rafmacalaba/datause-encoder-data huggingface dataset:
# **4.4 Diagnostic tests**

The country-specific VARX*, Equation (4), includes the contemporaneous values of **x**<sup>_∗_</sup> _it_ and **d** _it_ , on its right hand side. We investigate two issues relating to them in this subsection. One is about the weak dependence of the idiosyncratic shocks, which is a key assumption for the estimation of Equation (4). The second issue concerns the contemporaneous impact of foreign variables on the domestic counterparts.

In the literature of GVAR, it is a common practice that the country-specific VARX* models, Equation (4), i.e. the equations of **x** _it_ , are estimated on a country-by-country basis. As listed in Table 1, the commodity prices **d** _it_ are part of **x** US _,t_ , even though they are treated as exogenous in the small-open economies. Thus, Equation (4) specifies dynamics of the domestic variables **x** _it_ as well as the commodity prices **d** _it_ . On the other hand, the dynamics of **x**<sup>_∗_</sup> _it_<sup>isnotestimated,butdefinedbyEquation(3).Practically,thisenablesustoreduce</sup> the number of parameters significantly, and make us construct the world model.

Three justifications for this estimation procedure are given by Pesaran, Schuermann, and Weiner (2004). They are: 1) stability of the system; 2) smallness of weights _ωij_ ( _t_ ); and 3) the weak dependence of the idiosyncratic shocks.<sup>14</sup> Here, we examine the weak dependence of the idiosyncratic shocks. Table 6 reports the average pair-wise cross-section correlations for the levels and first differences of **x** _it_ , as well as the associated VARX* residuals.

Generally speaking, the average pair-wise cross-section correlations are high for the “Levels”, but they drop drastically as differenced. The correlations further decline as their dynamics are modeled by VARX*. A closer look reveals that the VARX* model with the contemporaneous “star” variables (Type-2) generally yields much weaker dependence of idiosyncratic shocks than that without the contemporaneous “star” variables (Type-1). This is consistent with the idea that the contemporaneous “star” variables function as proxies for the common global factors. Thus, once country-specific models are formulated as being

> 14The stability of the system is numerically confirmed when the impulse response analysis is examined in the latter section. The smallness of weights calculated from the trade flow data is reported elsewhere, thus we do not repeat it. Concerning the weak dependence, see Appendix for additional discussion.

20
Table 4: Effect of Climate Change Scenario on Crop Choice in Latin America

||Maize|Potato|Rice|Soybean|Squash|Wheat|Fruits|
|---|---|---|---|---|---|---|---|
|Baseline|19.5%|6.8%|4.8%|7.9%|8.0%|14.4%|38.6%|
||||20|20||||
|CCC|-0.7%|-1.4%|1.3%|-0.5%|1.9%|-0.7%|0.2%|
|CCSR|-1.5%|-1.8%|1.7%|-0.4%|2.2%|-0.2%|0.0%|
|PCM|1.9%|4.9%|0.0%|-1.2%|-2.2%|-4.9%|1.4%|
||||20|60||||
|CCC|-1.2%|-0.8%|0.3%|-1.1%|4.3%|-2.2%|0.7%|
|CCSR|-0.3%|-2.0%|0.3%|0.3%|3.2%|-3.1%|1.6%|
|PCM|2.2%|3.8%|0.9%|-1.1%|-1.7%|-6.3%|2.1%|
||||21|00||||
|CCC|-3.3%|2.2%|1.1%|-3.3%|9.7%|-5.0%|-1.3%|
|CCSR|-0.7%|-2.5%|0.1%|-0.6%|5.5%|-3.0%|1.1%|
|PCM|3.1%|2.7%|-0.1%|-1.2%|-1.3%|-6.5%|3.2%|

22
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single company that controls the provision of inputs and other services to farmers, and operates as the sole buyer of the entire cotton crop. With the exception of Burkina Faso, national governments are majority shareholders of these companies. The lack of transparency under these managed monopoly systems creates a multitude of opportunities for rent seeking and the mismanagement of resources, generally at the cost of farmers, the national budget, and the economy as a whole.<sup>7</sup> In particular, it severely limits incentives to minimize costs, and it stifles entrepreneurial decision-making at all levels of the supply chain.

Essentially, the viability of national sectors under the current system is based on their ability to (a) tax producers and accumulate profits in times of high export prices, and (b) rely on budgetary support from national governments in times of low international prices. This strategy has not worked well in many countries owing to poor management of the stabilization fund by the cotton monopsonists. This strategy needs to be changed in an era of 7 Various sector diagnostic studies and technical audits of cotton companies that have been carried out in many countries over the years have repeatedly documented a considerable level of rent seeking and relatively high operating costs in the ginning sector. The latest examples are the technical audit of CMDT in Mali in 2000 and the survey of ginning costs in Benin in 2001.
poverty falls in the wage labor strata as well as for diversified households. However, agricultural profits fall, since producer costs rise faster than revenues. Thus agricultural poverty rises slightly in the short run. In the terminology of Ravallion and Lokshin (2004), horizontal impacts seem to dominate the vertical ones when inter-sectoral factor mobility is limited.

