rafmacalaba/datause-encoder-data huggingface dataset:
Other quality indicators, whose availability is more limited, tell a similar story. Calderón and Servén (2009) show that telephone faults per 100 main lines declined considerably across Latin America -- to a regional median of 5 in 2001-6 from 52 in 1991-6. In addition, not only the number of phone faults declined, but also the percentage of faults cleared next day rose considerably -- from 55% in 1991-6 to approximately 87% in 2001-6.

_Power_ . The percentage of transmission and distribution losses relative to total output offers a rough measure of the efficiency of the power sector. However, observed power losses include both ‘technical’ losses, reflective of the quality of the power grid, and pilferage (i.e. power theft), and unfortunately it is virtually impossible to disentangle the relative importance of the two. The quality measure of power reported in Table 1 is based on these losses, rescaled to lie between 0 and 1, so that higher values indicate higher quality.

By this measure, the table shows that the quality of electric power services actually deteriorated in Latin America over the 1980s and 1990s. The same happened among middle-income countries. In contrast, quality showed a steady improvement in East Asia and industrial countries, so that Latin America’s gap vis-à-vis these highperforming regions has widened over time.<sup>8</sup> _Land Transportation._ The only quality indicator widely available for this sector is the percentage of paved roads in the total road network.<sup>9</sup> Table 1 shows that, by this measure, the LAC region lags the other country groups by a huge margin. By 2001-5, less than a quarter of the road network (25 percent) was paved in the typical Latin American country, far behind the non-LAC MIC norm (close to 50 percent), and much further behind East Asia and the industrial-country norm, both close to 100 percent. Not only the level of the indicator was low in Latin America; it also grew at a very slow pace -- from

> 8 In addition to transmission and distribution losses (sometimes considered a measure of operating performance), the World Bank Report “ _Benchmarking data of the Electricity Distribution Sector in Latin America and the Caribbean 1995-2005_ ” computes quality measures such as the frequency and duration of interruptions of electricity services per subscriber (number and hours, respectively). However, we lack information over a longer time horizon, as well as for countries in other regions.

> 9 An alternative measure of road quality is the international roughness index (IRI), developed in the 1980s, and used to define a key characteristic of the longitudinal profile of a traveled wheel-track (UMTRI, 1998). The calculation of this index is based on the ratio of a standard vehicle's accumulated suspension motion (say, in millimeters) divided by the distance traveled by the vehicle during the measurement (say, in kilometers).  However, the information is sparse and has limited cross-country and time-series-coverage.

8
Countries where financial markets are not sufficiently active and liquid should not rely on potentially misleading market valuations. The use of book values should then be allowed but unmatched liabilities of individual institutions should be subject to more onerous technical and capital reserves.

Effective management of the longevity risk in fixed nominal and real annuities as well as escalating annuities requires access to long-duration assets. For the more uncertain tail end of the age distribution, annuity providers should be encouraged to resort to global reinsurance. This will require removal of any asset localization requirements. The use of longevity bonds and longevity derivatives could be encouraged when these instruments become well established in global markets.

The risk-sharing arrangements of variable annuities whereby longevity risk is shared among annuitants offer several advantages. However, the offer of variable annuities requires a high level of transparency and integrity and is best organized through a centralized structure along the lines discussed above.

The introduction of government guarantee schemes covering all types of retirement products merits serious consideration. The government guarantees could emulate evolving practice in deposit insurance schemes, including upper limits on the amounts insured and a reasonable amount of coinsurance by pensioners in order to minimize the possible loss of market discipline at the point of purchase. The potential cost of government guarantees should be estimated and such estimates should be used to determine risk-based premiums on annuity providers.

The high volatility of financial markets, which was recently underscored by the 2008 global crisis, highlights the need for a safety net to cover the accumulation phase at the point of retirement. The offer of a lifetime government guarantee that retirement savings will earn a specified minimum real rate of return deserves special study.

The conditions and other particulars of two options are worth considering: either a minimum zero real rate of return or a specified fraction of the long-run rate of return of specified portfolios. In either case, asset allocation strategies during the accumulation phase will need to follow prescribed principles and patterns to discourage moral hazard and prevent gaming of the guarantees by retiring workers.

