rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Firms’ innovation and technology adoption is widely considered as the key driver to economic growth. Google and Apple are two prime examples in the developed world, where their innovation and new products not only contribute to the economy, but also fundamentally change the way we work, entertain and communicate. Many developing countries, via different means such as foreign direct investment, also try to encourage firms to adopt new technologies and management practices.

Yet many firms do not innovate or adopt new technology. In seeking explanations for that, the conventional focuses have been on the obstacles to firms. For example, firms might not have the ability to innovate: they might not have the know-how or access to new technologies<sup>1</sup> . Even if they do, they might not have access to finance for the research or the adoption. Girma et al (2008) show that private and collectively owned firms without foreign capital participation and those with poor access to domestic bank loans innovate less than other firms do.

In this paper, we do not follow the conventional path to examine the obstacles to firms’ innovation, but rather, turn our focus to firms’ incentives to innovate. This angle, although more neglected, deserves more attention in our view. We argue that in many developing countries, firms might not have the incentives to innovate because the reward to innovation is small. For instance, in an environment where property rights are not well protected, a firm’s new product can be easily copied<sup>2</sup> . This will significantly reduce the return to innovation. Lin et al (2010) use the 2003 World Bank Enterprise Survey of over 2,400 firms in 18 Chinese cities to show that firms’ perception about property rights protection is positively and significantly related to corporate R&D activity. Another example is that in a monopolized sector, the incumbent might not need to innovate: their products, good or bad, are the only ones available in the market.

To make our point, we will proceed in two steps. In the first step, we estimate the return to firms’ innovation across many developing countries. We measure the quantitative return in terms of sales, and sales per worker. We find that the return is low, which implies that the incentive to innovate is small. In the second step, we compare the return to innovation across countries with different institutional quality. We find that in countries with lower institutional quality (in particular, rule of law, regulatory quality and property right protection), the return to firms’ innovation is lower.

> 1 Burstein and Monge-Naranjo (2009) shows that developing countries’ output can grow significantly when they eliminate all barriers to foreign know-how.

> 2 In this line, see Branstetter et al (2006).

2
As noted above, the WEAI time diary collects information about the time men and women spent on a wide range of work and non-work activities during the previous 24 hours. Strictly speaking, however, the WEAI only requires information on the time men and women spend on market and nonmarket work activities, which we use to assess whether a person has an excessive workload (see Alkire et al. 2013 for details). In response to concerns raised by implementers about the time required to conduct the WEAI time diary interview, we designed a stylized question version of the WEAI time diary, aimed at “streamlining” time use data collection in the WEAI. The questionnaire asks respondents the following questions with respect to 12 types of market and non-market work (see Figure 3):

1. In the last seven days, how much time in hours did you spend on [ACTIVITY]?

2. Did you spend a usual amount of time on [ACTIVITY] in the last seven days?

3. Since the last week was not usual, within the last six months how much time did you usually spend on [ACTIVITY] per week?<sup>12</sup> We expected that the stylized questions would be easier for respondents and enumerators to understand and require less time to implement in the field compared to the time diary, which collects information on a wider range of activities. A survey experiment designed to test this hypothesis was included as part of the pilot testing of the WEAI 2.0 survey in Bangladesh and Uganda in 2014.

Sample villages were randomly assigned to either the original WEAI time diary with primary and secondary activities, or an experimental time use module, including stylized questions plus a time diary collecting only primary activities. For respondents assigned to the latter group, the stylized questions were asked directly prior to the time diary during the same visit. Enumerators were instructed to record the time at the beginning and end of each survey module. Basic descriptive statistics for the data are given in **Table**

**_1._ Descriptive statistics of WEAI 2.0 pilot survey by country** Table 1.

> 12 Question 3 was only asked if the time spent on [ACTIVITY] in the last seven days was unusual according to the respondent.

