rafmacalaba/datause-encoder-data huggingface dataset:
similar with an average absolute difference between the modalities of 4.75 percentage points. This is less than one-third of the average difference between the modalities in the questions that were more sensitive.

Furthermore, the attrition patterns are also consistent with the respondents in the internet-based surveys believing in the anonymity of the modality. Respondents were offered one question at a time and had to provide a response before being posed the next question. Overall attrition rates were not systematically different for sensitive and nonsensitive questions, which we might expect if respondents did not believe their responses were anonymous. Furthermore, attrition rates at each question were nearly identical in regions controlled by the de facto authorities in Sana’a (DFA) and the internationally recognized government (IRG) despite the fact that some of the questions were potentially more sensitive in DFA-controlled regions.

However, the ability of novel internet-based surveys to effectively elicit sensitive information relies on two critical issues. First, it is important to characterize how internet users differ from the general population to properly analyze and caveat the results. The results illustrate that the internet surveys are biased in exactly the ways one would expect- the internet respondents are slightly more educated and better off economically than mobile phone respondents, who themselves are slightly better educated than the general population. However, all results are qualitatively identical when restricting the sample in both the internet and the mobile phone surveys to be more comparable.

Second, it is important to investigate the degree to which the low response rate in the internet survey and the potentially different motives for non-response than in other modalities might be biasing the results. Although we cannot fully reject the possibility that there is unobserved sample selection, the results are consistent with the sample selection not being different from more commonly used modalities. The answers to nonsensitive questions are similar between the internet and mobile phone survey- both overall and when making the internet and mobile phone samples more comparable demographically; and the internet surveys are also able to identify many of the major ways in which the conflict has impacted households that have been independently reported by other 4
Licensing & royalties balance of payments, 2009 _Includes entire World Bank dataset, not just case study states; also uses different definitions of income categories Table 2. Licensing and Royalties Balance of Payments, 2009_

|**Country Group**|**Royalty, License**<br>**Fees Received**|**Royalty, License**<br>**Fees Paid**|**IP Balance of**<br>**Payments**|**Payments as a**<br>**% of Receipts**|
|---|---|---|---|---|
|**High income**|$177,835,372,041|$156,372,095,047|$21,463,276,994|88%|
|**Middle income**|$3,766,598,542|$32,421,925,901|-$28,655,327,359|861%|
|**Low income**|$33,678,498|$67,097,779|-$33,419,281|199%|

(Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files)

# Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files **3. The Monetization of Intellectual Property**

## **Intellectual Property and Technology Commercialization**

The institutionalization of property rights is a constitutive element of a market economy. Intellectual property is typically conceived as a means of reducing market failures associated with knowledge assets that are non-excludable and non-rivalrous in consumption (Merges 1994). The introduction of property rights over intangibles renders embedded and tacit knowledge codifiable, functional and manageable. In general, the introduction of property rights enables the generation of surplus value. People can still extract ‘use value’ from land, such as collecting fruits and game, without property rights. However, the introduction of property rights over land enables additional revenue streams not necessarily related to its primary use value, such as rent and sale. In a similar manner, IPRs enable people to not only extract value from their primary use, but also through a variety of secondary monetization mechanisms.

The conventional economic rationale for IP is linked with the hedging of risk associated with research and development. While this is certainly true, we are more interested in elaborating on the various mechanisms available to monetize IP. Like scholars such as Knight (1942) and Schumpeter (1942), we argue that IP bears the potential to foster entrepreneurship as the introduction of property rights is a constitutive element of prospering markets. IP institutionalizes a commercial paradigm over knowledge relations and can thus be seen as a cornerstone of the knowledge based economy. To discuss to what extent it is legitimate to commercialize knowledge relations and where the boundaries of knowledge privatization should be set is not the subject of the paper. This said, the privatization of knowledge relations is an important political issue that should not go unnoticed. By disaggregating knowledge and invention from its owner, IP is legally packaged for the transfer of ownership through a transaction (May 2002).

13
incomes and consumption of middle- and upper-income households (Sulla, 2011). It is likely that the ability or inability of households to use effectively strategies to cope with shocks can influence their subjective perceptions of the crisis: faced with job loss or wage reductions, a household that is able to access social safety-nets, or to borrow from friends and family, or use savings, could have a more benign perception of the impacts of the crisis. Going beyond expenditure and labor supply adjustments, we therefore also examine coping strategies, as declared by households, in particular the extent to which they differed across countries and individuals and how they correlated to subjective crisis impact.

The paper begins by describing the data, household subjective perceptions of the impact of the crisis, and the transmission channels through which they were affected. We then analyze heterogeneity in the use of various coping strategies, distinguishing between strategies to increase earnings, private and public safety nets and expenditure reductions. Last, we examine the relationship between household perceptions of the crisis impact and socio-political outcomes.