In addition, the authorities should compile a comprehensive database of retirement products and should undertake educational programs to expand financial literacy and improve understanding of the main features, cost and performance of different retirement products.

6
Policy Research Working Paper 6937

# **Abstract**

The dynamism of air traffic markets in the Middle East obscures the persistence of restrictions on international competition. But how important are such restrictions for passenger traffic? This paper uses detailed data on worldwide passenger aviation to estimate the effect of air transport policy on international air traffic. The policy variable is a quantitative measure of the commitments under international agreements. The paper analyzes, for the first time, not only bilateral agreements, but also plurilateral agreements such as the one between Arab states. The analysis finds that more liberal policy is associated with greater passenger traffic between countries. Higher traffic levels appear to be driven primarily by larger numbers of city pairs being served, rather than by more passengers traveling along given routes. To demonstrate the quantitative implication of the estimates, two liberalization scenarios in the Middle East are evaluated. Deepening the plurilateral agreement among Arab states would lead to a 30 percent increase in intraregional passenger traffic. Widening the agreement to include Turkey would generate significantly larger gains because current policy vis-à-vis Turkey is much more restrictive.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cristea@uoregon.edu, rhillberry@worldbank.org, and amattoo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
||**Table A4: In**|**teraction term**|**results for th**|**e Herfindahl-**|**Hirschman Ind**|**ex**||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Herfindahl<br>-Hirschman Index|0.748|0.889*|0.972*|1.071**|1.153**|0.772**|0.951**|
||(0.508)|(0.505)|(0.552)|(0.519)|(0.545)|(0.390)|(0.380)|
|Exports (%, cell|0.106**|0.107**|0.088*|0.084*|0.098*|0.099*|-1.000**|
|average)||||||||
||(0.052)|(0.051)|(0.051)|(0.050)|(0.054)|(0.053)|(0.504)|
|Herfindahl-|<br>-10.006***|<br>-10.794***|<br>-11.705***|<br>-11.128***|<br>-15.403***|<br>-10.67***|<br>1.930|
|Hirschman Index|(3.823)|(3.533)|(3.618)|(3.607)|(4.214)|(3.836)|(7.299)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.120**|
||||||||(0.055)|
|No. of workers|||||||-4.336**|
|(logs)*Herfindahl-<br>Hirschman Index||||||||
||||||||(1.726)|
|No. of Workers||0.544**|0.714***|0.749***|0.906***|0.917***|<br>1.318***|
|(logs)||||||||
|||(0.244)|(0.257)|(0.279)|(0.291)|(0.291)|(0.332)|
|GDP per capita||16.228***|16.436***|18.519***|18.865***|8.307|5.893|
|(logs)||||||||
|||(4.853)|(4.915)|(4.867)|(5.196)|(5.356)|(5.480)|
|Age of Firm (logs)|||<br>-0.494|<br>-0.886***|<br>-0.656*|<br>-0.806**|<br>-0.800**|
||||(0.358)|(0.337)|(0.359)|(0.355)|(0.357)|
|Foreign Ownership|||<br>-0.881|<br>-0.103|<br>0.125|<br>0.163|<br>0.218|
|(proportion)||||||||
||||(0.846)|(0.883)|(0.970)|(0.962)|(0.960)|
|Female Ownership||||<br>6.182***|<br>5.978***|<br>5.939***|<br>5.949***|
|Y:1 N:0||||||||
|||||(0.615)|(0.652)|(0.652)|(0.650)|
|Skills Obstacle (0-4)||||<br>0.609***|<br>0.676**|<br>0.646**|<br>0.659**|
|||||(0.226)|(0.267)|(0.269)|(0.267)|
|How Much Of An||||<br>-0.036|<br>0.017|<br>-0.006|<br>-0.004|
|Obstacle: Transport?||||||||
|||||(0.193)|(0.209)|(0.209)|(0.209)|
|Firm Purchased||||<br>-0.927*|<br>-1.066*|<br>-1.041*|<br>-0.987*|
|Fixed Assets Y:1||||||||
|N:0||||||||
|||||(0.522)|(0.557)|(0.556)|(0.557)|

56
|Trinidad &<br>Tobago|-2.598|-3.251|0.292|0.362|-0.001|
|---|---|---|---|---|---|
|Kuwait|-2.687|-3.682|0.578|0.458|-0.041|
|Congo|-3.432|-2.638|0.015|-0.806|-0.004|
|Kazakhstan|-3.715|-4.443|0.005|0.713|0.010|
|Azerbaijan|-3.919|-4.371|0.193|0.237|0.021|

Notes: Entries are expressed in percentage points. The fitted rate of growth in SE is expressed as a deviation from the sample mean. SE stands for school enrollment. Only the statistically significant regressors are used.