13
Table 7: Female-owned activity by distance ring to GQ highway network

||Share o|f establishm<br>are fem|ents in eac<br>ale-owned|h band that|Share of|employee<br>f|s in each b<br>emale|and who are|Share|of female e<br>owned es|mployees<br>tablishmen|in female-<br>ts|Share o|f male emp<br>estab|loyees in m<br>lishments|ale-owned|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Distance|2001|2006|2010|Change|2001|2006|2010|Change|2001|2006|2010|Change|2001|2006|2010|Change|
||||||||A. Unor|ganized manu|facturing||||||||
|Nodal|0.155|0.244|0.230|0.075|0.100|0.108|0.144|0.044|0.618|0.732|0.815|0.197|0.949|0.964|0.937|-0.012|
|0-10|0.273|0.393|0.429|0.156|0.350|0.390|0.364|0.014|0.860|0.919|0.935|0.075|0.760|0.791|0.849|0.089|
|10-50|0.217|0.250|0.344|0.127|0.284|0.280|0.359|0.075|0.893|0.820|0.952|0.059|0.816|0.835|0.835|0.019|
|50-100|0.287|0.344|0.380|0.093|0.365|0.391|0.390|0.025|0.902|0.927|0.919|0.017|0.753|0.760|0.775|0.022|
|100-250|0.233|0.380|0.375|0.142|0.317|0.394|0.342|0.025|0.887|0.932|0.941|0.054|0.790|0.794|0.867|0.077|
|250+|0.340|0.457|0.393|0.053|0.359|0.412|0.339|-0.020|0.910|0.946|0.952|0.042|0.818|0.854|0.891|0.073|
|||||||||B. Services|||||||||
|Nodal|0.089|0.129|0.117|0.028|0.118|0.160|0.150|0.032|0.679|0.733|0.866|0.187|0.942|0.922|0.922|-0.020|
|0-10|0.082|0.097|0.095|0.013|0.168|0.174|0.167|-0.001|0.737|0.795|0.858|0.121|0.883|0.892|0.901|0.018|
|10-50|0.087|0.064|0.084|-0.003|0.152|0.145|0.151|-0.001|0.792|0.767|0.852|0.060|0.904|0.903|0.903|-0.001|
|50-100|0.055|0.076|0.078|0.023|0.149|0.191|0.153|0.004|0.728|0.728|0.820|0.092|0.889|0.855|0.896|0.007|
|100-250|0.069|0.068|0.071|0.002|0.170|0.169|0.151|-0.019|0.759|0.768|0.845|0.086|0.873|0.878|0.904|0.031|
|250+|0.095|0.103|0.094|-0.001|0.164|0.163|0.151|-0.013|0.717|0.803|0.844|0.127|0.903|0.909|0.920|0.017|

Notes: See Table 1. Distances measured in km.
David Hess and Gregorio Impavido

**Table 3. Board Composition (fraction of board)**

||.<br>...<br>ND-|**Minimuim**<br>**6i**|**Maximumn.a**|**Mean**|**Media-**|
|---|---|---|---|---|---|
|**Howselectedto**||||||
|**board:**||||||
|Exofficio|26|0.00|0.85|0.1850|0.0000|
|Appointed|26|0.09|1.00|0.7044|0.8167|
|<br>Elected|26|0.00|0.91|0.1407|0.0000|
|Trusteesrepresenting<br>specificgroups:||||||
|<br>Tradeunions|25|0.00|0.62|0.1844|0.2000|
|Employersassociation<br>|25|0.00|0.38|0.1291|0.0000|
|<br>Other employees<br>|25|0.00|0.38|0.0885|0.0000|
|association||||||
|Governmentasplan|25|0.00|1.00|0.3104|0.2500|
|sponsor||||||

# **_IV.B Nomination and Termination_**

An independent and vigilant board requires trustees that are not subject to political influence and that are free to exercise their independent judgment. These are the reasons behind the strong push toward corporate governance for boards dominated by outside directors. It is feasible for the participants of smaller civil service pension plans to directly elect some outside trustees, but for national schemes this may not be possible. Instead, the government may appoint trustees to represent stakeholder groups or to bring independent expertise to the board. The government's involvement in such appointments inevitably raises the concern that the trustees selected will be biased toward the government's policy goals and therefore will not be truly independent, however.