In the full mobility factor market closure, the _within stratum_ earnings differences are much greater. Now, the welfare of the low-income self-employed workers hinges largely on changes in unskilled wages, regardless of their sector of employment, while the fate of the wealthy households depends on skilled wages and returns to capital and land. (In this “long run” factor market closure, agricultural land absorbs the reduction in agricultural profits.) With unskilled wages rising, this results in very similar rates of poverty reduction across all strata. So with full factor mobility, the horizontal inequality tends to be eliminated, while vertical inequality is exacerbated. This is a hypothesis that warrants further examination for other countries and other types of trade reforms. As with the Bourguignon et al. (2003) study, this work also highlights a limitation of those studies that simply assume a common income shift for all households within a given “representative group.”

# **VI.  Estimating the Linkages between Trade, Growth and Poverty**

In the long run, there is little doubt that the only way to obtain large reductions in poverty is through economic growth. Thus, in the context of any discussion of trade and poverty, it is natural to look for a linkage between trade liberalization and growth, which in turn might give rise to a long run reduction in poverty rates (see also Winters et al., 2004, for an extensive discussion of the tradegrowth-poverty linkage). In the last ten years, much has been written on the connection between trade and growth – both from a theoretical and an empirical perspective. The empirical work has largely relied on cross-country regression analysis, as exemplified by Dollar and Kraay’s (2001) paper on the poverty impacts of trade liberalization.

These authors first categorize developing countries as either globalizers or non-globalizers based on changes in trade volumes and tariff rates since 1980. They then carry out case study as well as statistical analysis. Looking at anecdotal evidence on poverty, including time-series Gini coefficients and income growth rates for average households versus the poorest quintile, they find no general trend in inequality among countries classified as globalizers. Globalizers, however, tend to 22
# **Annex 4 – Alternative specifications of main results**

# **_Table A4.4a Relationship between country-level characteristics and IEG outcome rating, ordered probit_**

|||**De**|**pendent varia**|**ble: IEG 1-6**|**outcome rati**|**ng**||
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|CPIA rating at|0.167**||0.437***||||0.425***|
|approval year|(0.075)||(0.080)||||(0.094)|
|CPIA change over||0.343***|0.630***||||0.580***|
|project lifetime||(0.085)|(0.106)||||(0.117)|
|Log(GDPpc PPP$||||-0.074||-0.052|-0.115*|
|at approval year)||||(0.060)||(0.059)|(0.056)|
|Real GDPpc growth|||||1.856***|1.905**|1.486|
|over project lifetime|||||(0.712)|(0.700)|(0.926)|
|Observations|2,790|2,767|2,767|2,735|2,665|2,580|2,516|
|Pseudo R-squared|0.070|0.073|0.081|0.069|0.070|0.070|0.079|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using Ordered Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project and IEG evaluator fixed effects.

# **_Table A4.4b Relationship between country-level characteristics and binary IEG outcome rating,_** **_<u>probit</u>_**

||**Depende**<br>|**nt variable: Bi**<br>|**nary IEG outc**<br>|**ome rating**<br>|**(Satisfactory**<br>|**=1, Non-satis**<br>|**factory=0)**<br>|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|CPIA rating at|0.190**||0.482***||||0.422***|
|approval year|(0.083)||(0.099)||||(0.109)|
|CPIA change over||0.355***|0.673***||||0.593***|
|project lifetime||(0.102)|(0.126)||||(0.135)|
|Log(GDPpc PPP$||||-0.041||-0.011|-0.057|
|at approval year)||||(0.060)||(0.061)|(0.060)|
|Real GDPpc growth|||||2.346**|2.452**|2.363**|
|over project lifetime|||||(1.195)|(1.209)|(1.110)|
|Observations|2,593|2,572|2,572|2,528|2,475|2,386|2,331|
|Pseudo R-squared|0.082|0.086|0.099|0.080|0.084|0.085|0.101|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project and IEG evaluator fixed effects.

48
**Figure 6: Portion of callable bonds among SLBs and green and conventional corporate and municipal bonds**

_Source: Figures on corporate non-green callable bonds from Dias (2021)._ While the sheer difference in these proportions may suggest that call options are more valuable for issuers in SLBs than other bonds, it is important to note that not all call options are useful in avoiding penalties. In particular, we draw attention to two types of such call options: ‘cleanup’ call options and make-whole call options. The former refers to call options which can only be exercised within three months before maturity. These are typically used by issuers to reduce administrative expenses. These cannot be used to minimize the penalty amount (at least substantially) given they can only be exercised shortly before maturity.

Make-whole call options require the issuer to pay the bondholders not only the call price upon exercise but also the present value of all remaining coupon payments from call date to maturity. In essence, the issuer is responsible for its original liability, so the bondholder does not suffer a financial loss if the bond is redeemed early. Make-whole call options are rarely exercised in practice due to the high cost of doing so for the issuer. The high cost makes make-whole call options an inefficient tool to minimize penalty payouts. This is especially true as the makewhole premium the issuer has to pay the bondholder is typically higher if targets are unmet (as the higher coupon rate is used to determine future coupon payments), causing make-whole options for SLBs to be even costlier.