# **2. Objective shocks and perceptions of crisis impact**

# **2.1. The data**

The Life in Transition survey was conducted jointly by the European Bank for Reconstruction and Development (EBRD) and the World Bank in 2006 and 2010. Nationally representative samples of between 1,000 and 1,500 households were interviewed in every country. In total, in 2010, almost 39,000 households were surveyed in 34 countries of Europe and Central Asia to assess public attitudes, well-being and the impacts of economic and political change.

The crisis impact module of the 2010 LiTS elicits subjective perceptions of the impact of the crisis, in addition to collecting information on the shocks experienced, the coping strategies employed, and access to public safety nets. Respondents were asked how much the economic crisis had affected their household in the past two years, with responses coded as follows: 1: “a great deal”, 2: “a fair amount”, 3: “just a little” and 4: “not at all”. Respondents who reported that their households were affected were asked whether they (or a household member) experienced shocks such as job loss, wage reductions and lower remittances, and which impacted the most. Regardless of the subjective impact, all respondents were asked if they (or a household 3
||VH||||||
|---|---|---|---|---|---|---|
||H|||1|||
|**PGA**|||||||
|<br>**475‐years**|M|14||25|30|3|
||L|38||56|82|10|
||VL|170|10|269|519|119|
||VH||||||
||H|||9|15|1|
|**PGA**<br>**975‐years**|M|21||45|39|5|
||L|76||92|205|21|
||VL|125|10|205|372|105|
||VH||||||
||H|3||16|18|1|
|**PGA**<br>**1500‐years**|M|34||50|61|11|
||L|80|2|117|283|22|
||VL|105|8|168|269|98|
||VH||||||
||H|13||26|27|2|
|**PGA**<br>**2475‐years**|M|43||65|111|12|
||L|78|3|128|298|37|
||VL|88|7|132|195|81|

**Table 2.** Population (millions) with reduced wastewater system functionality in different hazard bands across 250‐, 475‐, 975‐, 1500‐, and 2475‐year return period PGA with liquefaction susceptibility. The specific return periods are given in each table. Columns add to ~460 million with values rounded for readability.

||||**Liquefaction Gro**|**up**||
|---|---|---|---|---|---|
|||VL|L<br>M|H|VH|
||VH|||||
||H|||||
|**PGA**<br>**250‐years**|M|0.7|3|6|0.6|
||L|7|13|12|2|
||VL|60|2<br>109|196|49|
||VH|||||
||H||0.5|||
|**PGA**<br>**475‐years**|M|5|7|9|0.6|
||L|10|17|22|3|
||VL|53|2<br>101|182|48|
||VH|||||
||H||3|6|0.6|
|**PGA**<br>**975‐years**|M|6|13|10|2|
||L|23|30|61|5|
||VL|38|2<br>79|136|44|

18
# **Figure 2: Estimated levels of urbanization using different methods and data sets**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>UN AI - LandScan AI - GHSPop EC - LandScan EC - LandScan EC - GHSPop EC - GHSPop<br>(All urban (HD clusters (All urban (HD clusters<br>clusters) only) clusters) only)<br>World LAC SSA SAR ECA MENA EAP N. America<br>Note: EC refers to the cluster method for the consistent measurement of urbanization. “All urban clusters” refers to the<br>share of population living in all urban clusters, while “HD clusters” refers to the share of population living just in high-<br>density clusters.<br>Share of population in urban areas<br><!-- End of picture text --> The conclusion to be drawn from the above is that, when measured consistently, LAC appears far less urban relative to other regions than we are generally led to believe. This finding is consistent with the use of more relaxed standards for classifying areas as urban in LAC versus non-LAC countries (see Section 2). It also confirms findings reported by Uchida and Nelson (2010) and the World Bank (2008) using the original version of the AI.

# _4.2. Re-assessing LAC’s position in relation to the basic stylized facts_

Tables 2 and 3 show results from two basic sets of regressions. In Table 2, the (natural log) of a country’s GDP per capita level is regressed on its level of urbanization. Meanwhile, in Table 3, a country’s level of urbanization is regressed on the share of its national GDP that is generated by agriculture. In both tables, columns (1) – (3) report results obtained when using the WUP data, which are based on national definitions, to measure levels of urbanization, while columns (4) – (6) report the corresponding results when instead using the AI with _LandScan 2012_ population data as input.

