rafmacalaba/datause-encoder-data huggingface dataset:
lessons have not been learned in spite of a growing policy dialogue on ways to manage debt and run fiscal policy in a sustainable manner (see Figure 3).

**Figure 3:** Number and size of defaults

<!-- Start of picture text --> 100<br>100<br>75<br>75 No Default<br>50<br>Default<br>25<br>50<br>0<br>1990 2000 2010 1990 2000 2010<br>Count<br>Value of Default (%GDP)<br><!-- End of picture text -->

**_Source: Own calculations using 2020 version of Beers and Mavalwalla (2017)_**

Consequently, the work program for addressing public debt vulnerabilities in emerging and low-income countries remains more relevant than ever. The objective is to minimize the economic and fiscal costs associated with shocks and to increase debt data transparency and the efficiency and sustainability of fiscal and debt policies. Four pillars are needed to achieve this: 1) improving debt analysis and early warnings systems; 2) enhancing debt transparency; 3) strengthening debt management capacity; and 4) reviewing debt policies. Developing an early-warning framework to monitor public debt vulnerabilities and fiscal risks for market-access countries, would go a long way to bolster this agenda.<sup>2</sup> That includes: i) developing a methodological framework for assessing debt vulnerabilities; ii) developing a tool to support the analysis; and iii) establishing a consultation process and internal mechanisms to discuss action plans WB teams could propose for countries with elevated debt vulnerabilities.

This paper presents the methodological outline of a framework for monitoring debt vulnerabilities in market-access economies. It seeks to complement existing World Bank debt vulnerability assessments, including the joint Bank-Fund Debt Sustainability Framework.

The paper produces four indicators of debt vulnerability, which are used for predicting debt default. The four indicators capture overlapping-but-different angles of vulnerabil2MACs or market access countries are defined as countries that are not eligible for the Poverty Reduction and Growth Trust (PRGT). Emerging market countries (EMs) are a subset of MACs. MACs thus cover all advanced economies and the majority of emerging markets and may include LICs that have significant access to market financing. The classifications of advanced economies and emerging markets are defined by the IMF’s World Economic Outlook.

3
non-socialist world consumption of primary aluminum (source: Metallgesellschaft, _Metal Statistics)._ The lagged expectation of current consumption, used to define the consumption innovation InCONSt - Et.linCONSt, was generated by recursive estimation of the equation

# InCONSt = 60 + 6t

This equation may be rationalized by noting that the price expected by producers in period t-1 to hold in period t will be given by the intersection of the expected demand curve with the known and only slowly changing industry supply curve. However, the price will differ from this expected price as actual demand exceeds or falls short of expected demand since the short run supply response of the primary sector if very low and the excess or deficit is met by changes in stocks. Unfortunately we do not have access to a long comprehensive stock series and cumulation of excess supplies is unreliable since it also cumulates systematic inaccuracies in the data. That stocks are important is however indicated by the significance of the lagged production consumption ratio since

# tn(PRODt/CONSt) = ASTOCKtICONSt

If stocks are high, then the expected price will be lower than that given by the intersection of the expected demand and supply curves, and _vice versa_ if stocks are low. However, further lags of this variable are insignificant. We suspect that these stocks are in general held by producers rather than speculators.

96. There is no clear evidence that a nonlinear framework is inadequate here. Replacement of equation (68) by a spline function defined analagously with equation (67) by splitting both the consumption innovation and lagged production-consumption ratios at their mean values allowed acceptance of the linear equation with an F valaue of F2 ,2 2 = 57
**Table 5:** Sex of the first two-born children, realized (and desired) fertility and the use of contraception

|dep. var:|# children ever|=1 wants another child|=1 currently using contrac.|
|---|---|---|---|
||(1)|(2)|(3)|
|girl,girl|0.127***|0.051***|-0.015**|
||(0.039)|(0.015)|(0.008)|
|one boy one girl|0.038|-0.003|-0.002|
||(0.032)|(0.012)|(0.007)|
|Observations|15065|14975|15027|
|R-squared (or Pseudo)|0.655|0.262|0.230|

_Notes._ OLS estimates in column (1). Probit estimates in columns (2)-(3), marginal effects reported. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights. Includes all the controls as in column (6) of table 3 as well as a dummy for whether the second child is dead and its interaction with second-born girl. The # children ever born is included in columns (2)-(3).<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

Altogether, these results suggest that women with daughters _want_ to have more children to a greater degree than women with sons, and they seem to intentionally continue bearing children until they have the desired number of sons.

