) is

8
# **Global Liquidity and External Bond Issuance in Emerging Markets and Developing Economies**

Erik Feyen* Swati Ghosh Katie Kibuuka Subika Farazi

**World Bank Washington, D.C.**

Key words: Global Liquidity, Bonds, Primary Market, Capital Flows, International Finance. JEL Classification: F21, F32, F36, G12, G15.

* Corresponding author: Erik Feyen is a Lead Financial Sector Economist in the Finance and Markets Global Practice (efeijen@worldbank.org). Swati Ghosh is an Adviser in the Macro and Fiscal Management Global Practice. Katie Kibuuka and Subika Farazi are both Financial Sector Specialists in the Finance and Markets Global Practice. We thank Pierre-Laurent Chatain, Stijn Claessens, Mario Guadamillas, Roberto Rocha, Hyun Shin, and participants of the Analytical Group on Vulnerabilities of the Financial Stability Board for comments and Diego Sourrouille for excellent research assistance.
We calculate the transport price, _P,jk,_ as the share of liner transport charges in import values for good k (at the 6 digit HS aggregation), multiplied by the unit value of imports. The U.S. Department of Transportation defines transport charges as all freight, insurance and other charges (excluding import duties) incurred in bringing the merchandise from alongside the carrier at the port of export and placing it alongside the carrier at the first US port of entry.<sup>27</sup> However, actual data reported may include charges for port services and inland transportation.<sup>28</sup> To reduce the potential bias resulting from differences in inland transportation costs, we exclude observations for which the origin of the import is different from the country of the port of shipment (e.g., landlocked countries) as well as all in-transit shipments.<sup>2 9</sup> Additional information on the construction and sources of all variables is provided in the data appendix.

Table I presents an overview of our estimation dataset. It covers all U.S. imports carried by liners from the 59 countries for which we could find information on maritime policies. Data refer to 1998. Liner imports account for around 65 percent of the total value of maritime imports, the remaining 35 percent being carried by tramp services.<sup>30</sup> About half of all U.S. imports (including all modes of transport-maritime, air and road) are carried by liners.

# **IV.** Econometric Estimates

# _Estimation methodology_

We begin with an ordinary least squares estimation of equation (5) over the entire data set. The error term _eUk_ is assumed to be independently distributed across exporting countries, but we _27_ If insurance costs are not closely correlated with transport charges, there is the possibility that our transport price variable is distorted. However, this should at least partially be remedied by the inclusion of product fixed effects, as differences in insurance costs are likely to be greatest across products.

> **28** According to e-mail communication with an official from the U.S. Department of Transportation.

> 29 Note that we do not exclude the trade originating in third countries and in-transit traffic when calculating total import values qij.

> **_30_** However, if we exclude U.S. oil imports (HS category 27) this share rises to 70% and liner transport becomes relatively more important for developing countries.

_15_
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transportation, and communication. Of course, all capital expenditures on these categories do not necessarily represent “investment”, as conventionally defined; some components of these outlays may be related to maintenance operations. However, we did not have sufficient information to refine the GFS estimates.

Figure 2 shows the evolution of total capital expenditure in proportion of GDP, and capital expenditure on infrastructure both as a share of total public expenditure (including current spending) and as a share of total capital expenditure only. The figure shows a declining trend in all three ratios since the early 1980s for Jordan and the late 1980s for Tunisia. For all three countries, the behavior of the ratio of total capital expenditure to GDP is consistent with the evolution of total public investment displayed in Figure 1. As a proportion of total capital outlays, the share of capital expenditure on infrastructure has averaged 30 percent for Jordan and almost 40 percent for Tunisia in recent years. For Egypt, by contrast, there are large fluctuations during the past 40 years, but no clear trends. In recent years, the share of capital expenditure on infrastructure in total capital outlays has fluctuated between 45 and 50 percent.

Using our flow data on infrastructure spending, we calculated the stock of public capital in infrastructure, using the perpetual inventory method and the GDP deflator to estimate real values in constant local prices. This procedure is, of course, subject to limitations.<sup>9</sup> Differences in the efficiency of the public sector and the price of infrastructure capital, in particular, means that the same level of capital spending on infrastructure may yield very different results across countries. We account for quality _ex post_ by using various indicators, as discussed below. More generally, the use of perpetual inventory methods to calculate stocks from expenditure flows may introduce systematic errors in stock estimates. At the same time, however, it should be noted that adequate price variables for public infrastructure are difficult to

> 9See Hulten (1990) for a detailed discussion of the conceptual and measurement problems involved in constructing capital series. To calculate the public capital stock, we used a uniform depreciation rate of 2.5 percent.  By comparison, Nehru and Dhaneswar (1993) used a uniform rate of 4 percent, whereas Larson et al. (2000) use alternative values of 4 and 6 percent, to estimate aggregate stocks of capital. However, sensitivity analysis showed that our empirical results are not unduly sensitive to our particular choice.

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NEEs and the more advanced Southeast Asian economies a more efficient high-end manufacturing in China implies more competition in high-end manufactures which comprise a large share of their exports. Consequently, these countries are not as well off as they are in the case of a productivity increase in services (Figure 2).

Finally, a comparison of these two cases with the case of a uniform productivity growth" suggests that Vietnam and Indonesia will benefit the most if China's economy becomes more efficient in the production of high-end manufactures. In contrast, the most favorable outcome for the more advanced Southeast Asian economies and the NIEs is for China to continue to specialize in labor-intensive products.

