rafmacalaba/datause-encoder-data huggingface dataset:
This particular value is used for illustrative purpose, and will allow us to make some comparisons with subsequent simulations with emissions targets (see Section 5).

The abatement paths have triangular or trapezoidal shapes; this shows that one of the inertia (Eq. 6) or ceiling (Eq. 4) constraint is always binding. In this case, the intuitive ranking of abatement activities is respected: the social planner starts implementing cheap before deep. But the social planner does not use the full potential of cheap before starting using deep, which enters in 2017 while cheap does not reach its ceiling before 2030. Moreover, a more stringent objective would force deep to start even earlier (see below).

The optimal implementation strategy does not strictly follow an hypothetical merit-order in which all the potential of the cheapest solutions is used before more expensive solutions are introduced. This example illustrates why the MAC curves should not be interpreted as abatements supply curves.

A more systematic analysis using dierent cumulative emission objectives (Fig. 5) conrms that for any objective it is never preferable to implement the expensive deep before cheap. But it shows that if the objective is stringent enough (about 210 GtCO 2 ), deep has to begin before all the potential of cheap has been exploited; the implementation is not sequential. If it is even more stringent (about 135 GtCO 2 ), deep is forced to start in 2000, at the same time as cheap.

Figure 5: Entry date et ceiling date of each abatement activity as a function of the cumulative emission objective. The entry date is the date when an activity begins to be implemented; the ceiling date is the date when the full potential of an activity is achieved.

# 4.2 Expensive options may be useful even when cheaper ones appear sucient

Let us rst analyze a case in which the climate objective is not very restrictive, say 210 GtCO 2 . This translates into cumulative abatements of 45 GtCO 2 over the period.<sup>6</sup> Cheap has a cumulative abate-

> 6Cumulative emissions in the baseline amount to 5 Gt/yr during 51 years, with a total of 255 Gt.

11
19 percent, but in Colombia 40 percent of households have accounts, whereas this ratio is 70 percent for Lithuania. Or similarly, both in Estonia and Switzerland over 85 percent of households have accounts, but while Estonia’s financial depth is again around 20 percent, Switzerland’s is over 160 percent. The positive but imperfect correlations of use of financial services with economic development and financial depth raises questions regarding what drives cross-country differences in financial use and access.

# **_4.3. Barriers to Access_**

It is important, however, not only to understand the actual use of financial services, but also barriers to access to be able to design policies to broaden access. Conducting a survey of up to five large banks in over 80 countries, Beck, Demirguc-Kunt and Martinez Peria (2007b) develop indicators of such barriers. They create barrier indicators for three types of banking servicesdeposit, loan and payments- across three dimensions – physical access, affordability and eligibility.

Barriers such as availability of locations to open accounts and make loan applications, minimum account and loan balances, account fees, fees associated with payments, documentation requirements, processing times and the like are found to vary significantly both across banks and countries. Indicators of access barriers are also found to be negatively correlated with actual use of financial services, confirming that they can exclude individuals and small firms from using bank services.

Beck, Demirguc-Kunt and Martinez Peria summarize different access barriers measured by their survey by creating two aggregate principal component indicators, one for access barriers for deposit services and another for loan services. Table 1 reports partial correlations of these barriers with different country characteristics. These relationships are not necessarily causal ones, both barriers and the country characteristics shown will have common underlying structural causes.
# Table B.7: **Effects of Inpres on Intergenerational Educational Mobility with Inverse Probability Weighting (IPW) Correction**

