rafmacalaba/datause-encoder-data huggingface dataset:
|Line of business|F.<br>Gross provisions for|G.<br>Net retained provisions|
|---|---|---|
|Motor vehicle liability insurance|15,000|13,000|
|Marine, aviation and transport insurance|1,000|400|
|Fire and otherdamage to property insurance|1,000|500|
|Generalliabilityinsurance|3,000|2,700|

||H=A<br>Volume measure<br>premium|l=G6<br>Volume measure<br>reserves|J=H+1<br>Volume measure<br>V_lob|
|---|---|---|---|
|Motor vehicle liability insurance|19,540|13,000|37,540|
|Other motor insurance|4,753|1,500|6,253|
|Marine, aviation and transport insurance|2,260|400|2,660|
|Fire and otherdamage to property insurance|8,248|500|8,748|
|Generalliabilityinsurance|4,778|2,700|7,478|

||__|| <br>StdDev_prem<br>without NP Factors|orton <br>NP Factors||sane] <br>StdDev with NP<br>Factors|soho |<br>StdDev_res|acta<br>StdDev_lob|
|---|---|---|---|---|---|
|Other motor insurance|7.0%|72.1%|5.0%|10.0%|5.4%|
|Marine, aviation and transport insurance|17.0%|60.3%|10.3%|14.0%|9.9%|
|Fire and other damage to property insurance|10.0%|60.6%|6.1%|11.0%|6.1%|
|Generalliabilityinsurance|15.0%|77.4%|11.6%|11.0%|10.0%|
# **1.  Background**

With a GDP per capita of around $100, Ethiopia is one of the poorest countries in the world. Life expectancy, literacy rates, and other indicators of human development are all extremely low. Spells of drought, with resulting famines, have a strong impact on the whole economy and have led over the past three decades to a high degree of output and income volatility (see Figure 2). Changes in rainfall have a substantial impact on consumption growth, which appears to persist for several years (Dercon (2004)). Domestic savings, at slightly above 2 percent of GDP in 2002, are too low to meet the country’s investment needs. As a result, foreign borrowing and foreign aid increased significantly during the 1990s.

After a long period of continuous deterioration due to detrimental economic policies pursued during the communist regime (1974-91), the downward trend in real income per capita was reversed in the 1990s. Real GDP grew at an average of 3.6 percent per year over the 1992-2002 period. This led to an increase of 1.5 percent in income per capita. By the end of the 1990s, the level of income was back to the all time high achieved in the early 1970s. As a result, poverty declined during the 1990s. However, the magnitude of this decline remains a matter of debate. Official sources, based on household surveys, indicate that the poverty headcount index was 44.2 percent in 1999/00 for the country as a whole, down from 45.5 percent in 1995/96, whereas inequality changed relatively little, with a Gini coefficient of 0.29 for 1995/96 and 0.28 for 1999/00 (see Federal Democratic Republic of Ethiopia (2002)). By contrast, Bigsten et al. (2003), in a study focusing on the period 1994-97 but with surveys of smaller size, found very different results. They decomposed changes in poverty into growth and redistribution components. They found that poverty declined from 41 percent to 36 percent for the country as a whole. But the increase in real per capita income was to some extent counteracted by a worsening of income distribution, with an increase in the Gini coefficient from 39.2 percent in 1994 to 43.5 percent in 1997. Despite these differences, it is clear that poverty remains high.

26
# **3 Data**

We employ a set of nationally representative cross-sectional Living Standards Measurement Surveys (LSMS) from 1999 and 2002 administered by the Bolivian National Institute of Statistics<sup>5</sup> . The surveys include a comprehensive socioeconomic module, Bolivida receipt information at the individual level, and detailed expenditures for all members in the household who are at least six years old<sup>6</sup> . The sample comprises all school-age children who live in households with at least one person in the age range of 56 to 73<sup>7</sup> . The school-age range considered is 6- to 18-years as the minimum legal working age in Bolivia is 14. The sample excludes households that belong to the top 1 percent of the educational expenditures distributions. The total sample includes 3,645 school-aged children and 1,038 eligible elders distributed among 1,915 households. The surveys employ a stratified two-stage sampling whose sampling frames are based on census data<sup>8</sup> .