**Figure 1. Output changes in China.**

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*Source: Authors' simulations with GTAP-Dyn. 'Standard' denotes results from the standard policy case with no productivity gains; 'Services' denotes the case of increased productivity in China's services; 'Manufactures' denotes the case of increased productivity in China's high-end manufactures; 'All sectors' denotes uniform productivity increase in China.

> **53** The comparison allows us to evaluate the effect of productivity growth in the absence of an income effect and therefore to capture the 'pure' effect of a sector-specific productivity growth on the industry structure.

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**Table 1. Description of Variables and Data Sources**

|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|Capital<br>share<br>(�)|In the production function of the<br>modern economy, the capital<br>share measures the share of<br>national income that accrues to<br>owners of capital.|We use a constant estimate of 0.5. This corresponds<br>roughly to the median value of country‐specific<br>estimates we derived from labor shares presented<br>in the Penn World Tables (averaged from 1990‐<br>2012). These country‐specific estimates are used<br>directly in the appendix where, if a country is<br>missing data, we use ��0.5. All other variables<br>used in the appendix are derived using this<br>alternative country‐specific capital share.|
|Subjective rate<br>of<br>time<br>preference (�)|The marginal rate of substitution<br>between<br>present<br>and<br>future<br>consumption.|Using the equation that determines the rate of<br>capital growth in the modern economy and the<br>average growth rate of capital from 2000‐2012, we<br>|
|Capital<br>depreciation<br>rate (�)|The rate at which the economic<br>value<br>of<br>the<br>capital<br>stock<br>decreases over time.|obtain the implicit depreciation rate and the<br>subjective rate of time preference. We set a<br>minimum value of 1 percent.|
|Capital<br>stock<br>(K)|Goods<br>or<br>assets<br>used<br>for<br>production<br>in<br>the<br>modern<br>economy.|We develop historical estimates using Gross Capital<br>Formation in 2005 US dollars from WDI (1960‐<br>present), extrapolating using Investment from<br>Penn World Tables (1950‐2011) for past years in<br>which it is not available. For the initial conditions,<br>we use an average from 2008‐2012 and update<br>future years using the growth rate derived from<br>solutions of the model.|
|Total<br>factor<br>productivity of<br>the<br>formal<br>sector (A)|The total factor productivity of<br>firms operating in the formal<br>sector, using capital and labor.|We develop country‐specific estimates using the<br>production function of the modern economy and<br>our estimates of capital and labor in the modern<br>economy. For the initial conditions, we use an<br>average from 2008‐2012.|
|Growth<br>of<br>Total<br>factor<br>productivity of<br>the<br>formal<br>sector (A)|Growth of total factor productivity<br>of firms operating in the formal<br>sector, using capital and labor.|We calculate an annual log growth rate of the<br>historical estimates of A and take an average from<br>2000‐2012. We set a minimum value of 0.|
|Growth of the<br>Labor force<br>(growth of LF+<br>LI+ LR)|Growth of the total labor force (i.e.<br>in the modern and rudimentary<br>economy) by country.|We use estimates and projections of the size of the<br>labor force by country from the ILO (available until<br>2030) to find an annual growth rate. For the initial<br>year, we use an average of estimates of the size of|

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The ultimate impact on capital flows of unexpected U.S. monetary policy tightening (beyond one warranted by strengthening U.S. activity) would also depend on the reaction of long-term yields in other major advanced economies, and in particular how market participants reassess monetary policy expectations in these economies. Effects would be amplified if it coincided with synchronized increases in long term yields across G4 economies (United States, Euro Area, Japan, and the United Kingdom), but would be dampened if long term yields only increase in the United States.

A 100 basis point increase in long-term U.S. bond yields could reduce capital flows to EMDEs by 20-45 percent, with the upper bound of this range reflecting simultaneous increases in long term yields across G4 economies. These results are derived from a vector autoregression model including capital flows to EMDEs (foreign direct investment, portfolio investment, and other investment as a share of GDP), quarterly real GDP growth in EMDEs and G4 countries, real G4 short-term interest rates, G4 term spread, and the VIX index of implied volatility of U.S. S&P 500 options (Arteta et al. 2015).

# **4.4 Fiscal policy spillovers**

Changes in U.S. fiscal policy could generate international spillovers by affecting U.S. demand for imports from abroad, by causing exchange rate movements or by influencing international borrowing conditions. Simulations using the Federal Reserve Board’s model (FRB/US) suggest that a fiscal stimulus of 1 percent of GDP could be expected to raise U.S. growth by between 0.7 and 1.5 percent after two years (World Bank 2017; Brayton, Laubach and Reifschneider 2014). However, the effectiveness of fiscal stimulus in lifting U.S. growth over the short and medium run depends critically on the circumstances of its implementation.

Fiscal multipliers—the additional output generated by an additional U.S. dollar of government spending or tax cut—depend on the presence of economic slack, the reaction of monetary policy, and the nature of the fiscal measures (Laforte and Roberts 2014; Brayton, Laubach, and Reifschneider 2014; Whalen and Reichling 2015). In particular, fiscal stimulus measures could be expected to have different effects if they take the form of tax cuts or measures to bolster government spending and infrastructure investment.

The short-term fiscal multiplier associated with corporate tax cuts is generally estimated to be below one, although considerable uncertainty surrounds these estimates (Chahrour, Schmitt-Grohé, and Uribe 2012; Ljungqvist and Smolyansky 2016; Whalen and Reichling 2015). Regarding personal income tax cuts, empirical studies find that fiscal multipliers vary considerably, from 0.3 and 1.5 (Whalen and Reichling 2015). The effect on growth depends 18