# **5.2 Birth spacing results**

I turn now to the impact of the sex composition of earlier-born children on the length of the interval between births. Columns (1) to (3) of table 6 show that the birth of a girl has no effect on the birth interval when considering all births. This is probably due to the fact that in a high fertility context the effect on spacing might become negative (and significant) only after the birth of several girls. The death of a child has a negative and significant effect on birth spacing as mothers probably attempt to conceive again to replace the lost child. It is interesting to note that the omission of the indicators for mortality leads to an overestimation of the effect of the birth of a girl. Consistent with the results found for fertility, women with male children appear to wait less on average than those with daughters due to the higher mortality of sons and the urge to replace the dead child. Columns (4) to (6) show that the birth of each successive girl implies a reduction in the interval for women with a first-born girl. However, this effect is not significant. Columns (7) to (9) focus on the subsample of women who had at least four children, among which the first two are girls. Column (8) shows that, on average, mothers wait 1.32 months less after the birth of a girl than a boy (this effect is significant at the 5% level). The estimates obtained using OLS are similar (column 9). Finally, columns (10) to (12) consider the subsample of women with 5 or more children. Interestingly, for women with girls as her first three born, the length of the interval after the birth of a girl is reduced relative to that of a boy by 3.25 months and this effect is significant at the 1% level.

Table 7 shows additional results for the subsamples of women with a first-born boy (column 2), first and second-born boys (column 4), three first-born boys (column 7), and mixed gender 21
**Table 8: Current Capability Levels, SACU Countries**<sup>12</sup>

|_Ctry_|Proximity.<br>to Markets|Nat.<br>ural<br>Capital|Human<br>Capital|Phys.<br>Capital|Inst. Capital|Logistics|Wage Comp.|Mkt<br>Access|Access<br>to<br>Inputs|
|---|---|---|---|---|---|---|---|---|---|
||GDP‐<br>weighted.<br>distance<br>index<br>(0‐far to<br>1‐near)|$ billion|Years of<br>schooling|$US<br>per<br>capita|Rating from<br>‐2.5 to 2.5|Rating<br>1 to 5|Min. wage in<br>$US<sup>13</sup>|Index<br>(0<br>to<br>1)|Index<br>(0 to<br>1)|
|_BWA_|0.42|30|9.6|19.70|0.66|2.49|105.0|0.89|0.99|
|_LSO_|0.38|2.5|6.6|3.98|‐0.30|2.37|104.1|0.90|0.96|
|_NAM_|0.44|14|6.0|9.90|0.23|2.66|‐‐|0.91|0.99|
|_SWZ_|0.40|13|7.6|4.61|‐0.50|‐‐|107.5|0.68|0.98|
|_ZAF_|0.35|790|8.6|13.61|0.10|3.43|646.4|0.94|0.95|

Source: See Table 1 for data sources How do these underlying capabilities for SACU countries stack up globally? Figure 2 shows the standardized capability levels compared to a region of major importance in cross‐border value chains, ASEAN. South Africa is analyzed separately from the rest of SACU given its size and level of industrialization. The figures are standardized so that capabilities can be compared to one another and read in terms of standard deviations from the average country. Then, the standardized figures are compared in the chart. A bar extending to the right of zero denotes an advantage relative to the ASEAN average and a bar extending to the left of zero denotes a deficit. South Africa has several advantages relative to ASEAN: logistics, institutional capital, natural capital, human capital, market access and access to inputs. South Africa lags behind ASEAN in physical capital per capita and wage competitiveness. Although Botswana, Namibia, Lesotho and Swaziland fare worse than ASEAN on most of the nine capabilities, they are not without some advantages: access to inputs, institutional capital and wages.

> 12 Two measurements are missing in the original databases and these gaps were filled in the following way. Namibia’s wage competitiveness is assumed to be no better than Swaziland, and Swaziland’s logistics are assumed to be the same as Lesotho’s. 13 For a 19‐year old worker or an apprentice.