Table 1 reports summary statistics. On average, families living with an eligible elder are not substantially different from those living with a soon-to-be eligible elder, factors that have not changed significantly during our period of study. Eligible households are slightly smaller and have slightly more children than non-eligible ones, even though both have nearly the same number of school-age children, especially in later years. However, non-eligible and eligible households have different family characteristics. Particularly, a higher percentage of non-eligible households have both the father and mother living in the household, and they are of parental age. In addition, the parents’ years of schooling and the children’s education expenditures are higher for eligible households. Finally, both eligible and non-eligible families allocate approximately 10 percent of their total income to children’s educational expenditures<sup>9</sup> .

> 5 INE website: http://www.ine.gob.bo.

> 6Approximately 18 percent of households are three-generation extended families.

> 7The preferred sample is defined based on the bandwidth used for the estimation of the regression discontinuity model. Robustness tests were conducted using slightly larger and smaller samples.

> 8The sampling frame for the baseline survey was constructed based on the 1992 census enumeration areas list. The follow-up survey uses an updated sampling frame that was constructed upon revised cartographic information compiled for the 2001 census. While this may raise questions regarding the bias of the estimates, we perform robustness tests that show no statistical effect of this sampling framework. These results are available upon request.

> 9We also observe that the largest disparities appear in human capital among ethnic groups as well as between urban and rural populations where educational gender gaps among indigenous people are particularly large.

6
estimators show a positive partial correlation between FCS in China and India and those in the rest of the world.

The second panel of the table shows the results using the U.S. sector data. As with the OECD and UNCTAD data, there is an unconditional positive correlation between Chinese FCS and FCS in LAC in the U.S. data. In contrast, here we cannot reject the hypothesis that the overall impact of India FCS in LAC is different from zero. These results are robust to the use of the alternative estimators. The impact of China in the manufacturing sector also appears to be either positive or not significant. The evidence of the impact of India in the manufacturing sector is somewhat mixed. Indian FCS in the manufacturing sector has a negative and significant coefficient in the Poisson estimation. However, this finding is not robust across estimators. We find no effect of this variable using the OLS and an overall positive effect using the Negative Binomial estimator.

Table 2 reports the results from the fully specified KCM, based on the OECD and UNCTAD data. Again, the bottom panel contains the point estimates of the China and India effects on LAC FCS. The first column shows the OLS results for our benchmark model. The results for most of the control variables are similar to those in the KCM literature and consistent with the theory. GDPSUM and GDPDIF have the expected signs and are statistically significant. The coefficient in the skill difference is negative, which is contrary to the theory but consistent with other empirical findings (e.g., Blonigen et al 2003). We do not find a different effect for the negative skill difference. Both measures of investment cost are negative and statistically significant. The distance and the trade costs variables also have a negative coefficient and are statistically different from zero.

12
# **China, India, and the Future of the World Economy: Fierce Competition or Shared Growth?**

by Betina Dimaranan, Elena Ianchovichina and Will Martin<sup>∗</sup> World Bank _JEL: F11, F12, F43_

> ∗ Will Martin is Lead Economist, Development Research Group at the World Bank; his email address is <u>wmartin1@worldbank.org. Elena Ianchovichina is Senior Economist, Economic Policy and Debt Department at the</u> World Bank; her email address is <u>eianchovichina@worldbank.org. Betina Dimaranan is Research Fellow, IFPRI-MTID;</u> her email address is <u>b.dimaranan@cgiar.org. We would like to thank Alan Winters, Shahid Yusuf, Yasheng Huang,</u> T.N. Srinivasan, and participants in the 10<sup>th</sup> Annual Conference on Global Economic Analysis at Purdue University for helpful comments on earlier drafts.  The findings, interpretations and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
_NTMit_<sup>_out_, corresponding to the share of HS-10 digit products subject to at least one NTM</sup> within the corresponding five digit industry _i_ in the manufacturing plant-level data, in year _t_ (see sections 2.4 and Appendix B.1 for details on the manufacturing data).

