rafmacalaba/datause-encoder-data huggingface dataset:
MONGOLIA

**-** 2. Projections NRR-1 by 2020

|ToerPeputation|O p..<br>ratio|Period|UGrot<br>**rat**|h<br>Ca|CD4|TFt|b<br>MaIm|*o<br>F_lt|it|qs<br>**X10OO**|
|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>2186|79.3|1990-94|2.66|34.6|7.9|4.49|62.3|64.7|58|77|
|1995<br>2497|77.7|1995-99|2.41|31.1|6.9|3.98|64.5|67.0|48|62|
|2000<br>2817|74.5|2000-04|2.13|27.3|6.1|3.47|66.6|69.2|39|49|
|2005<br>3134|68.7|2005-09|1.86|24.4|5.8|3.00|67.8|70.6|34|42|
|2010<br>3439|61.7|2010-14|1.61|21.7|5.6|2.59|69.0|72.1|28|35|
|2015<br>3728|55.5|2014-19|1.38|19.2|5.5|2.24|70.3|73.7|22|27|
|2020<br>3993|50.2|2020-24|1.27|18.2|5.5|2.12|71.7|75.4|,6|20|
|2025<br>4255|47.2|2025-49|0.96|15.6|6.1|2.09|74.9|79.2|8|10|
|2050<br>5413|50.8|2050-74|0.44|13.0|8.7|2.07|78.4|83.4|4|5|
|2075<br>6044|66.1|2075-99|0.12|12.2|11.0|2.06|80.2|86.0|3|4|
|2100<br>6234|70.8|2100-24|0.07|11.9|11.2|2.06|81.6|88.0|2|3|
|2125<br>6349<br>2150<br>6406|72.2<br>73.5|2125-49|0.04|11.8|11.4|2.06|82.2|88.8|2|3|

3. Age structure (percent): 1990 and 2025 <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75 +<br>70-74<br>56-69<br>60-64<br>55-59<br>60-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

TFR <!-- Start of picture text --> eO<br><!-- End of picture text --> Growth rate <!-- Start of picture text --> Co.utry Region Incom group<br><!-- End of picture text --> <!-- Start of picture text --> Country Region Incom group Country Region .ncom group<br><!-- End of picture text --> 79
radiative forcing significantly within a decade (Shindell et al. 2012). To assist the stakeholder community, we have worked with the World Bank’s Development Data Hub to publish an online database that makes georeferenced CH4 atmospheric concentration data more accessible to non-specialists.

# **2.2 Satellite Measurement Components**

We have constructed the atmospheric CH4 concentration database from observations by the ESA’s Sentinel-5P platform that typically number over 200,000 per day. We locate each observation in the 25-km global grid developed for CO2 analysis by Dasgupta, Lall and Wheeler (2022a,b). The dominant component in each observation is the global stock of atmospheric CH4 molecules that have accumulated during the past decade. The second component is seasonal, determined by natural processes over the annual cycle. The third component is geographic: Emitted CH4 molecules tend to remain within their latitude bands of origin for significant periods before full global mixing.

The fourth observation component is determined by local anthropogenic CH4 emissions, which remain near their point of origin long enough to be distinguished from the three “background” components. We term this the “concentration anomaly” since it measures the local deviation from the global background CH4 concentration. As previously noted, its primary sources are emissions from agriculture, fossil fuel production and use, and waste disposal.

# **2.3 Data Collection and Processing**

The ESA’s Sentinel-5P (S5P) operates in a Sun-synchronous near-polar orbit with an equatorial crossing at 13:30 local solar time. It completes 14 orbits of the Earth per day, with a site revisit time of one day and a current spatial resolution of 5.5 x 3.5 km. We use the S5P L2 Offline georeferenced measures of XCH4 (the column average dry air mixing ratio of methane<sup>4</sup> ), corrected for bias associated with XCH4 dependence on surface albedo.<sup>5</sup> Recent global research by Sha et al. (2021) has shown that S5P CH4 measures correspond very closely to independent measures taken at ground stations maintained by TCCON (Total Carbon Column Observing Network).<sup>6</sup> Sentinel-5P’s bias-corrected XCH4 data exhibit a difference of −0.26±0.56 % when compared with the corresponding TCCON data. Per the ESA’s recommendation, we use only pixels with quality values greater than 0.5. The data have been

> 4 the total atmospheric column between the surface and the top of the atmosphere normalized to the corresponding dry air column.