15
# **1. Current Debt Management System**

||**Yes**|**No**|**Total**|
|---|---|---|---|
|**Do you currently use a software to record your debt**<br>**management operations?**|30|2|32|
|**Was the software developed internally? **|9|23|32|
||**< 1 year**|**1 – 5 years**|**> 5 years**|
|**How long haveyou been using your current DMS?**|**2**|**6**|**24**|
||**Yes**|**No**|**Partially**|
|**Does the system accommodate your transaction flow**<br>**(e.g. recording, payment, reporting)? **|28|0|4|
|**Assess the capacity of your DMS to process the below**<br>**debt instrumentspertaining toyour needs**|**Fully**<br>**Able**|**Not**<br>**Able**<br>**Part.**<br>**Able**|<br> <br>**No**<br>**Resp. **|
|Bills|28|2<br>2|0|
|Bonds(Bullet)|30|0<br>2|0|
|Bonds(Amortizing)|30|0<br>2|0|
|Variable Interest Rate Products|27|1<br>3|1|
|Inflation-linked Instruments|18|5<br>6|3|
|Project finance loans|26|1<br>3|2|
|Guarantees|21|6<br>4|1|
|On-lent Debt|23|6<br>2|1|
|Receivables(from on-lending, defaultedguarantees)|18|8<br>4|2|
|Fees|24|2<br>5|1|
|Interest Rate Swaps|15|8<br>7|2|
|Cross CurrencySwaps|12|7<br>10|3|
|**How many staff use the DMS?**|Average|Maximum|Minimum|
|**Front-Office Users**|7|50|0|
|**Middle-Office Users**|5|27|1|
|**Back-Office Users**|11|45|1|
|**2.**<br>**Borrowing Operations(Front-Office)**||||
|**Does your system:**|**Yes**|**No**|**No Resp.**|
|Record transactions not yet committed (e.g. loan<br>pipeline)?|21|11|0|
|Integrateyour auctionplatform to the debt database?|11|20|1|
|Interface with external sources such as electronic trading<br>platforms to record a transaction?|9|23|0|
|Integrate with market data providers (e.g. Bloomberg,<br>Reuters,Central Bank,Eurostat,etc.)?|10|22|0|
|Check whether a transaction violates your compliance<br>and limit requirements (e.g. debt strategy guidelines or<br>budget limitations)?|9|23|0|

30
percent. During the same time, the share of remittances in household consumption and GDP grew by 14 and 15 percent per year, respectively.

_Figure 1 Evolution of remittances in Jamaica_ <!-- Start of picture text --> 25<br>Remittances as a share of GDP<br>Remittances as a share of household final consumption<br>20<br>15<br>10<br>5<br>0<br> 1976 1980 1984 1988 1992 1996 2000<br><!-- End of picture text --> There is little agreement in the literature on the economy-wide effects of increased remittance inflows. Connell and Brown (2005) argue that while “[some studies show that] consumption is of considerable importance, they also show that higher levels of consumption are a major welfare gain and contribute to meeting basic needs that are otherwise sometimes poorly satisfied. They also show that remittances have been increasingly important for both investment and savings [...] Remittances are also invested in economic activities, in human capital, and in the well being of others.” Focusing more specifically on the relationship between labor supply and remittances, there is very little scientific evidence to substantiate the common preconception that remittances elicit reductions in the labor force participation of recipient households. Controlling for endogeneity represents a major difficulty in empirical tests of this labor supply behavior. In analyses of cross section data, it is quite difficult to establish whether remittances cause lower labor supply or, conversely, remittances from relatives working abroad are a response to adverse conditions at home and support incomes of family members who have been unable to offer their labor services in the domestic market. Panel data offer better chances to establish exogeneity, but they are still rare for many developing countries.

Apart from the above direction-of-causality concerns, households who receive remittances normally self-select into this group by sending a working member of the household 6
on average twice the median of the world throughout the four decades, compared to the 50% of the world median value observed in developing countries.

**Table 4. Innovation (Medians) in High-Income and Developing Countries overt Time**

|||**WOR**|**LD**|||**HIGH IN**|**COME**|||**DEVEL**|**OPING**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**1960's **|**1970's **|**1980's **|**1990's **|**1960's **|**1970's **|**1980's **|**1990's **|**1960's **|**1970's **|**1980's **|**1990's**|
|Patents granted by the USPTO*|0|0|0|0|0.16|0.31|0.38|0.52|0|0|0|0|
|Patents granted by the EPO*|||0|0|||0.06|0.30|||0|0|
|R&D personnel*|5.20|7.54|10.94|15.22|9.27|13.00|17.79|24.56|1.21|2.37|3.28|11.47|
|R&D expenditure**|0.42|0.75|0.85|0.85|1.19|1.29|1.49|1.73|0.21|0.30|0.39|0.59|
|R&D financed by the productive sector**|0.42|0.38|0.51|0.56|0.62|0.57|0.69|0.92|0.03|0.03|0.07|0.18|
|R&D financed from abroad**|0.01|0.01|0.02|0.04|0.01|0.01|0.03|0.08|0.02|0.003|0.001|0.02|
|R&D performed by the productive sector**|0.22|0.42|0.67|0.65|0.49|0.51|0.83|1.07|0.05|0.12|0.26|0.29|
|R&D performed by high education**|0.15|0.21|0.30|0.26|0.21|0.27|0.33|0.39|0.03|0.05|0.14|0.13|
|R&D performed by the public sector**|0.19|0.20|0.27|0.27|0.17|0.25|0.28|0.31|0.29|0.13|0.18|0.23|