Turning now to the education case, we find a somewhat different pattern than in the case of child mortality (see Table 7). The biggest difference relates to the role of initial level of enrollment. In the education case, this is by far the biggest contributor to the gap than any other independent variable for the majority of oil rich countries (10 of the 14 countries in the table). In all but three cases, the role of this factor is negative: the initial level of school enrollment was so high for the countries in question in 1980 that it slowed down the rate of subsequent increase in enrollment. However, in the cases of Angola, Nigeria and Indonesia, the initial enrollment in 1980 was so low that it imparted great momentum to subsequent increases.

Initial enrollment was not important in explaining the gap between actual and predicted changes in the cases of Algeria and Indonesia. In the case of Algeria, public spending on education and undernourishment were the most important contributing factors whereas in the case of Indonesia, undernourishment was the single most important determinant in a positive direction. Once again, some oil-rich countries performed better than others. If we split the non-high-income OECD sample into quintiles of rate of growth in school enrollment, we find that two oil-rich countries (Nigeria, Angola) are in the top quintile and five (Trinidad and Tobago, Kuwait, Congo, Kazakhstan, and Azerbaijan) in the bottom quintile. The differences in fitted growth rates of school enrollment across oil-rich countries were mainly driven by conditional convergence and, to a lesser extent, public spending on education, undernourishment and economic growth.

# **G. Concluding Observations**

We have attempted in this paper to identify the policy and institutional factors that best account for the differences in development performance, measured in terms of the decline in child mortality and the increase in school enrollments, among hydrocarbon rich countries. Modeling considerations introduce a legacy factor into our assessment. This factor is the initial level of child mortality and secondary school enrollment in 1980 in each country. Our regression analysis suggests that this legacy factor is a very important determinant in both cases and it operates in a way that confers a momentum 19
# **Table 8: Comparison of International Long Distance Tariffs (Telecom),** 1999

|_ICountry_|_From7US_|_To US_|_Ac._<br>_at_|_Liealztio_<br>_'inn'l_|_CalBk_|_One_<br>_i_|
|---|---|---|---|---|---|---|
|Australia|$0.25|$0.14|$0.42|Competition|Yes|na|
|Hong Kong|<br>$0.31|$0.24|$0.80|Competition|na|na|
|**India**|**$0.66**|**$1.27**|**$1.28**|**Monopoly**|**No**|**49**|
|Singapore|<br>$0.35|$0.51|$0.85|Monopoly|Yes|49|
|Peru|$0.55|$0.71|$1.13|Monopoly ends in 99|<br>No|100|

_Note:_ 1 .Tariffs are per minute charges in the residential international long distance market and are subject to change. "From US" tariffs refer to the unweighted average of international calling plan rates offered by Sprint, MCIWorldCom, and AT&T (monthly fees are not considered). "To US" tariffs represent the rate charged by the foreign monopolist or, in case of competition, refer to the average tariff charged by a random sample of foreign operators. In case of peak/off-peak tariff discrimination, the time-weighted average tariff was used. Foreign operators' tariffs were converted into US dollars using market exchange rates as published by the Financial Times.

2. Accounting rates are from the US Federal Communications Commission.

3. Information on the remaining variables comes from the ITU.

_Source:_ Research in progress by Fink and Mattoo, World Bank, 1999 38
This augmented specification controls even more tightly for geographical distance as a determinant of technology diffusion, finding continued and strong evidence that differential technology transfer from the United States matters. Similar results are also found when including an equivalent set of fixed effects that partition on GDP/capita of countries.