> 5 For more technical details, see the documentation at https://sentinel.esa.int/documents/247904/3541451/Sentinel5P-Methane-Product-Readme-File

> 6 Current TCCON sites are located in the United States, China, Canada, Germany, Poland, France, Japan, Australia, New Zealand, the Republic of Korea, Réunion, and Ascension Island.

3
The overall regression results in Table 8 lead to an important refinement of the conclusions presented in the foregoing section. We now see that type of school attended, whether vocational or academic secondary, does have an impact on labor market income. It is only when vocational school completers are employed in jobs unrelated to courses of study pursued at school, that earnings are broadly similar to those of workers who studied atacademic secondary schools. For those vocational school completers who work in study-related occupations, average earnings are significantly higher than those of workers who studied at academic secondary schools.

Finally, separate regressions are run for five-year age groups (within the 25-49 age range) to see whether these conclusions hold for each individual age cohort: the same specification is employed except that the experience variables are dropped, since in standardizing by age the number of years of job experience will be similar for individuals within each age cohort. Selected results are given in Table 9, for the certification variables ( which indicates a decline in credentialism as age of group increases) and the VOC sub group dummies.

The age cohort analysis confirms the central finding of the regressions relating to the total sample: those individuals that attended vocational schools and work in matched occupations consistently earn more than their counterparts that went through the academic secondary school stream. There is a tendency for the average earnings differential to rise with age, from about 4 per cent for the first age cohort reaching 11 to 15 per cent for the older age groups. Non-matched vocational school completers

- 36 -
At the outset, we acknowledge that this analysis has limitations. First, we do not assess the likelihood of alternative SLR scenarios. We take each scenario as given, and assess the impacts using our 6 indicators for each of the 84 developing countries and 5 regions. Second, we assess the impacts of SLR using existing populations, socio-economic conditions and patterns of land use, rather than attempting to predict their future states. Since human activity is generally increasing more rapidly in coastal areas, our approach undoubtedly _underestimates_ the future impacts of SLR in most cases. This underestimation will be greatest for SLR impacts on population and GDP in absolute terms (number of people impacted or $ of GDP impacted), Third, our study is conservative because we do not consider storm surge augmentation. Even a small increase in sea level can significantly magnify the impact of storm surges, which occur regularly and with devastating consequences in some coastal areas.

Despite these limitations, we believe that our comprehensive baseline estimates of SLR impacts can assist policymakers and international development institutions in allocating resources for adaptation to climate change. In particular, we believe that our specific estimates, based on existing coastal conditions, are more likely to interest decisionmakers than estimates based on projections of future coastal population, economic activity, etc.

In the next section, we describe the methodology and data sources used to estimate the impact of SLR in developing countries. We present our results in Section III, at the global, regional and country levels. Section IV provides a summary and conclusions.

# **II. Methodology and data sources**

# **II.1 Data Sources**

We employed geographic information system (GIS) software to overlay the critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5 m. SLR. We used the best available, spatiallydisaggregated data sets from various public sources, including the Center for Environmental Systems Research (CESR), the Center for International Earth Science Information Network (CIESIN), the International Centre for Tropical Agriculture (CIAT), 6
Table 2: Impacts on children’s educational achievement: <u>progression</u> to the next <u>grade</u>