Hence, our analysis excludes both clean-up call options and make-whole call options when examining if SLB issuers exploit call option provisions to minimize potential penalties. This leaves only 40 (19 percent) of SLBs that have a call option that could be useful to potentially minimize step-up penalties; our subsequent analysis in this subsection focuses on these ‘penalty-minimizing’ call options. SLBs with such penalty-minimizing call options tend to be more likely to be speculative grade, shorter in maturity and have a smaller issue size than SLBs 15
-4-

poverty measure with others, such as nutrition, life expectancy, under 5 mortality, and school enrollment rates." The Human Development Report, on the other hand, goes a step forward and attempts to combine income and two other dimensions of the standard of living -- literacy and life expectancy -- into a unidimensional measure of the standard of living along which countries can be ranked (neglecting for the moment the distribution of this measure within each country). The result of this attempt is the "Human Development Index" (HDI), which has attracted considerable attention.

The HDI is defined on the basis of three indicators of well being at the national level -- logarithm of real _GDP_ per capita (X1), adult literacy rate (X2) and life expectancy at birth (X3). From the values of these indicators for countries, the maximum and minimum value for each indicator is identified (for example, for life expectancy the maximum is 78.4 years while the minimum is 41.8 years; for adult literacy the maximum is (almost) 100% while the minimum is 12.3%). On this basis, the deprivation for the ith country along the ith dimension is defined as The normalization used in the formula ensures that DJ lies between 0 and 1. The "deprivation index" for the jth country is defineci as a simple average of DI,, **D2j** and D3J. In other words,
Financial market volatility does not necessarily coincide with policy uncertainty, yet both appear to be detrimental to investment. Policy uncertainty is measured by the Economic Policy Uncertainty Index (EPU), a news-based measure of policy uncertainty (Baker, Bloom and Davies 2015). A sustained 10 percent increase in the index of U.S. EPU could, after one year, reduce U.S. output growth by about 0.15 percentage point, EMDE output growth by 0.2 percentage point, and EMDE investment growth by 0.6 percentage point (Figure 10). Similar to the results presented above, these estimates are based on vector autoregression models including the U.S. EPU, equity prices, bond yields, and GDP and investment growth in the respective economies for 18 EMDEs for 1998Q1-2016Q2 (World Bank 2017).

# **5 Spillovers to the United States from the global economy**

Important as the U.S. economy is to the global economy, the U.S. economy is also affected by the strength of its linkages with the rest of the world (Figure 11). Moreover, global economic and financial developments play an important role in driving activity and financial markets in the United States.

## **5.1 Global trade**

In 2015, trade accounted for more than one-quarter of U.S. GDP (28 percent) and manufacturing output for slightly more than one-fifth (22 percent) of GDP. Most U.S. goods exports are manufacturing goods (87 percent of U.S. goods exports), followed by agricultural products (4 percent) and oil, gas and minerals (2 percent). The most prominent goods export categories are petroleum oils (other than crude), motor vehicles and their parts, and electronic parts. Most U.S. goods and services exports are shipped to Canada, the EU, Mexico, and China, which altogether account for more than 60 percent of total U.S. exports. Exportintensive industries in the United States have tended to be more productive and offered higher wages than non-export-intensive industries: during 1989-2009, on average, their total factor productivity growth was 51 percent higher; labor productivity was 10 percent higher; and wages were 17 percent higher (Council of Economic Advisors 2015).

## **5.2 Global value chain participation**

Many U.S. companies are deeply integrated into global supply chains. As a result, U.S. exports themselves are often an input into other countries’ production for exports ("forward participation"). One-quarter of U.S. exports represents U.S. value added embodied in other countries’ exports. Such forward participation is particularly high in chemicals, business services, and electronics, and with China, Canada, and Mexico. "Backward participation" is 21
Policy Research Working Paper 9626

# **Abstract**

This paper disaggregates human development indicators across disability status to assess the situation of persons and households with disabilities. The paper uses 24 censuses and general household surveys from 21 low- and middle-income countries. Disability status is measured through self-reports of functional difficulties (for example, seeing or hearing). There are several findings of interest. First, disability is not rare in low- and middle-income countries. The median prevalence stands at 10 percent among adults ages 15 and older, and at 23 percent among households. There are consistent inequalities associated with disability and, in particular, with respect to educational attainment, work outcomes, poverty, food security, exposure to shocks, living conditions, and assets. At the same time, not all persons with functional difficulties experience deprivations. There is a gradient in inequalities associated with the degree of functional difficulty: persons with at least a lot of difficulty tend to be worse off than persons with some difficulty, who themselves tend to be worse off than persons with no difficulty. The results in this paper on the prevalence of functional difficulties and their association with socioeconomic deprivations show that disability should be central to human development policy, data, and research. More work is needed to curb the inequalities associated with disability.

This paper is a product of the Development Data Group, Development Economics. 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 mitra@fordham.edu.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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37
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