* Per 10,000 inhabitants

** As percentage of G.D.P.

Looking at what happened in each group of countries, we see positive trends for most of the variables. In high-income countries, the median values for patents granted by the U.S.PTO tripled between the 1960s and the 1990s. European patents granted to inventors residing in highincome countries quadrupled during 1980-2000. Other variables, such as R&D financed from abroad, R&D personnel, and R&D performed by the productive sector, increased by more than 100% from the 1960s to the 1990s. R&D financed from abroad increased by more than 500%. In the case of developing countries, increases were not observed in the median of patents, but the corresponding medians did increase for the rest of the R&D variables. For example, R&D personnel and R&D financed and performed by the productive sector increased by more than 500% during this period. Also, total R&D investment and R&D performed by higher education increased by more than 100%. On the other hand, R&D performed by the public sector and R&D financed from abroad decreased over time in developing economies.

During the period 1960-2000, the gaps between developed and developing countries increased for patents, R&D financed from abroad, and R&D performed by the public sector by more than 100%. For the rest of the variables, especially for R&D personnel and R&D financed and performed by the productive sector, the gaps decreased by 50 to 80%. Despite this reduction, 13
5. We also impose the same aggregate search rate as was reported there, 7 _._ 3 inspections per 100 shipments. Given these restrictions and the parameter choices discussed in the previous subsection, we choose the value of _γ_ in equation (15) to best match the inspection rate targets.<sup>61</sup> A value of _γ_ = 0 _._ 33 aligns the inspection rates across bins reasonably well. We focus the calibration on hitting the very high levels of inspection activity on the first bin, and the drop-off that occurs over the other bins with few lagged shipments. We approximate the inspection frequency pretty well in all bins except the bin with the highest lagged frequency. The structural parameters that drive variation in the inspection rates across bins are the _δl_ ’s, all seven of which are pinned down by _γ_ and equation (15). Panel A of Table 3 reveals values of _δl_ that drop rapidly across shipment frequency bins. The ambiguity level for shipments that were not observed in the prior year is nearly an order of magnitude higher than the level observed for shipments occurring 6-10 times, and more than two orders of magnitude larger than the value of _δ_ that applies to the bin with shipments observed more than 100 times in the prior year.

# **References**

- [1] Allingham, M.G. and A. Sandmo, 1972. Income tax evasion: a theoretical analysis. _Journal of Public Economics_ 1(3-4), pp. 323-338.

- [2] Armenter, R. and M. Koren, 2014. A balls-and-bins model of trade. _American Economic Review_ 104(7), pp. 2127–2151.

- [3] Bernasconi, M., 1998. Tax evasion and orders of risk aversion. _Journal of Public Economics_ 67(1), pp. 123-134.

> 61Experimentation showed that increases in the optimism/pessimism parameter _α_ change the allocation of search activity in a manner similar to decreases in _γ_ . Relative to an earlier version of the paper, this parameterization has a larger value of _α_ , the effects of which are largely offset by somewhat larger _γ_ . These offsetting changes in the structural parameters had very little effect on the magnitude of the counterfactual changes in the model.

40
|Hubei|-0.4|-2.8|-1.8|-1.7|
|---|---|---|---|---|
|Hunan|-0.5|-5.3|-4.7|-4.5|
|Jiangsu|-1.1|-0.2|-0.9|-0.5|
|Jiangxi|-3.1|-2.8|-4.8|-4.5|
|Jilin|-1.2|-2.1|-2.2|-2.0|
|Liaoning|-0.6|0.4|-0.6|-0.4|
|Inner Mongolia|-0.3|-4.7|-3.2|-3.7|
|Ningxia|-0.5|-9.4|-10.0|-10.0|
|Qinghai|-4.4|7.7|-2.5|-3.2|
|Shaanxi|-0.1|-1.2|-1.7|-1.6|
|Shandong|-3.0|-3.9|-7.0|-6.9|
|Shanghai|-0.8|-1.9|-3.5|-3.4|
|Shanxi|-1.8|-2.6|-7.3|-7.3|
|Sichuan|-0.6|-2.3|-2.0|-1.8|
|Tianjin|-1.0|-1.4|-2.2|-2.1|
|Xinjiang|-0.2|-2.7|-2.4|-2.4|
|Xizang|-0.1|1.7|1.5|1.8|
|Yunnan|-0.4|-2.1|-1.8|-1.7|
|Zhejiang|-0.6|-0.6|-1.4|-1.4|
|China|-3.5|1.5|-3.6|-3.6|

