rafmacalaba/data-use-mentions-extended huggingface dataset:
12

# 5.2 The case of banks

In order to highlight the difference between bank and non-bank ratings, we replicate the previous set of estimates also for banks. Although, in a large number of cases, bank deposit ratings are a mechanical reflection of sovereign ratings, it is useful to verify to what extent this rule applies in equal measure to HIUCs and NHICs. The bank panel contains observations referring to Moody's long term bank deposits ratings for 959 banks from 57 countries. Country-wise, 73.4 per cent of the banks are based in highincome countries holding a median rating equal to 80.4 (or just above Al), which compares with 44.5 (or just below Ba2) for rated banks in NHICs. This entails that bank ratings are generally higher than non-bank ones in both high-income and NHICs.

Table 7 reports the results for the same five specifications of the panel regression'<sup>6</sup> used for non-banks. The results of the first specification show that rating changes for bank i at time t are negatively related to the rating of bank i at time _t-1_ (RATING(T-1)); they are strongly positively related to sovereign rating changes at time t (ASOVEREIGN) and they also depend positively on the level of sovereign ratings at time _t-1_ (SOVEREIGN(T-1)).17 As in the non-bank panel, the negative and significant coefficient of the dummy for NHICs (NONHINCM DUMMY) suggests that rating changes are generally smaller in their absolute size-but not necessarily in relative terms-in these countries.

Similar to the results obtained for non-banks, the second specification adds the new variable NONHINCM*ASOVEREIGN, in order to check whether sensitivity to sovereign rating changes is larger for banks in NHICs. Results are interesting. Although ASOVEREIGN is still significant, its coefficient drops from 0.80 to 0.28, while NONHINCM*ASOVEREIGN is significant and exhibits a large coefficient (0.65). This implies that bank rating changes are sensitive to sovereign rating changes
In sum, while there is evidence that trade has resulted in the transfer of CO2 emissions to low-income countries according to comparative advantage (see below), there is little evidence that environmental and trade policies have validated the PHH.

# **4.2 Climate Change Mitigation, Leakages and Border Tax Adjustments**

<u>Comparative-advantage-based shifts in CO2 emissions. Throughout the Kyoto Protocol</u> (KP) negotiation period (1990-2008), CO2 emission transfers from developing to developed countries through trade were 1.2Gt, an estimate that exceeded the target KP1 emission reductions (Peters et al. 2011) indicating that, as feared by environmentalists, virtual trade in carbon has adverse unintended consequences for climate policy. Other estimates also conclude that the pattern of trade between developed and developing countries has been shifting with a rising import-export ratio of energy-intensive products in high-income OECD countries (especially the US) and the opposite in low and middle-income countries (especially China).<sup>16</sup> The upshot is that, contrary to what is reported in the production-based UNFCC statistics, Annex B countries have continued to contribute to the growth in emissions.

These estimates suggest three remarks. First, the territorial-based caps under the KP have thus far allowed Annex B countries to meet their target because of their positive BEET, so that any cap on emissions should be on consumption and not on production (as under the Montreal Protocol). Second, developing countries whose exports are carbon-intensive can be expected to continue opposing joining a successor to KP. Third, the importance of carbon-intensive exports by developing countries to developed countries suggests that countries like China might be willing to undertake more substantial climate-mitigation commitments in exchange for firmer trade disciplines that would guarantee them access to carbon-intensive markets in the OECD.<sup>17</sup>

- <u>Policy induced leakage estimates. The above estimates can be considered passive or</u> comparative-advantage based (as opposed to policy-induced and subject to leakage). Turning to policy
richer. Private transport expenditures are overall increasing in a non-linear way. Figure 8 shows that for many countries a substantial share of households still consumes mostly public transport services, and have yet to start spending on private transport goods and services, which mostly depend on asset ownership.

Figure 8: Share of transport expenditure in total expenditure for the LSMS sample of countries <!-- Start of picture text --> Afghanistan Burkina Faso Cameroon Iraq<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>Kyrgyz Republic Malawi Mali Mongolia<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>Nepal Nicaragua Niger Nigeria<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>Pakistan Sierra Leone Tanzania Uganda<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>4 6 8 10 4 6 8 10 4 6 8 10 4 6 8 10<br>Annual expenditure per person (log scale)<br>All transport Private Services Household density<br>Share of total expenditure, in %<br><!-- End of picture text --> Note: Authors’ calculations using the LSMS surveys.

# **3.4 Income and private transport**

Expenditures for private transport include the acquisition, use, and maintenance of vehicles. The extent to which the consumption of private transport will affect carbon emissions depends on the number and types of vehicles and the intensity of use.