As can be seen, the tables confirm the results for the WUP data that were depicted visually in Figure 1. Hence, a country’s level of development is significantly positively correlated with its level of urbanization, while there is a significant negative relationship between a country’s urbanization level and the relative importance of agriculture to its economy. Furthermore, Latin American (LA) and Caribbean (C) countries depart significantly from these relationships, but in opposite directions. This is evident in the opposite signs on the dummy variables for Latin American and Caribbean countries in column (2) in both tables. On average, Latin American countries have significantly lower levels of GDP per capita than one would predict based on their levels of urbanization. Consistent with this, they also appear “over-urbanized” given the contribution that agriculture makes 13
# **_Table A4.7a Relationship between staff experience and IEG outcome rating, ordered probit_**

||<br>**IE**<br>|**Dependent variable:**<br>**G 1-6 outcome ratin**<br>|<br>**g**<br>|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|Number of projects prepared before by the|-0.018*|||
|preparation TTL|(0.011)|||
|(Log) Number of ISRs signed before by the||0.008||
|mid-point supervision TTL||(0.022)||
|(Log) Number of ISRs signed before by the|||0.040*|
|final supervision TTL|||(0.024)|
|Observations|2,767|2,762|2,757|
|Pseudo R-squared|0.081|0.081|0.08|

Note: Robust standard errors clustered at the country level are reported in parentheses. (***p<0.01,

**p<0.05, *p<0.10). All regressions are estimated using Ordered Probit, with controls for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

# **_Table A4.7b Relationship between staff experience and IEG outcome rating,_** **_<u>probit</u>_**

||**Dependent va**<br>**(Satisfac**|**riable: Binary IEG ou**<br>**tory=1, Non-satisfac**|**tcome rating**<br>**tory=0)**|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|Number of projects prepared before by the|-0.004|||
|preparation TTL|(0.015)|||
|(Log) Number of ISRs signed before by the<br>mid-point supervision TTL||-0.016<br>(0.030)||
|(Log) Number of ISRs signed before by the|||0.035|
|final supervision TTL|||(0.028)|
|Observations|2,572|2,567|2,563|
|Pseudo R-squared|0.099|0.099|0.099|

Note: Robust standard errors clustered at the country level are reported in parentheses. (***p<0.01,

**p<0.05, *p<0.10). All regressions are estimated using Ordered Probit, with controls for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

57
12 almost as much of a contribution as reform of other goods sectors (and considerably more in Sub-Saharan Africa and South Asia).<sup>9</sup> This partial offset is consistent with the fact developing countries have tariffs on non-food manufactures that are sizeable but less than those for agriculture, and whose removal has the effect of lowering a little the price of mobile factors employed in farming.

Value added in the processed food sector is similarly affected, although to a lesser extent on average than in the farm sector. Value added in non-agricultural primary production is affected very much less for developing countries as a whole, but note from the final column of Table 3 that it benefits proportionately more than farming in Africa, the Middle East and the former Soviet Union where that sector is of relatively greater importance. By contrast, non-food manufacturing value added would decline in all developing country regions except Asia following a move to global free trade. This is despite its lower protection than agriculture in developing countries, and a further reflection of the importance of high-income country farm support programs in depressing developing country farm incomes.

The differential impacts on net farm incomes within regions from freeing trade are shown in Table 4. In East Asia it is primarily Chinese farming that would benefit while in South Asia, farming is benefited in Pakistan and Sri Lanka rather than in India and Bangladesh. That reflects the latter countries’ relatively weaker comparative

> 9 The smallness of our estimated contribution of service sector distortions to value added in agricultural and other goods sectors is consistent with the findings of two other recent studies drawing on similar service distortion estimates (Dee 2004; Dihel 2004). Recall, though, that the distortions to the services sector we use (see Table 2) may well seriously underestimate the full extent of actual services distortions.
of Adverse Events on Implications Major Productivity<sup>_∗_</sup> Alistair Dieppe,<sup>_†_</sup> Sinem Kilic Celik _‡_ , and Cedric Okou<sup>_§_</sup>

**Keywords** : Productivity, local projections, disasters. **JEL classification** : C1, E3, O44, O47

> _∗_ This paper is based on Chapter 2 of the book Global Productivity: Trends, Drivers and Policies, edited by Alistair Dieppe, published by the World Bank. We are indebted to Ayhan Kose, Franziska Ohnsorge, Carlos Arteta, St ́ephane Hallegatte, Davide Furceri, Chris Papageorgiou, Annabelle Mourougane, Christopher Towe, Kevin Clinton, Graham Hacche, Markus Eberhardt and Peter Nagle for helpful discussions. We thank Xinyue Wang, Yi Li and Xi Zhang for excellent research assistance. We gratefully acknowledge financial support from the PHRD fund. This work was undertaken when Alistair Dieppe was at the World Bank. The paper reflects the views of the authors and should not be attributed to the institutions to which the authors are associated.