Based on the output NTM variable, we compute the input NTM measure, _NTMit_<sup>_in_.</sup> To that end we construct input-output (IO) tables from the plant-level data. For each five digit industry _i_ , input NTM is defined as the weighted sum of output NTM in each five digit supplying industry _k_ : where _wik_ is the share of _k_ in total input value in industry _i_ . Our approach results in much more granular IO tables to construct upstream linkages, compared with the trade reforms literature (Javorcik, 2004; Bourlès et al., 2013). In particular, the weights _wik_ are obtained from detailed base year information on nine digits raw inputs used by each plant (see sections 2.4 and Appendix B.1). While this data would allow us to compute plant level input NTM variable, we prefer to aggregate the plant level information at the five digit level to minimize measurement error.<sup>12</sup> The results of the analysis below are qualitatively similar when using plant- instead of industry-specific input NTMs although the estimates are less precise, consistently with the larger noise in the data (results available from the authors upon request).

The evolution of output NTM in our sample is depicted by the solid line in Figure 1. The figure suggests a widespread and increasing incidence of NTMs for Indonesian manufacturing plants. Over the years of the sample, the share of products subject to NTM went from 60 to almost 80 percent. The increasing incidence of NTMs for Indonesian plants is also reflected in our measure of input NTM. In Figure 1, the dashed line represents the average share of intermediate inputs subject to at least one NTM. The share of inputs used by plants subject to NTMs went from less than 30 percent in 2008 to 60 percent in 2015. The higher incidence of output NTM presented in Figure 1 suggests that final consumption goods are more likely to be subject to NTMs than intermediate goods, although the gap shrinks considerably over time.

> 12The input data at the plant level appears to be quite noisy with several examples of plants changing their input mix frequently over the years. The aggregation across plants at the five digit industry allows to smooth this variability.

10
The current paper attempts to document recent developments in African telecommunications and examine what factors could facilitate network development in the region. Particular attention will be paid to the price structure, which has rarely been investigated in the existing literature. Aggregate data on penetration and certain standardized prices, e.g., costs of a three minute local call, are fairly available, but the fact that the pricing in the telecommunications sector is highly complex is overlooked and makes it difficult to interpret actual telecommunications prices. Moreover, it remains unknown what type of price discrimination is used on the operational level. The analysis relies on new data on the tariff structure from 45 fixed-line and mobile operators in 18 African countries, rather than using country-level aggregate data.<sup>4</sup> It finds that discriminatory pricing facilitates network expansion. A policy implication is straightforward: price liberalization could be conducive to developing the telecommunications network. Some countries in Africa are still imposing certain price control on telephone operators. It is also shown that the implied price-cost margins (PCM) are very high in Africa’s telecommunications markets. This implies that there is a good potential for counting on private telephone companies expanding telecommunications access. However, the authorities still need to address a remaining policy issue: how to ensure reciprocal access between operators at reasonable costs.

In the African context, understanding how the telecommunications sector has been developing is particularly important to accelerate economic growth and encourage vigorous private investment in the region. The empirical growth literature indicates that the contribution of telecommunications infrastructure services to GDP is most substantial among infrastructure sectors (e.g., Easterly and Levine, 1997; Esfahani and Ramírez, 2003; Calderón and Servén, 2004).<sup>5</sup> In addition, telecommunications is deemed one of the infrastructures that can be led primarily by the private sector. Like other regions, the

> 4 My sample includes Botswana, Ghana, Kenya, Lesotho, Malawi, Mauritius, Namibia, Niger, Nigeria, Rwanda, Senegal, Seychelles, Sierra Leone, South Africa, Sudan, Tanzania, Uganda, and Zimbabwe.

> 5 Easterly and Levine (1997) quantify the positive impact of the stock of infrastructure—including telecommunications—on economic development. Esfahani and Ramírez (2003) and Calderón and Servén (2004) address the same issue using the structural estimation and GMM techniques, respectively.

4
# **Annex 1: Comparison of employment and unemployment indicators using ILO and NSO definitions**

There is difference in employment rate and in unemployment rate according to the International Labor Organization (ILO) definitions and the Philippines National Statistics Office (NSO) definitions. This annex provides statistics using both definitions for nuance.

Employment rate is defined as the proportion of the working-age population that work or have a job to return during the reference period. The difference in employment (to working age population) rates between ILO and NSO lies in the difference between their definitions of working age population. ILO defines 15 to 65 years old as working age while NSO defines 15 years and above. In this study, the NSO employment rates for the Philippines as well as for each of the 17 regions are slightly lower than the ILO employment rates.