## **3.4.1 Income and vehicle ownership**

**Fact 6: As countries get richer, more households acquire cars and each household might acquire
**_Results._** The CFA devaluation has had excellent intermediate-term effects on growth. For the 12 CFA countries in Devarajan's sample, real GDP growth from 1990 to 1993 averaged almost negative 0.3 percent annually weighted by GDP, according to World Bank data (World Bank, 1999). However, from 1994 to 1997, growth in these same countries averaged 5.1 percent annually, according to the same data source.<sup>23</sup> Cameroon, the largest country in the CFA zone, grew at an annual rate of-3.4 percent in the first period, but 4.5 percent in the second period (World Bankc, 1999). Devarajan found that a year after the devaluation, the average undervaluation was two percent for the group, but with significant variance.

# **Lessons from Africa**

The countries of Sub-Saharan Africa have made significant strides in correctly valuing their exchange rates. In 1980, 45 African countries had pegged exchange rates, four had managed floats, and none had independent floats. By 1996, 25 had pegged exchange rates, six had managed floats, and fully 19 had independent floats, according to Sekkat and Varoudakis. In both years, 14 were pegged to the French franc. This move towards a flexible exchange rate allowed most non-CFA zone countries to experience a real depreciation, starting in 1984-5. The CFA zone countries had their own depreciation in 1994.

By the late 1990s, much of Africa had returned to growth. Herndndez-Catd (1999) reported that real GDP grew 4.25 percent annually from 1995 to 1998, up from less than 1.5 percent from 1990 to 1994, with per capita output up one percent in the later period, against a decline of 2.25 percent in the earlier period. Inflation peaked at 47 percent in 1994, and dropped to 10 percent 1998. He credited the improved perfornance to better policy rather than external developments, which were adverse in 1998.

A great deal of
road|21.7|**26.2**|59.9|
|Paved road traffic|Average annual<br>daily traffic|1,049.6|**600.6**|2,786.0|
|Unpaved road traffic|Average annual<br>daily traffic|62.6|**44.5**|12.0|
|Paved network condition|% in good or fair condition|80.0|**85.5**|79.0|
|Unpaved network condition|% in good or fair condition|57.6|**89.4**|58.3|
|Perceived transport quality|% firms identifying roads as major business<br>constraint|23.0|**16.4**|10.7|

_Source:_ Gwillliam and others 2009. Derived from AICD national database, http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ GIS = geographic information system.

## **Challenges**

Road preservation expenditure still falls significantly short of what is needed to preserve the network in good condition. Based on the Road Network Evaluation Tool (RONET) analysis, Malawi’s recent spending on road maintenance falls about 24 percent short of what is needed to sustain the infrastructure (figure 4). One of the issues is that the fuel levy of around $0.07 per liter, while fairly typical of southern Africa, falls substantially short of the $0.25 per liter needed to fully finance optimal maintenance requirements. This benchmark level is probably higher than what could in practice be applied, and certainly well above the highest level observed in the region (in Tanzania, at around $0.16 per liter). Government transfers to the road fund currently make up for part (although by no means all) of this difference, reaching an implicit spending level of almost
Malawi). This heterogeneity reflects the fact that some countries are more agrarian than others and that some are large net-consumers, others are large net-producers, and yet others either consume and produce little cereals or do so in similar magnitudes so that the net effects tend to cancel out. A similar story can be told about the pro-poor bias of liberalization. There are countries where cereals liberalization would be clearly pro-poor (Tanzania, Bhutan, Guinea Bissau, Ecuador) and countries where it would be clearly pro-rich (Vietnam). There are also cases where the liberalization would be neutral and this can in turn be because of little direct consumption and production of grains (Armenia, Georgia) or because of the offsetting consumption and income effects (Madagascar, Nigeria).

As this analysis illustrates, the HIT data and our framework enable researchers to examine and exploit the extensive household heterogeneity in incomes and expenditures in many dimensions. There are some limitations to the framework that are important to note for accurate interpretation of our results. To make it operational, we need to impose some structure, in particular perfect competition, constant returns to scale, and product homogeneity. Since we work with household surveys and with an agricultural household model `a la Deaton, there are some limitations in the scope of the analysis as well. The household surveys do not collect reliable information on the returns to capital and on profits and often fail to capture very rich households. Also, there are marked differences between consumption and production aggregates from the household surveys and those from the national accounts. As a result, there might be some discrepancies between the household welfare effects from the HIT data and the aggregate welfare effects stemming from more general trade models. In addition, some relevant impacts, such as heterogeneous varieties, two-way trade or labor and investment effects, may require additional modeling assumptions as well as additional data (such as demand and supply elasticities and so on). In any case, most of the discussion about poverty, inequality and household welfare is typically based
|8.890(0.191)|9.440(0.155)|
|R-squared|0.530|0.362|0.532|0.380|
|Observations|2,152|2,775|2,026|2,572|