||**Panel A: Daughte**|**rs**|||
|---|---|---|---|---|
||**Years o**<br>**Line**|**f Schooling**<br>**ar CEF**|**Primar**<br>**Qua**|**y Completion**<br>**dratic CEF**|
||**Unweighted**<br>(1)|**IPW Corrected**<br>(2)|**Unweighted**<br>(3)|**IPW Corrected**<br>(4)|
|Born Between 1968-1972 × Inpres|0.513<br>(0.466)|0.432<br>(0.431)|0.197***<br>(0.047)|0.174***<br>(0.052)|
|Father’s Edu × Born 1968-72 × Inpres|0.043|0.021|-0.017*|-0.015|
||(0.062)|(0.057)|(0.010)|(0.013)|
|Father’s Edu Sq × Born 1968-72 × Inpres|||0.001*<br>(0.001)|0.001<br>(0.001)|
|Observations|848,350|848,350|848,350|848,350|
|R-squared|0.409|0.429|0.188|0.240|
||**Panel B: Sons**||||
||**Years o**<br>**Quadr**|**f Schooling**<br>**atic CEF**|**Primar**<br>**Qua**|**y Completion**<br>**dratic CEF**|
||**Unweighted**<br>(1)|**IPW Corrected**<br>(2)|**Unweighted**<br>(3)|**IPW Corrected**<br>(4)|
|Born 1968-72 × Inpres|1.534***|1.553***|0.195***|0.201***|
||(0.380)|(0.376)|(0.042)|(0.040)|
|Father’s Edu × Born 1968-72 × Inpres|-0.255***<br>(0.086)|-0.293***<br>(0.097)|-0.038***<br>(0.011)|-0.033***<br>(0.012)|
|Father’s Edu Sq × Born Between 1968-72 × Inpres|0.015**<br>(0.007)|0.018**<br>(0.007)|0.002***<br>(0.001)|0.002***<br>(0.001)|
|Observations|1,199,814|1,199,814|1,199,814|1,199,814|
|R-squared|0.323|0.332|0.117|0.153|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). The CEFs are based on Table A.2. Quadratic model is adopted following Becker et al. (2015). Unweighted estimates are same as estimates reported in Table 2 and Table 3 for years of schooling and primary completion outcomes, respectively. Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Years of schooling (Edu) in Census 2000 was calculated based on the education level completed. Years of schooling (Edu) in the IFLS was calculated based on highest grade completed in an education level. Primary completion takes the value of 1 if a child has completed 6 or more years of schooling and 0 otherwise. Family background is measured by father’s years of schooling. The Inverse Probability Weighting (IPW) estimates are calculated using 1975 district level density interacted with time trend. Data sources: Indonesia’s full count census 2000, Duflo (2001), IFLS and IFLS-East.

70
# **I. Introduction**

The gravity model is now enormously popular for analysis of a wide range of trade questions. This popularity is due to its apparently good performance in representing trade flows and to the strong theoretical foundations provided in papers such as Anderson (1979) and Anderson and van Wincoop (2003). Until recently, the gravity equation was almost invariably estimated using data sets converted into logarithms and truncated to contain only positive trade flows. The problem of zero trade flows was rarely emphasized, partly because trade theories were silent on their causes, partly because of a lack of recognition of their frequency, and partly because of the convenience of the log-linear estimator.<sup>1</sup> Recently, there has been growing recognition among trade economists that zero trade flows do not occur randomly and that using truncated samples in logarithms may yield misleading results. Melitz (2003) and myriad subsequent studies have added firm-level foundations to earlier models of zero trade flows, such as those based on the Tobin (1958) and Heckman (1979) models. Santos Silva and Tenreyro (SST) (2006) raised an important additional concern by pointing to the possibility of serious bias from combinations of heteroscedasticity and nonlinearity in the gravity model. Helpman, Melitz and Rubinstein (HMR) (2008) pointed to another potential source of bias in coefficient estimates—failure to account for the increase in demand for exports as the number of exporting firms increases.

Like SST (2006), we believe that at least part of the process of identifying the right estimator must involve analysis of generated data whose statistical properties are known. In this paper we evaluate different estimators of the gravity equation in the presence of pervasive zero trade flows _and_ under different patterns of heteroscedasticity. Because we are unsure of the process generating real-world data, we test alternative estimators on data sets generated using three different economic models in which the probability of a nonzero trade flow and the volume of trade when trade is nonzero are jointly determined: the threshold-Tobit model of Eaton and Tamura (1994); the Heckman model; and an extended Heckman model of the type used by HMR (2008).

> 1 As shown in Annex A, zero trade flows account for more than 40% of the possible bilateral trade flows in country-level data and more than 60% in U.S. 10-digit product-level export data, and the probability of zero trade flows is strongly linked to the explanatory variables in gravity models. The frequency of zero trade flows is much higher at the more disaggregated levels of product and firm trade that are increasingly being used for analysis.