> _†_ ECB alistair.dieppe@ecb.europa.eu

> _‡_ World Bank skiliccelik@worldbank.org

> _§_ World Bank cokou@worldbank.org
# **Longevity and Lifetime Education: Global Evidence from 919 Surveys**

# **1. Introduction**

By 2015, world life expectancy at birth was 73.8 years for women and 69.6 years for men, more than double the average life expectancy in 1900 (UN 2017, Kinsella and Velkoff, 2001). A long-held theoretical model in economics explains why greater longevity would also increase lifetime human capital investment (Ben-Porath, 1967; Heckman, 1976; Becker, 1993). An increase in the human life span brings economic benefits to individuals and economies: lifetime earnings would rise because better health allows people to work more years and more hours per year, raises productivity per hour of work, and induces higher savings and more investments in physical and human capital. Analyses of cross-economy and individual-level data generally find that increases in life expectancy raise lifetime earnings, but their impact on schooling levels has been mixed.

The contrasting results from these studies motivate us to reexamine the relationship afresh using a different identification strategy applied to cohort-specific data from a large number of surveys in developing economies. This paper aims to establish a causal relationship between increases in life expectancy at birth and the completed years of schooling of cohorts born between 1905 and 1988 in 147 economies.

Unlike other cross-economy studies which we review below, our study uses data on completed years of schooling from 919 household surveys rather than estimates of schooling levels based on school enrollment data from population censuses. We adopt a birth-cohort approach that matches cohort-specific completed years of schooling with cohort-specific life expectancy at birth, allowing us to exploit between-cohort, within-economy variations in life expectancy at birth and schooling. In this approach, we incorporate data on parents’ life 2
40 (Table 1 - cont.) lc. Correlations Matrix for Inflation and Selected Real Variablesa

||DP|UNE|DIP|DEMP|DCON|DRER|RIR|DRW|
|---|---|---|---|---|---|---|---|---|
|DP|1.00||||||||
|UNE|.38<br>(-.21)|1.00|||||||
|DIP|-.17<br>(-.10)|.23<br> <br>(.19)|1.00||||||
|DEMP|<br>-.26<br>(-.25)|.14<br>(-.00)|.49<br> <br>(.54)|1.00|||||
|DCON|<br>-.27<br>(.02)|.06<br> <br>(.09)|.34<br> <br>(.19)|.12<br>(.04)|1.00||||
|DRER|<br>-.24<br>(-.50)|.17<br> <br>(.07)|.50<br>(-.03)|.29<br>(.14)|.25<br> <br>(.05)|1.00|||
|RIR|-.35<br>(-.87)|.35<br> <br>(.07)|.21<br> <br>(.00)|.23<br>(.16)|.27<br>(-.00)|.45<br> <br>(.54)|1.00||
|DRW|-.31<br>(-.05)|-.05<br>(-.01)|.17<br> <br>(.39)|.40<br>(.35)|.30<br> <br>(.06)|.19<br>(-.14)|.33<br> <br>(.08)|1.00|

Notes: a) The variables DP, DIP, DEMP, DCON, DRER, and DRW denote the rates of change of the CPI, industrial production, employment, consumption, real exchange rate, and real wage. UNE and RIR denote, respectively, the rate of unemployment and real interest rate. Each entry gives a correlation coefficient for the period 1980:2-1985:6. Figures in parentheses are correlation coefficients for the period 1986:1-1988:12.
36

# Table 6. Selected Economic Indicators for Two Townships In Wuxi and Shangrao Provinces, China (in yuan)

|Indicator|Township A<br>(Wuxi, 1985)|Township<br>(Shangrao|B<br>Ratio<br>, 1986)<br>A/B|
|---|---|---|---|
|Population (number)|18,779|23,396|<br>0.8|
|Average per capita income<br>of rural population|907|131|<br>6.9|
|Gross value of sgricultural<br>output per capita|<br>399|197|<br>2.0|
|Gross value of industrial<br>output per capita|11,268|17|<br>662.8|
|Sales income of township<br>enterprises per capita|5,846-|14|<br>418.0|
|Local revenue per capitab|1,268|9|<br>417.6|
|Expenditure per capita|522|18|<br>29.0|

a Industrial enterprises only.

b. Includes both budgetary and nonbudgetary revenues but not additional budgetary funds provided by the county for quota budget expenditures.

Source: Fieldwork of World Bank/Institute of Economics of the Chinese Academy of Social Sciences research proJect.
# **Table A7: Variables most commonly selected by LASSO**

|Predictors|Count|
|---|---|
|[YouWin: Total award paid]<sup>3</sup>|5|
|[Business Sector: Shop] x [Raven test: Q1A8 (correct)]|4|
|[Grit: often new interests] x [Application includes “employer of labor”]|4|
|[# gas stoves]<sup>3</sup>|4|
|[Raven test: Q9A9 (incorrect)] x [Loan from cooperative missing]|3|
|[Raven test: Q11A9 (incorrect)] x [Loan from cooperative missing]|3|
|[# Workers with secondary education missing] x [Length all answers application]|3|

Notes: Count denotes the number of times (out of 30) that the variable was chosen by LASSO in predicting employment, sales, and profit outcomes.
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