Additional estimations also test including the three Rybczynski effect controls discussed earlier. In the unweighted estimations, the inclusion of these controls does not materially influence the instrumented elasticities. All three elasticities are in the range of 1.5-2.1 and are statistically significant at a 5% level or higher. In the weighted estimations, these controls have a larger impact. For the non-production share control, the weighted elasticity is 2.5 (1.7), while estimations with the other two controls have invalid first stages. Thus, the conclusions regarding heterogeneous technology transfer, productivity growth, and exports for the weighted estimations need to be cautious to acknowledge that these effects are not well-distinguished from a generalized Rybczynski effect operating inside of the manufacturing sector itself. In unweighted estimations, these effects are better distinguished.

# Extended IV Results: Instrument Design

Table S7a reports additional robustness checks related to the instrument design, with Table S7b again providing reduced-form results. Columns 2-4 consider variations in the city-industry technology trend term in Mit<sup>koutlinedinthemaintext,whileColumns5-9considervariations</sup> in the cross-sectional distributions used to weight US cities for countries.

Column 2 begins by showing similar results when using the total technology trends that encompass all ethnic groups in model Mit<sup>k, rather than just the Anglo-Saxon ethnic trend.Column</sup> 3 goes the opposite direction by emphasizing even further isolated Anglo-Saxon-led developments. A possible concern for achieving identification with the Anglo-Saxon ethnic technology trend is that it could be very tightly integrated to migrant contributions and their inputs into the innovation process, ranging from the generation of breakthrough ideas to serving as research assistants. While it is impossible to fully disentangle all of these features, patent citations provide useful traction for considering knowledge collaboration and information exchange among peers. Column 3 reduces the emphasis on Anglo-Saxon-invented patents when the patents cite a substantial number of recent patents with non-Anglo-Saxon inventors in the United States; in contrast, it emphasizes even more the Anglo-Saxon-invented patents where the citations are mostly going to recent work by other Anglo-Saxon ethnic inventors.<sup>4</sup> These estimations deliver

> 4More specifically, the procedure first isolates the “backwards” citations that each patent makes to patents

11
_Step 2_ : Using the data in survey round 1, estimate equation (1) and obtain the distributions of the predicted parameters<sup>βˆ</sup> 1<sup>,μˆ</sup> 1<sup>, andεˆ</sup> 1<sup>.</sup> _Step 3_ : Take a random draw from the normal distributions of the predicted parameters<sup>μˆ</sup> 1<sup>, and</sup> ~ˆ<sup>εˆ</sup> 1<sup>obtained in step 1 and denote these byμ~ˆ and</sup> ε 1 . Then using these predicted parameters and the data in survey round 2, estimate the consumption level for each household in round 2 as follows _Step 4_ : Estimate the quantity in (5) and the first term on the right hand side in (6) (i.e., _V_ ( _P_ ˆ2 |<sup>_x_</sup> 2 ) ), using the given poverty line z1 in survey round 1 and<sup>_y_ˆ</sup> 12<sup>obtained from Steps 1 and</sup> 2 above.

_Step 5_ : Repeat steps 3 to 4 S times and save the data with all S simulations. Take the average of the estimated quantity in (5) over the S simulations to obtain the estimate of poverty rate in survey round 2. (We use S= 1,000 in our simulations below.) ˆ _Step 6_ : Take the average of the estimated quantity for _V_ ( _P_ 2 |<sup>_x_</sup> 2 ) over the S simulations to obtain the estimate of the first term on the right hand side in (6). Obtain the estimate for the second term on the right hand side in (6) using the simulated dataset and add this estimated quantity to the estimate for the first term to obtain the estimate of the variance of poverty rate in survey round 2.

_Step 7 (recommended but optional)_ : Provide additional robustness checks using the empirical distributions of the error terms or other modelling options discussed above.