2
_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 17 External debt as a share of exports in developing counties, 1992-2006**

<!-- Start of picture text --> Percent<br>700<br>First-time access<br>600<br>500 Countries without access<br>400<br>300 Countries with frequent access<br>200<br>100<br>0<br>1992 1994 1996 1998 2000 2002 2004 2006<br>External debt / GDP<br><!-- End of picture text --> _Source:_ World Bank Debtor Reporting System To sum up, governments and corporations in developing countries have gained access to the international bond market under a wide range of circumstances, suggesting that several factors are important. This is supported by recent empirical studies. Grigorian (2003) finds that first-time sovereign bond issues by 32 middle-income countries over the period 1980-2002 are influenced by a few external factors (international interest rate and US GDP growth) along with several internal factors (the level and growth rate of GDP, GDP per capita, the current account and fiscal balance, the external debt burden, the level of foreign reserves relative to imports, and inflation rates). Gelos, Sahay and Sandleris (2004) find the quality of countries’ policies and institutions and their vulnerability to external shocks all play prominent roles as well. The analysis is further complicated by complex linkages between supply and demand factors. For instance, public sector borrowing requirements depend on governments’ overall fiscal position, which in turn is influenced by their ability to access external financing. In other words, governments that can access private debt markets on a frequent basis are more able finance larger budget deficits.

# **4. Prospects**

Although the surge in private capital flows to developing countries in 2003-07 has largely gone to middle-income countries, a number of low-income countries have gained access to the international bond market. However, the prospects for other developing countries to access private debt markets have worsened significantly in late 2008 in the wake of the global financial crisis. Spreads on emerging market sovereign bonds widened sharply in September 2007 following the collapse of Lehman Brothers and other major financial institutions (figure 18). As of mid-January 2008, sovereign spreads exceed 1000 basis points in 15 of 38 emerging market economies tracked by JPMorgan indexes. Moreover, spreads on emerging market corporate bonds widened by even more, especially for non-investment grade issues 23
**Table 3: Tanzania’s Maize Production Volatility Varying with Production Volatility in Other Regions and Percent Deviation of Maize Production from Trend**

|||**Deviat**|**ions from tr**|**endproduction**|**(percent of**|**trend)**|
|---|---|---|---|---|---|---|
||**Correlation (w.r.t.**|**1995**|**1971**|**1980**|**1982**|**1983**|
||**Tanzania)**<br>**in Percent**<br>**Deviations from**<br>**Trend of Maize**<br>**Production,**<br>**1971-2001**|**Tanza**|**nia +**|**Tanzania 0**|**Tanz**|**ania -**|
||**I**|**II**|**III**|**IV**|**V**|**VI**|
|Argentina|-0.22|-6.94|5.24|-21.76|15.98|7.43|
|Brazil|0.12|17.90|-0.65|8.04|8.45|-10.02|
|China|0.29|5.68|8.25|5.71|-7.15|0.03|
|E. Asia|0.18|-6.89|17.97|-3.80|-14.76|-0.31|
|E. Europe &<br>Former USSR|0.06|-9.66|-8.59|-4.11|20.35|8.29|
|Latin America<br>& Caribbean|0.37|2.75|9.72|-7.48|-6.83|-11.72|
|Malawi|0.11|0.23|-7.15|-2.95|14.57|9.83|
|Mexico|-0.19|9.32|16.61|14.85|-11.45|12.02|
|Middle East<br>&<br>North Africa|0.00|-14.90|2.53|-2.64|-7.28|-5.20|
|Mozambique|-0.02|-2.21|-44.67|32.76|24.28|14.98|
|Oceania|0.13|-11.95|6.79|-4.68|14.12|-17.12|
|R.E. Africa|0.28|-1.90|-9.45|-5.26|4.70|10.07|
|R.N. America|0.27|-3.23|15.17|12.44|17.08|2.56|
|Rest of Sub-<br>Saharan<br>Africa|0.13|13.43|86.01|-28.44|-32.15|-34.30|
|S. Asia<br>|0.15|-2.41|-11.09|5.40|-3.76|8.89|
|Southern<br>Africa|-0.11|-43.46|-2.49|25.86|0.04|-49.22|
|Tanzania|1.00|18.60|15.84|-0.10|-13.10|-16.69|
|Uganda|0.20|16.84|-25.59|-28.96|-3.22|-0.07|
|USA|-0.01|-14.96|-0.47|-0.33|19.26|-40.63|
|W. Europe|0.01|-8.69|0.74|-0.59|8.22|3.12|
|Zambia|0.29|-29.00|-14.53|-22.26|-37.99|-22.64|
|Zimbabwe|-0.13|-52.31|6.07|-14.77|1.90|-48.75|

Note: Global maize production time series data for the period 1971-2001 were obtained at the national level from FAOSTAT (2010), and then de-trended using a statistical model that explains physical production of maize as a function of time. The time series of estimated residuals, expressed as percentages of predicted production, is taken to represent the percentage deviations of production from trend. Percent deviations of maize production from trends for all years and the variance-covariance matrix are available in Appendix A.