|Dependent variable:|||One if|pass|||
|---|---|---|---|---|---|---|
|||December 20|13|De|cember 2|014|
||LPM|LPM|Probit<br>(ME)|LPM|LPM|Probit<br>(ME)|
||(a)|(b)|(c)|(d)|(e)|(f)|
|All products|0.022|0.023|0.024|-0.059|-0.058|-0.058|
||(0.030)|(0.031)|(0.033)|(0.042)|(0.042)|(0.039)|
|One product|0.002|-0.003|-0.012|-0.024|-0.024|-0.022|
||(0.026)|(0.028)|(0.026)|(0.038)|(0.038)|(0.036)|
|Age|-|0.011|0.014|-|0.005|0.006|
|||(0.015)|(0.015)||(0.015)|(0.014)|
|Education|-|0.211***|0.223***|-|-0.006|-0.007|
|(years)||(0.029)|(0.021)||(0.028)|(0.026)|
|Male|-|-0.035|-0.040|-|0.003|0.001|
|(dummy)||(0.032)|(0.037)||(0.062)|(0.058)|
|Household size|-|-0.016|-0.018|-|0.009|0.008|
|||(0.013)|(0.013)||(0.018)|(0.017)|
|No. of male members|-|-0.000|-0.000|-|-0.002|0.001|
|||(0.015)|(0.017)||(0.027)|(0.025)|
|Head age|-|-0.001|-0.002|-|-0.001|-0.001|
|(years)||(0.002)|(0.002)||(0.002)|(0.002)|
|Head education|-|0.002|0.002|-|0.007|0.007|
|(years)||(0.005)|(0.005)||(0.005)|(0.005)|
|Head male|-|0.040|0.034|-|0.120*|0.116**|
|(dummy)||(0.045)|(0.045)||(0.061)|(0.054)|
|Land size|-|0.001|0.001|-|-0.000|-0.000|
|(decimal)||(0.001)|(0.001)||(0.001)|(0.001)|
|All = one (p-val.)|0.485|0.417|0.243|0.389|0.419|0.343|
|All + one = 0 (p-val.)|0.612|0.700|0.824|0.236|0.241|0.219|
|R-squared|0.217|0.325|0.294|0.139|0.145|0.104|
|No. of obs.|850|850|754|845|845|832|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean _y_ (control)|0.716|0.716|0.686|0.644|0.644|0.643|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade.

26
# **TABLE** 5.3 _B_

_MODIFIED CONTROL-GROUP ESTIMATES_<sup>_OF PROGRAM EFFECTS!'_</sup> _(constant prices) (1985-88 relative to 1981-84)_

||_C_|_GDP2_|_INV2_|_SAVDOM2_|_EXP2_|_RER2_|_FISC2_|_SHOCK3_|_d_|
|---|---|---|---|---|---|---|---|---|---|
|_Change in_|_0.009_|_-0.750_|_0.057_<br>|_-0.030_<br>|_-0.015_<br>|_0.000_<br>|_0.028_<br>|_0.026_<br>|_0.020_<br>|
|<br>_CDP Growth_|_(0.625)_|_(-11.480)_|_(1.521)_|_(-0.989)_|_(-0.708)_|<sup>_(0.643)_</sup>|<sup>_(0.883)_</sup>|_(0.459)_|_(2.561)_|
|_Change In_|_0.027_|_0.006_|-0.343|_0.092_<br>|_-0.022_<br>|_0.000_<br> <br>|_-0.052_<br>|_0.081_<br>|_0.001_<br>|
|<br>_Investment/GDP_|_(0.908)_|_(0.063)_|_(-4.986)_|_(1.635)_|_(-0.662)_|<br>_(0.360)_|_(-0.860)_|_(0.806)_|_(0.039)_|
|_Change_<br>_in_|_-0.026_|_0.116_|_0.095_|_-0.238_|_0.069_<br>|_0.000_<br>|_-0.029_<br>|_0.128_<br>|_0.037_<br>|
|<br>_Saving/GDP_|_(-0.702)_|_(0.860)_|_(0.795)_|_(-2.714)_|_(1.249)_|_(0.306)_|_(-0.312)_|_(1.199)_|_(2.186)_|
|_Change in_|_-0.006_|_0.086_|_0.035_|_0.027_|_0.069_<br>|_-0.000_<br>|_0.000_<br>|_0.063_<br>|_0.025_<br>|
|<br>_ExportslCDP_|_(-0.219)_|_(0.817)_|_(0.494)_|_(0.508)_|_(1.522)_|_(-0.849)_|_(0.006)_|_(0.688)_|_(1.640)_|

_Note: The t-values are in parentheses._ _* Statistically significant_<sup>_at the 2.5 1 level._</sup> _** Statistically significant_<sup>_at the 5 t level._</sup> _*** Statistically significant_<sup>_at the 7.5 t level._</sup> _**** Statistically significant_<sup>_at the 10 t level._</sup> _al Estimation_ of _equation (9), using d_<sup>_as instrument of d._</sup>
observed difference in schooling between old and young cohorts is less than it would have been without selective mortality. Given we find a significant negative impact of the genocide on school-aged children exposed to the conflict, our results likely underestimate the true negative impact in an environment of selective mortality for the older cohort, and it is unlikely that the killing of educated individuals is the mechanism explaining our results.

A second possible mechanism is orphanhood. The large proportion of orphans in post1994 Rwanda suggests that the absence of one or both parents could be the principal driving factor. However, as discussed previously, Figure 3 does not confirm this intuition. While it is true that orphans do slightly worse than non-orphans in terms of schooling outcomes in 2000, it is striking that non-orphans in 2000 are doing worse than orphans in 1992.