# **IV. Imputation Combining Surveys of Different Designs to Update Poverty Estimates**

While it may not seem unreasonable to make the assumption that the two rounds of survey under consideration are representative of the population and consequently produce comparable estimates (Assumption 1), this assumption is restrictive. Contexts where one survey has 27
Table 3: Testing for the reference <u>groups</u> hypothesis <u>(assets)</u>

||(1)<br>Depende|(2)<br>nt variable:|(3)<br> perceived|(4)<br>decile in th|(5)<br>e income d|(6)<br>istribution|
|---|---|---|---|---|---|---|
|Objective decile|0.188<sup>∗∗</sup>|||0.125<sup>∗∗</sup>|0.116<sup>∗∗</sup>|0.105<sup>∗∗</sup>|
|(asset index)|(0.0101)|||(0.0164)|(0.0186)|(0.0174)|
|Decile in Age/Educ.||0.160<sup>∗∗</sup>||0.0665<sup>∗∗</sup>||0.0462<sup>∗∗</sup>|
|ref. group||(0.00665)||(0.0127)||(0.0170)|
|Decile in Region/Educ.|||0.170<sup>∗∗</sup>||0.0752<sup>∗∗</sup>|0.0397<sup>∗∗</sup>|
|ref. group|||(0.00702)||(0.0138)|(0.0175)|
|_Indiv.Controls_|Yes|Yes|Yes|Yes|Yes|Yes|
|_CountryFE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Y earFE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|60987|57964|57964|57964|57964|57964|
|_R_<sup>2</sup>|0.177|0.175|0.178|0.183|0.183|0.184|

Standard errors clustered at the country-year level in parentheses. Individual controls include age, gender, religion and household composition of the respondent ∗ _p_ < 0 _._ 10, ∗∗ _p_ < 0 _._ 05, ∗∗∗ _p_ < 0 _._ 01

# **3.2 Subjective ranking as a measure of** **_well-being_**

An alternative explanation to the bunching in the middle is offered by changing the interpretation of the Cantril’s ladder question. In this alternative case, the underlying variable measured by the responses is not anymore of a distributional nature, but it is of a subjective well-being nature. Individuals, rather than answering in distributional terms, i.e. attempting to figure out to which part of the distribution of consumption they belong, they interpret the question in terms of a standard well-being question. Such a question usually provides a range from a minimum to a maximum intensity that respondent can choose from. In fact, in the LiTS surveys, the question asking individuals their position on Cantril’s ladder is phrased in a way that is ambiguous about whether each step should include 10% of the population. The reference to a 10-percent-sized group is explicitly made only with respect to the top step which is said to include the 10% of the richest, and the bottom step with 10% of the poorest people. It seems quite plausible that the subjective ranking lacks a direct distributional meaning and should be read more as an economic measure of well-being. In fact, this interpretation of the Cantril’s ladder has been common in a stream of the literature starting by Ravallion and Lokshin (2002), who refer to it as the Economic Welfare Question (EWQ). Bunching in the middle is less surprising for a well-being measure since this is also observed in other non-economic happiness variables .

This section will start by showing that, indeed, there is a similarity between the distribution of individuals’ subjective ranking and that of other well-being measures such 14
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Johnson and Papageorgiou, 2020, What remains of cross-country convergence?, JOURNAL OF ECONOMIC LITERATURE, https://www.aeaweb.org/articles?id=10.1257/jel.20181207&&from=f King, Robert G., Plosser, Charles I., Rebelo, Sergio T. , 1988, Production, growth and business cycles: I. The basic neoclassical model, Journal of Monetary Economics, 21 (2–3) (1988), pp. 195-232 Kremer, Willis, Yang, 2021, Converging to convergence, NBER Macroecon. Annu., 36 (2021), - - <u>https://bfi.uchicago.edu/wp content/uploads/2021/11/BFI_WP_2021 136.pdf</u> Lucas, 1988, On the mechanics of economic development, J. Monetary Econ., 22 (1988), pp. 3- 42 Lakner and Milanovic, 2015, Global income distribution: from the fall of the Berlin Wall to the Great Recession, World Bank Econ. Rev., 30 (2) (2015), pp. 203-232 Maddison, A. 2001. Development Centre Studies The World Economy A Millennial Perspective: A Millennial Perspective. OECD Publishing.

Mahler, D. G., Yonzan, N., Lakner, C., Castaneda Aguilar, A. and Wu, H., 2021. Updated estimates of the impact of COVID-19 on global poverty: Turning the corner on the pandemic in 2021?, World Bank Data Blog, https://blogs.worldbank.org/opendata/updated-estimates- <u>impact-covid-19-global-poverty-turning-corner-pandemic-2021</u> 33