8
- 7 -

public corporations with varying degrees of autonomy, as well as specialized training institutions established within public agencies to serve particular sectors of the economy -- such as construction, transport, and agriculture.

1.23 This definitional taxonomy has been used to structure the sample of industry VET project components on which the review is based. Within the focus on employment in the industry sector, country income level and training mode -- and the additional criterion of changes over time -- provide the basis for comparative analysis of Bank VET investment experience.

# A Dynamic Conceptual Model

1.24 A dynamic conceptual model of training systems has been developed for heuristic purposes as part of the overall research plan for the lank's broad VL policy study (Middleton and Schwartz, 1986). A social 3ystems approach was taken in order to identify the key factors that determine both the internal and external efficiency of any training institution. These factors then become the basis for the identification of variables and data needs for a range of studies, including operational reviews and field cases.

1.25 The model (Figure 2) rests on the basic premise that the internal and external efficiency of any training system (8.0) are determined by the interaction of the system with employment systems (5.0). The two are dynamically connected by flows of information and resources, including a crucially important feedback component on the effcctiveness of skills development (16.0).

1.26 The model is gencric. The nature of its components and connecting flows, however, is likely to be very different when compared across distinctive political and economic systems. The model provides a structure for the analysis of VET systems, suggesting where to look for the key elements and potential points of policy intervention.

1.27 The model shows how the political and economic system of a society, as shaped by external economic and social factors (1.0), functions through its policy institutions and mechanisms (7.0) to establish basic policy and resource parameters (9.0, 10.0) for training (8.0) and employment (5.0) systems. Differences in these basic resources and policy parameters lead to differences among training and employment systems. These are each comprised of dynamically-linked sub-systems that define their efficiency and effectiveness. In the training system, for example, training is comprised of teachers, studernts, facilities and equipment linked through a curriculum and learning activities. In the employment system, manpower is joined with other resources and technology in the production process.
set of estimates headed cuAt), the estimates of α2 are less than 1 for all of these groups. Although the estimates are not very precise, they are generally more than two standard deviations away from one.

The more interesting results occur when we divide the youth unemployment into education or household income groups. Youth in the lowest education group and in the lowest income quintile respond more than proportionally to the adult unemployment rate. As the youth education level rises, the estimate of α2 gets progressively smaller, implying that more educated youth are less harmed by adverse business cycles. Although the pattern is not perfectly consistent, the tendency is that as household income level rises, the estimate of α2 also decreases, and so it is the poorest youth who are most adversely affected by adverse business cycles. In the differenced specification, the pattern remains for education groups, with the two least educated groups facing more than proportional increases in unemployment. The pattern with respect to income quintiles does not hold up, except that the unemployment of youth in the highest income quintile is the least sensitive to business cycles.

# **Youth Employment Rates**

In only four cases does an atypically large youth cohort affect youth employment rates, and in three of the cases, the impact is positive. In changes all of these effects disappear. We conclude that there is no evidence that atypically large youth cohorts cause youth employment rates to fall.

Negative estimates of α2 are consistent with a negative impact of increased adult unemployment rates on youth employment rates. In the level regressions, every estimate is negative and most are statistically significant. Magnitudes do not vary greatly across genders or across urban and rural markets. There is modest evidence that the adverse impact is greatest for the least educated. In the differenced form equations reported in the second set of estimates, the employment responses for male and female youth and for urban and rural youth are all insignificantly different from zero. Similarly, the apparent negative effect of adult unemployment on youth employment rates also disappears. We conclude that youth employment rates are not sensitive to fluctuations in adult unemployment.

# **Youth Jobless Rates**

There is more evidence that the crowding from atypically large youth cohorts increase the jobless rate. In the level regressions of Table III, 11 of 16 estimates of α1 are positive and five are statistically significant. However, the effect is fragile with respect to the presence or absence of country fixed effects. In differenced form, 15 of 16 estimates of α1 are negative, suggesting no systematic penalty in the form of youth joblessness resulting from large youth cohorts.