A third possible mechanism is that households were made poorer due to the conflict (loss of crops and assets and disruption of business activities) and given the more difficult economic circumstances might further discriminate against the schooling of girls. However, our results indicate that the negative impact on education was actually larger for boys and for children from non-poor families, suggesting that the genocide had a leveling-off effect that brought boys and the non-poor to the same lower level of schooling as girls and poor children.

Finally, a fourth possible mechanism is the destruction of schools or lack of teachers due to the genocide and the subsequent impact on children’s schooling. However, we do not find evidence that infrastructure problems are driving the decline in educational attainment as we find almost no impact for grade 1 completion rates and the largest impact is not seen until grades 3 and 4. This seems to indicate that the most likely mechanism linking the genocide to reduced educational attainment is through grade progression, as opposed to not entering the school system, and this is consistent with large repetition rates (World Bank, 2003). Following the end 20
inhabitants. With the 1965 law the requirement of a minimum number of employees was in principle abolished but coverage should only be extended to establishments with less than 4 employees in cities and towns and with less than 8 employees in other places to the extent the necessary organizations were created. It will be understood that coverage gradually has been extended from 1965 to smaller and smaller localities and establishments.

To give an impression of the coverage we shall compare number of SII insured in manufacturing in 1963 and 1980 with number of employees enumerated by the censuses of manufacturing industry for these years since these two censuses included all establishments irrespective of size. The annual. surveys cover from 1971 only public establishments and private establishments with 10 persons and more engaged (that is, including unpaid family labor, employers and partners). Table 3.2 shows number of SII insured classified as employed in manufacturing and average number of employees enumerated by the censuses of manufacturing and business, both for 1963 and 1980 and both distributed by 20 identical sub-groups.

For 1980 both total number of insured and employees and their distributions were very similar, 1,023.6 thousand insured against 1,000.6 thousand employees with no dramatic differences for individual sub-groups. Neither social insurance nor censuses may cover all establishments perfectly, in particular not small

- 42 -
# **4. Poverty and overall economic growth**

Any poverty measure found in practice can be written as a function of the survey mean relative to the poverty line and the relative distribution of income, as represented by the Lorenz curve.<sup>42</sup> When the poverty line is fixed in real terms, all such poverty measures are strictly decreasing functions of the mean (<sup></sup> _t_<sup>) for any given relative distribution (though the elasticity</sup> can vary greatly, depending on the initial mean and Lorenz curve). A higher growth rate may also entail a shift in distribution for or against the poor. In characterizing the overall poverty impact of growth, we are interested in the total effect of growth on poverty, allowing distribution to change, rather than the partial effect, holding distribution constant.<sup>43</sup> We call this the “growth elasticity of poverty reduction”, or “growth elasticity” for short.

We estimate the growth elasticities at the national level by the regression coefficient of log poverty measure ( ln<sup>_P_</sup> _t_ ) on log mean per capita consumption ( ln<sup></sup> _t_ ) across the available time series, allowing the error term to be autocorrelated and heteroskedastic. Whenever both the dependent and independent variable of such a regression are estimated from the same survey data the possibility arises of bias due to the fact that measurement errors in the survey can be passed onto both variables; when overestimating the mean, for instance, one will tend to underestimate poverty. (The sign of the bias is theoretically ambiguous since measurement error in the independent variable will also induce an attenuation bias in the least squares estimate of the elasticity.) In all our regressions we have also tried an Instrumental Variables (IV) estimator, in which the instruments excluded any variables derived from the same survey as the dependent variable. Our estimates also control for the use of mixed reference periods for some of the NSS rounds.

Table 3 gives our estimates of the elasticities of all three poverty measures with respect to three measures of economic growth based on: (i) consumption per person from the NSS; (ii) consumption per person as estimated by the NAS and population census; and (iii) NDP ("income" for short) per person, also from the NAS and census. In all cases, the elasticities are estimated by regressing the log poverty measure on the log mean consumption or income. We

> 42 This follows from the fact that the mean and the Lorenz curve fully specify the cumulative distribution function. 43 Analytic formulae for the partial elasticities are found in Kakwani (1993). On the conceptual distinction between the partial and total elasticities in this context, see Ravallion (2007).