Larger difference lies in ILO and NSO unemployment to participation rates, which results not only from the difference in working age definitions but also to a larger extent from the difference in unemployment definitions. ILO defines unemployment as the working age individual who does not work, is available for work and is actively looking for work. NSO employs a broader definition of unemployment as the proportion of labor force that does not work, is available for work and (1) is actively looking for work, or (2) is not looking for work because of following reasons: (i) belief that there is no job available (discouraged worker); (ii) awaiting results of previous job application (labor in transition); (iii) temporary illness (temporarily disabled); (iv) bad weather; and (v) waiting for job recall (laid-off).

The national unemployment rate by the NSO definition is twice that by the ILO definition (Table A1). This suggests that the NSO unique unemployment, including the discouraged, temporarily disabled, laid-off, in transit, and not looking for work because of bad weather, accounts for 50 percent of total NSO unemployment. A higher ratio of the unemployed falls into the NSO unique unemployment in some regions than others. For example, NSO unique unemployment accounts for 70 percent of NSO unemployment in ARMM, while only 37 percent in NCR. The trends of changes in ILO unemployment and the NSO unemployment also differ across regions. For instance, while the ILO unemployment rate in Western Visayas and in Eastern Visayas did not change significantly over the period, the NSO unemployment increased in Western Visayas and decreased in Eastern Visayas.<sup>15</sup>

> 15 The changes of NSO unemployment rate in Western Visayas and Eastern Visayas are both significant in statistical sense.

18
- Velez, Carlos E. and Al-Shawarby, Sherine and El-laithy, Heba, Equality of Opportunity for Children in Egypt, 2000-2009: Achievements and Challenges (August 1, 2012). World Bank Policy Research Working Paper No. 6159. Available at SSRN: http://ssrn.com/abstract=2127059

- Wendelspiess Chávez Juárez, Florian. "Measuring Inequality of Opportunity with Latent Variables." Journal of Human Development and Capabilities (2015): 1-14

19
Lockheed, M.E., D.T. Jamison and L.J. Lau (1980), "Farmer Education and Farm Efficiency: A Survey," Economic DeveloDment and Cultural Chanoe, 29: 37-76.

McMahon, W.W. (1987), "Expected Rates of Returns to Education," in G. Psacharopoulos, ed., Economics of Education: Research and Studies, Oxford: Pergamon Press, 187-196. McMahon, W.W. (1984), "The Relation of Education and R & D to Productivity Growth," Economics of Education Review, 3: 299-313.

Mincer, J. (1974), Schooling. ExDerience and Earnings, New York: Columbia University Press.

Mincer, **J.** (1979), 'Human Capital and Earnings," in D. M. Windham, ed., Economic Dimensions of Education, Washington, D.C.: National Academy of Education, 1-31.

Psacharopoulos, G. (1985), "Returns to Education: A Further International Update and Implications," The Journal of Human Resources, 20: 584-604.

Psacharopoulos, G. (1987), "Earnings Functions," in G. Psacharopoulos, ed., Economics of Education: Research and Studies, Oxford: Pergamon Piess, 218-223.

Psacharopoulos, G. and A.M. Arriagada (1986), The Educational Attainznent of the Lao Force: An International ComDarison, Discussion Paper No. 38, Education and Training Series, Washington, D.C.: The World Bank.

Schultz, T.W. (1961), "Investment in Human Capital," American Economic Review, 51: 1-17.

Schultz, T.W. (1975), "The Value of the Ability to Deal with Disequilibria," Journal of Economic Literature, 13: 872-876.