Robust standard errors in parentheses; *** p <0.01, ** p<0.05, * p<0.1 Source: Serbia LFS, 2008 and 2009 The estimates from these regressions are used to calculate a threefold and a twofold Oaxaca-Blinder decomposition (Table 9). The wage differentials between men and women decreased from 9.2% in 2008 to 4.6% in 2009. As mentioned earlier, this is likely because male-dominated sectors have been somewhat more affected by the global economic crisis.

The results of a threefold decomposition (based on equation 3 described above) show that women have higher endowments (e.g. higher education levels) than men; however they receive lower returns for these endowments. In fact, if men on average had the same characteristics as women the wage differentials would have been larger. These conclusions are supported by findings from a twofold decomposition (according to equation 4). Women on average have better employment-related characteristics as reflected by the negative sign in “the explained part”), which has an equalizing effect on the overall wage gap. The “unexplained part” (i.e. the factors that cannot be attributed to differences in observed worker characteristics), accounts for the greatest part of the wage differential between men and women and is indicative of discrimination against women in the Serbian labor market.

To understand which variables have the greatest impact on the endowments and coefficients effects, a detailed OaxacaBlinder threefold decomposition was performed and the results are presented in

**Table 9. Oaxaca-Blinder threefold and twofold decomposition of** **<u>monthly wages of male and female employees</u>**

||2008|2009
# **2. Data**

To document worldwide patterns of capital flows by domestic and foreign agents, we assemble a comprehensive dataset on aggregate gross capital flows, including not only capital inflows and outflows but also their subcomponents, reflecting the different flow types. The data come from the analytic presentation of the IMF’s Balance of Payments Statistics Yearbooks (BOP).<sup>12</sup> The IMF’s BOP dataset provides country-level data, on an annual basis from 1970 until 2009, on different types of capital inflows measured in U.S. dollars. Fundamental to our goal, this dataset allows us to disentangle, respectively, capital outflows by domestic agents ( _COD_ ) and capital inflows by foreigners ( _CIF_ ), which are reported as flows related to the reporting country’s assets and liabilities vis-à-vis non-residents. In other words, _CIF_ is recorded as capital inflows to the reporting economy by foreign agents, indicating an increase in foreigners’ holdings of domestic assets. Analogously, _COD_ is reported as flows from the reporting economy, where positive values correspond to an increasing of the holdings of foreign assets by domestic agents.<sup>13</sup> Hence a positive _COD_ should be interpreted as capital outflows by domestic agents whereas a negative _COD_ means capital inflows.

Our dataset also allows us to analyze the behavior of the different types of capital flows. Flows are classified as: direct investments (also known as FDI), portfolio flows, other investments (mostly bank flows and trade credit), and international reserves.<sup>14</sup> Portfolio flows are further divided into equity and debt flows. Both private and public flows are included in our

> 12 Debt refinancing and rescheduling entries that involve changes in existing debt contracts or replacement by new ones, generally with extended debt service payments are excluded from our dataset. In the analytic presentation of the IMF’s BOP, these flows (credit and debt entries that account for the new contracts) are computed
# TV.

Baseline data reveal large gaps in knowledge and understanding of behaviors that are important for the development of children. Table 2 shows teacher reports of how important various behaviors are for child development, broken down by development domain. Teachers are able to select options from "very important" to "not important at all" when rating how important various behaviors are for child development. Figure 1 highlights the areas in Table 2 where there are the largest gaps in knowledge: cognitive, language, and social development. Most strikingly, only 31% of teachers agree that it is very important to encourage children to interact with others. The importance of child interaction with others is crucial for teachers to understand. Without lots of interaction, children do not develop social skills, self esteem, creativity, and imagination (Early Education Professionals (2018)). Less than half of teachers agree that practicing numbers and letters with children is very important. Also, less than half agree that it is very important that children be played with and that children should be encouraged to listen. While most teachers still select that these behaviors are important, they do not recognize that they are the most critical behaviors for development in each domain. These gaps reveal that teachers in ECED centers, who are partially responsible for the development of the children they teach, do not place enough importance on some behaviors that are critical for this development.

The largest gap in understanding in our sample comes in the realm of social development. As shown in figure 1, 31% of teachers agree that it is very important for child development for children to interact with others. Social development in children involves their ability to engage with their environment, maintain and create healthy relationships, and work with others; encouraging them to interact with others and make friends is crucial for this type of development. Still, only 50% of teachers agree that it is very important to encourage children to make friends. While it is heartening that most teachers still rate these activities as
: project LGAs do not seem to consistently outperform comparison areas, which often presented larger improvements.