15
hence whether impediments to changes in the mixture of banks and markets hamper economic development (Beck and Levine, 2002; Demirguc-Kunt and Maksimovic, 2002; Levine, 2002).

In this paper, we evaluate empirically the changing importance of banks and securities markets as economies develop. In particular, we focus on assessing whether economies increase their demand for the types of services provided by securities markets relative to the services provided by banks as countries grow. We do this by testing whether the economic development “returns” to improvements to both bank and securities market development change as economies grow. At a more exploratory level, we also examine whether each level of economic development is associated with an “optimal” financial structure, such that deviations from this optimum are associated with lower levels of economic activity. We use data on 72 countries, over the period from 1980 through 2008, and we aggregate the data in 5-year averages (data permitting), so that we have a maximum of six observations per country. We use several measures of bank and securities market development, including standard indicators such as bank credit to the private sector as a share of gross domestic product (GDP), the value of stock market transactions relative to GDP, and the capitalization of equity and private domestic bond markets relative to GDP.

The primary methodological contribution of this paper is using quantile regressions to assess how the sensitivities of economic activity to both bank and securities market development evolve as countries grow (Koenker and Basset, 1978). Ordinary least squares (OLS) regressions provide information on the association between, for example, economic development and bank development for the “average” country, the country at the average level of economic development. But, quantile regressions provide information on the relationship between economic activity and bank development at each percentile of the distribution of economic development. Thus, we assess how the associations between economic development and both bank and securities market development change during the process of economic development.

Besides confirming that both banks and securities markets become larger relative to the size of the overall economy as countries grow, the paper’s major findings are (1) the sensitivity of economic development to changes in bank development _decreases_ with economic development, and (2) the sensitivity of economic development to changes in securities market 3
Leone and 93% in Uganda cannot afford the fees for checking accounts, given their annual income and assuming that they cannot spend more than 2% of household income on financial transaction account charges. The fees on savings accounts are in general less restrictive. Approximately, 33% of the population in Malawi and Uganda and 17% of the Bolivian population cannot afford the fees and charges associated with a savings account.

While these computations are rough estimates, they are most likely conservative estimates of the share of the population that cannot afford these services, as we do not take into account the costs imposed by minimum balances, restricted locations to access services, and documentation requirements.

# **6. What explains banking barriers across banks and countries?**

This section explores which bank and country-level characteristics can explain the wide variation of barriers across countries. Theory suggests the importance of transaction costs, risk mitigation tools and market structure for the cost of financial services and thus barriers to banking. Theory also suggests that different business strategies, the size of a bank and its ownership structure might impact its cost structure and, thus, the barriers imposed on customers. We therefore consider whether the size, business orientation and ownership of the banks are associated with barriers and explore the role of physical infrastructure, costs of doing business, the contractual and informational frameworks, banking sector market structure, competitiveness, openness, and transparency of the economy in explaining cross-country variation in banks’ barriers. While bank-level data are from Bankscope, country-level variables are drawn from different databases.<sup>15</sup> Appendix Table A.2 shows definitions and sources for the explanatory

> 15 Bank ownership data are from Micco, Panizza and Yañez (2007), based on cleaned Bankscope data.

19
27 whether the tax incentives were important in their decisions. In a study of Mexico (Stanford and Christensen, 1959; quoted in ST), 14 of 24 firms surveyed said they would have invested the same amount even in the absence of the tax incentives. Nine firms reported that the "probably" would have invested the same amount, while only one firm attributed an important role for the tax incentives. Of 40 Pakistani firms surveyed, 8 reported that the tax incentives were important factors in their decision (Azhar and Sharif, 1974; ST). The equivalent figures for Jamaica was two firms out of 55 surveyed (Chen-Young, 1967; ST). As Shah and Toye (1980) note, bias in questionnaires can go in two directions. The first is that businessmen claim that the tax effects are no important, in order to extract more concessions from the government. The second, more likely, bias is that businesses may overstate the impact of the incentives to convince the government to retain such "effective" tax instruments.

A third method is to calculate what profits would have been had there been no investment incentive, and compare that measure with a "critical minimum rate of profit which firms require." If the profit rate without incentives is below that rate, but the profit rate with incentives is above that rate, the incentive package is judged to have been a success. Tax incentives in Pakistan (Azhar and Sharif, 1974; Kemal, 1975) and in Colombia (Billsborrow and Porter, 1972) were estimated to have increased investment by 20 percent, 30 percent, and 10 percent, respectively (the two Pakistan studies used different assumptions).