26
**<u>Table 3. Number of Firms and Observations by Exporting Country</u>**

|||Numbe|r of Expor|ting Firms|by Expor|ting Coun|try‐Year||Number of Obs|ervations by Ex<br>|porting Country<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||Initial|Dataset||||Initial Dataset|Tim<br>Expand|e‐Series<br>ed Dataset|
||2005|2006|2007|2008|2009|2010|2011|2012|Across All Years|Per Year<br>|Across All Years<br>|
|Albania|43|81|77|74|77|90|99|85|1,164|644|5,152|
|Burkina Faso|‐|‐|129|136|109|128|131|160|1,629|1,210|7,260|
|Bangladesh|243|383|321|408|343|355|429|‐|6,584|3,680|25,760|
|Bulgaria|516|779|‐|‐|‐|‐|‐|‐|9,049|6,565|13,130|
|Bolivia|‐|241|252|245|256|244|230|213|5,515|2,352|16,464|
|Botswana|70|90|99|109|118|113|‐|‐|1,881|1,149|6,894|
|Chile|790|963|981|1,018|1,037|1,092|1,155|1,029|54,127|20,024|160,192|
|Cote d'Ivoire|‐|‐|‐|‐|310|300|332|374|5,315|3,351|13,404|
|Cameroon|‐|‐|111|138|137|‐|‐|‐|973|696|2,088|
|Colombia|‐|‐|817|756|708|‐|‐|‐|7,713|4,648|13,944|
|Costa Rica|540|690|698|707|705|747|729|627|20,838|8,671|69,368|
|Dom. Republic|‐|‐|610|469|580|480|547|475|21,891|12,633|75,798|
|Ecuador|‐|426|458|488|542|‐|‐|‐|6,932|3,831|15,324|
|Ethiopia|‐|‐|‐|542|692|799|706|652|9,437|5,964|29,820|
|Georgia|95|176|117|132|130|150|151|163|2,581|1,716|13,728|
|Guatemala|617|738|797|782|767|809|‐|‐|20,342|8,780|52,680|
|Croatia|‐|‐|243|236|235|238|254|273|4,241|1,954|11,724|
|Iran|‐|1,525|1,521|1,472|1,233|1,277|‐|‐|18,726|13,021|65,105|
|Jordan|79|110|122|125|117|133|‐|‐|1,580|1,341|8,046|
|Kenya|‐|781|600|649|683|‐|‐|‐|6,974|4,545|18,180|
|Kyrgyzstan|‐|85|93|96|134|131|117|99|2,057|1,190|8,330|
|Cambodia|‐|‐|44|35|45|‐|‐|‐|199|157|471|
|Lebanon|‐|‐|‐|402|381|388|398|401|16,001|9,658|48,290|
|Morocco|452|637|642|651|665|696|700|614|17,773|8,095|64,760|
|Madagascar|‐|‐|187|215|224|246|357|225|4,396|2,769|16,614|
|Mexico|1,522|4,236|3,508|3,604|3,693|‐|‐|‐|48,279|28,681|143,405|
|Macedonia|204|328|349|319|322|355|‐|‐|6,388|3,509|21,054|
|Mauritius|59|85|96|96|97|96|92|87|3,634|1,773|14,184|
|Malawi|‐|81|87|73|86|82|108|111|1,616|628|3,943|
|Nicaragua|150|218|217|248|245|253|240|‐|5,419|2,676|18,732|
|Pakistan|805|1,110|1,133|1,239|1,502|1,541|‐|‐|21,914|12,559|75,354|
|Peru|643|980|1,009|1,053|1,122|‐|‐|‐|18,275|9,927|49,635|
|Paraguay|‐|‐|181|193|204|196|193|186|3,349|1,549|9,294|
|Romania|346|500|312|394|464|659|775||10,998|6,443|45,101|
|Senegal|‐|‐|‐|117|121|132|142|153|1,512|1,028|5,140|
|El Salvador|157|197|193|198|220|‐|‐|‐|3,136|1,420|7,100|
|Tanzania|172|205|206|232|263|275|292||4,681|2,742|19,194|
|Uganda|‐|‐|184|164|172|232|‐|‐|2,287|1,433|5,732|
|Uruguay|146|174|182|169|190|201|218|187|5,268|2,392|19,136|
|Yemen|‐|‐|‐|49|35|28|19|19|240|167|835|
|South Africa|1,019|1,416|1,411|1,457|1,453|1,414|1,390|1,839|107,562|49,039|392,312|
|Zambia|56|83|129|104|135|180|212|‐|1,706|1,126|7,882|
|Total|8,724|17,318|18,116|19,594|20,552|14,060|10,016|7,972|494,182|249,171|1,600,559|

Note: - indicates that the country does not have data for that particular year.

32