To formally test whether changes in project areas were different from changes in comparison areas, we estimate equation 1 above, including a series of individual level controls.<sup>22</sup> Figure 3 - Figure 5 present the results for these regressions for the household survey, while Figure 6 and Figure 7 present results for government officials. The left panel in each figure presents the point estimate and 95% confidence interval for the coefficient γ, indicating the change in outcome,<sup>23</sup> between 2016 and 2018, for comparison LGAs. The right panel presents the same for coefficient β, and it is the difference-in-differences coefficient: it represents the differential change in outcome among project respondents, when compared to non-project respondents.

Focusing first on the left-hand panel, we observe that for a vast number of indicators, as discussed above, comparison areas recorded improvements between 2016 and 2018. All indicators are constructed such that higher values indicate normatively better outcomes or improved views, and very few estimates indicate worsening performance between first and second rounds, in

> 22Controls include household size, age, gender, marital status and education level. Note that we are not estimating individual fixed-effect models, so we can include time-invariant comparisons. Furthermore, as discussed above, some households and officials are replaced in the second round survey so controls would still vary over time for some units. All standard errors are clustered at the LGA level. Due to the small number of clusters, we also report 95% confidence-interval for our estimates using wild-bootstrapping. Results are not sensitive to the inference method.

> 23For ease of reading, all dependent variables were standardized before the regressions, so coefficients are to be interpreted as changes in standard-deviation units of the dependent variable.

23
forward integration, albeit higher on forward participation in the global sample. On the contrary, distance to major manufacturing hubs has a strong positive effect on both backward and forward integration in SSA. This means that hubs that are relatively farther away from SSA tend to trade more in manufacturing GVCs than those that are closer to the region. This is not very surprising for two main reasons. First, is China’s increasing presence in the African continent despite not being the closest major manufacturing hub. Apart from China being one of the continent’s major trading partners for the past decade, SSA countries are increasingly diversifying their export partners toward the BRIC and other emerging economies which are far from the closest manufacturing hubs (i.e. Germany). Second, the strong positive effect of distance on backward and forward GVC participation in SSA can be explained by South Africa’s presence in the SSA sample. This is because South Africa has the highest rate of integration in global value chains despite being the farthest country from the three major manufacturing hubs.

# **VI. Concluding remarks and policy recommendations**

This study assesses the role of human capital and natural resource endowment in SSA participation in manufacturing GVCs. We looked at the effects for both backward (FVA) and forward (DVX) participations as well as the overall GVC participation ratio. Fixed effects panel regressions with robust standard errors are estimated for 23 SSA countries and results are compared with a global sample of 115 countries, covering 19 manufacturing subsectors. Because of the lack of skilled/unskilled labor data for most SSA countries, we generated skilled/unskilled labor content in export using the labor content of exports (LACEX) methodology developed by Cali et al. (2016). We mapped the skilled data set defined for 57 sectors from the input-output GTAP table with the GVCs data derived from UNCTAD-Eora GVC Database which has 25 sectors. We focus on the impacts of skilled/unskilled labor contribution, migration and initial human capital endowment on forward and backward GVC
10 1 are between zero and one and represent, respectively, the share of each capital type in total output. The sum of these two parameters is strictly less than unity and technological progress is assumed to be labor-augmenting.

Physical capital and human capital are assumed to accumulate in similar fashion. The dynamic equation for physical capita! is given by where K is the change of the physical capital stock at any point in time, sk is the share of output dedicated to the accumulation of this type of capital and a is its depreciation rate. Similarly, human capital accumulates according to where H is the change of the human capital stock at any point in time, sh is the share of output assigned to the accumulation of this input and the rest of the notation follows the definitions already presented. For simplicity we assume the same rate of depreciation for physical and human capital.

As discussed in Box 2, we use as the share of GDP assigned to the accumulation of physical capital the data on gross domestic investment in the economy as a percentage of GDP. For the share of GDP allocated to the accui lulation of human capital we will use as a proxy the average level of this input in the economy. We also assume that the policy framework (i.e., the existing economic policy, regulatory, and legal features of the economy) is essential in determining the effectiveness of human capital in the production process. As one example, the economic performance in recent decades of the formerly centrally planned economies of CEE is a good representation of why high levels of human capital, or of investment in this input, do not in themselves ensure long-term growth. This approach is also consistent with the results of empirical studies which introduce policy indicators as explanatory variables in growth equations