# **5. Conclusions**

In this paper, we analyze the potential impacts on the provincial economies of meeting China’s emission reduction pledges made under the Paris Agreement using a national emission trading scheme. For the analysis, we developed a multi-regional, multi-sectoral, recursive dynamic CGE model and calibrated it with the data from the latest (2012) provincial-level social accounting matrices. We consider two cases for allocation of emission allowances: grandfathering and auctioning. Grandfathering allocation is carried out under two approaches: emission-based and output-based. The revenue collected through allowance auctioning is recycled to the local economy through two schemes: public consumption/investment and lumpsum transfer to households. The national emission trading is assumed to start in 2020 and national caps on emissions will be tightened gradually over the years and fixed at the level in 2030 to meet the emission intensity targets under the Paris Agreement. The study also considers an alternative baseline, which incorporates detailed technological information by using the 55
# **1. Introduction**

Based on cross-country comparisons, a number of papers in the literature have found that measures of absolute poverty tend to fall with economic growth.<sup>2</sup> However, it is also evident that there is a sizeable variance in the impacts of a given rate of growth on poverty. Some of this is measurement error, but it has also been argued that there are systematic factors influencing the elasticity of poverty measures to higher mean income.

Probably the main reason advanced in the literature and in policy discussions as to why a given rate of growth can deliver diverse outcomes for poor people is that the “pattern of growth” matters independently of the overall rate of growth. We can state this hypothesis in slightly more formal terms as follows: _<u>Pattern of Growth Hypothesis (PGH): The sectoral and/or geographic composition of</u> economic activity affects the aggregate rate of poverty reduction independently of the aggregate rate of growth._ If true, then the often-heard claim that the policies that are good for growth are necessarily also good for poverty reduction becomes questionable, given that the actions needed for growth in one sector or place need not accord with those needed elsewhere. This is particularly salient to the role of agricultural growth, which is likely to require rather different policies to other sectors (Headey, 2008).

In principle one can think of two reasons why PGH might hold. The first is that the relevant between-group component of inequality is sufficiently large that the pattern of growth across those groups systematically alters the distribution of income and (hence) the extent of poverty at any given mean income. Intuitively, if economic growth is very intense in sectors that do not benefit poor people then inequality will rise, choking off the gains to the poor from growth.

The second reason is that the composition of economic activity is one factor influencing the initial level of inequality. This holds even if the subsequent growth process is distribution-neutral (all incomes grow at the same rate). Intuitively, when the poor have a low initial share of total income they will tend to have a lower share of the gains in aggregate income during the growth process. Empirically, the initial distribution of income is known to be important for the subsequent effect of economic growth on poverty (Ravallion, 1997; Bourguignon, 2003).

> 2 A partial list of contributions includes World Bank (1990, 2000), Ravallion (1995, 2001), Ravallion and Chen (1997), Fields (2001) and Kraay (2006).

2
schools based on religion. It appears that religion is a good variable for predicting private school choice, but not a determining factor of academic performance. In an equation of the form: Where _D_ is private school attendance, _Religion_ is a 0,1 dummy variable and if 1, means that parents choose schools based on religion and 0 otherwise, as derived from the PISA questionnaire, it is estimated that _Religion_ is a good predictor of private school choice: At the same time, _Religion_ it is not very much correlated with achievement: The variable _Religion_ therefore is used as an instrument for private school attendance. We estimate the impact of private school attendance using instrumental variables. The IV results show that private school attendance is associated with higher test scores (Table 4). The true effect of private schools is higher than in the base (OLS) equation. In fact, the true effect is at least three times what one obtains in the OLS regressions. Moreover, the effect size is higher and the private school variable becomes positive and significant in the case of science. Private school size effects in math, reading and science achievement are 0.17, 0.28 and 0.18, all significant. Given PISA’s scaling, this is close to 0.2 of a standard deviation in the case of math 19