Youth joblessness rises with increases in the adult unemployment rate. The estimates of α2 are all positive, ranging narrowly between 0.08 and 0.35. The similarity 20
# TABLE 3. OLS ESTIMATES.

|Dependent variable: EBRD|INDEX|||||||
|---|---|---|---|---|---|---|---|
||**(1)**|(2)|(3)|(4)|(5)|(6)|(7)|
|**_CONSTANT_**|2.55***<br>(3.06)|2.72***<br>(3.88)|2.92***<br>(3.16)|2.71***<br>(3.88)|2.6***<br>(3.18)|2.72***<br>(3.92)|3.35***<br>(3.89)|
|**_EBRDINDEX(-I)_**|0.72***|0.73***|0.72***|0.72***|0.71***|0.72***|0.70***|
||(8.52)|(10.73)|(10.3)|(10.7)|(8.63)|(10.8)|(6.85)|
|**_FREEDOM1993_**|0.22**|0.17**|0.18**|0.19**|0.23**|0.19**|0.19**|
||(2.11)|(2.12)|(2.25)|(2.34)|(2.32)|(2.37)|(1.99)|
|**_GOVCHANGE_**|1.69<br>(0.67)|1.14<br>(0.73)||-||||
|**_FREESTAB_**|0.38<br>(0.76)|0.20<br>(0.69)|-|-0.001<br>(0.02)|-|-||
|**_COHESIDEOL_**|0.40<br>(1.47)|0.28<br>(1.30)|0.26<br>(1.22)|0.26<br>(1.22)|0.24<br>(1.34)|0.26<br>(1.23)||
|**_AID_**|-0.002<br>(0.04)|-|-|-|0.00<br>(0.01)|-||
|**_WAR_**|0.65<br>(0.96)|-|0.49<br>(0.93)|0.49<br>(0.93)|0.67<br>(1.01)|0.48<br>(0.93)||
|**_FSU_**|-|-|-0.13<br>(0.33)|-|-|||
|**Adjusted R-squared**|0.85|0.84|0.84|0.84|0.85|0.84|0.84|
|F-**statistic**|40.2***|65.3***|78.9***|<br>78.7***|<br>57.9***|99.9***|195.7***|
|**White test for**<br>**heteroscedasticity**|23.2|20.0|21.1|19.7|21.8|18.7|18.1***|
|**Number of observations**|<br>50|75|75|75|50|75|75|

Absolute value of t-statistic in parenthesis. *, **, and *** mean that the coefficient is significant Respectively at 10%, 5% and 1% level of confidence. The t-statistics in equation (7) are Obtained using the White heteroscedasticity-consistent standard errors.

18
Lopez-Portillo, J.L., Lewis III, R.R., Saenger, P., Rovai, A.S (2017). Mangrove Forest Restoration and Rehabilitation. Mangrove Ecosystems: A Global Biogeographic Perspective. Springer, Cham. <u>https://doi.org/10.1007/978-3-319-62206-4_10.</u> Lovelock, C., Cahoon, D., Friess, D., Guntenspergen, G., Krauss, K., Rogers, R., et al. (2015). The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526 Massel, S.R., Furukawa, K., and Brinkman, R.M. (1999). Surface wave propagation in mangrove forests. Fluid Dynamics Research 24 219.

Maza, M., Lara, J.L., Losada, I. (2021). Predicting the evolution of coastal protection service with mangrove forest age. Coastal Engineering, 168.

Mazda, Y., Wolanski, E., King, B. (1997). Drag force due to vegetation in mangrove swamps. Mangroves and Salt Marshes 1, 193–199. <u>https://doi.org/10.1023/A:1009949411068</u> McIvor, A., Möller, I., Spencer, T., Spalding, M. (2012) Reduction of wind and swell waves by mangroves. Natural coastal protection series: Report 1. Cambridge coastal research unit working paper 40. The nature conservancy, Arlington, USA/Wetlands International, Wageningen, Netherlands, pp 27 McKee, K. and Vervaeke, W. (2018). Will fluctuations in salt marsh–mangrove dominance alter vulnerability of a subtropical wetland to sea-level rise? Global Change Biology 28 Menéndez, P., Losada, I.J., Torres-Ortega, S., Narayan, S., Beck, M. (2020). The global flood protection benefits of mangroves. Scientific Reports, 10.

Méndez, F.J., Losada, I.J. (2004). An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields. Coastal Engineering 51(2):103-118 Montgomery, J.M.; Bryan, K.R.; Horstman, E.M.; Mullarney, J.C. (2018) Attenuation of Tides and Surges by Mangroves: Contrasting Case Studies from New Zealand. Water, 10, 1119.

Morison, J. R., Johnson, J.W., O’Brien, M.P. (1950). The force exerted by surface waves on piles. Petroleum Trans. 189, pp. 149–157.

Mukhopadhyay, A., Wheeler, D., Dasgupta, S., Dey, A., Sobhan, I. (2018). Aquatic Salinization and Mangrove Species in a Changing Climate: Impact in the Indian Sundarban. World Bank Policy Research Working Paper No. 8532.

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