The equivalent situation on a corporate board would be that of the CEO selecting outside directors; should this occur, these directors at a minimum could be expected to be sympathetic to the CEO's views and therefore to be incapable of providing independent monitoring (Zajac and Westphal 1996; Main et al. 1995). The corporate governance solution to this problem has been to establish a nominating committee comprised entirely of independent directors. While the CEO will still have some influence in selecting new directors, it will be minimized. It is also recommended that the committee have fixed criteria for the selection of new directors, to ensure that the directors are qualified and to provide another control against favoritism in the selection process. Some public pension funds are experimenting with similar mechanisms.

The Canadian Pension Plan Investment Board (CPPIB) provides one example of how a pension fund is attempting to depoliticize the nomination of public pension plan governors (MacNaughton 2001). For the CPPIB, the federal finance minister and the finance ministers of the nine participating provinces appointed a nominating committee. Each government nominated one committee member, and the federal finance minister chose a private sector CEO as chair. For trustees, the committee identifies a set of qualified (as previously defined) prospective candidates from across Canada, referring this set to the federal finance minister. The federal finance minister then consults with his provincial counterparts on the proposed names before making final selection from the list recommended by the committee.

- 21 -
Policy Research Working Paper 6343

# **Abstract**

This paper studies the effect of subjective beliefs about HIV infection on fertility decisions in a context of high HIV prevalence and simulates the impact of different policy interventions, such as HIV testing programs and prevention of mother-to-child transmission, on fertility and child mortality. It develops a model of women’s life-cycle, in which women make sequential fertility decisions. Expectations about the life horizon and child survival depend on women’s perceived exposure to HIV infection, which is allowed to differ from the actual exposure. In the model, women form beliefs about their HIV status and about their own and their children’s survival in future periods. Women update their beliefs with survival to each additional period as well as when their HIV status is revealed by an HIV test.

Model parameters are estimated by maximum likelihood with longitudinal data from the Malawi Diffusion and Ideational Change Project, which contain family rosters, information on HIV testing, and measures of subjective beliefs about own HIV status. The model successfully fits the fertility patterns in the data, as well as the distribution of reported beliefs about own HIV status. The analysis uses the model to assess the effect of HIV on fertility by simulating behavior in an environment without HIV. The results show that the presence of HIV reduces the average number of births a woman has during her life-cycle by 0.15. The paper also finds that HIV testing can reduce the fertility of infected women, leading to a reduction of child mortality and orphan-hood.

This paper is a product of the Human Development and Public Services 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 author may be contacted at gshapira@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
attainment, health status, and degree of contraceptive use by various household members. Exceptionally, the 1998 APIS also included a module intended to capture the householdlevel effects of the dual financial and El Nino crises in the Philippines in 1998 (see Datt and Hoogeveen 2000).

The wage module in the APIS is quite detailed. It asks all household members aged 5 and older about salaries and wages earned in two three-month periods prior to the survey (April 1 to June 30, and July 1 to September 30). Data is gathered separately for the "primary" job and "others". The value of "basic salaries and wages, in cash or in kind" is recorded separately from the value of "allowances, honoraria, tips, housing, clothing, food, etc., in cash or in kind" (separately for both jobs, where applicable). In addition, the survey asks about the number of full days worked, the average number of working hours in these full days, the number of less-than-full days worked, and the average number of hours worked in these less-than-full days (again, separately for both jobs, where applicable). Information on the number of hours and days is collected separately for the months of July, August, and September (but no information on days or hours is collected for April through June). Like most surveys of its kind, the APIS also includes questions about the sector of employment, business classification, and class of worker for those employed. Finally, all adults who have not held ajob at all in the six months prior to the survey are asked if they have been looking for work and, if they have not, why.

7 About 60% of the households in the 1998 APIS are part of a panel with a survey carried out in 1997, the Family Income and Expenditure Survey (FIES). The FIES, which has been conducted in three-year intervals since 1985, has important advantages over the APIS-most notably, the fact that the consumption and income modules are much more extensive. But the FIES is not truly a multi-purpose survey. It includes information only on very basic household demographics and, for example, no information on the educational attainment of household members other than the household head. The large difference in the 7
Table 4. Cotton in district GJ8 of Gujarat: calculation of seven year average yields

|**Insurance**<br>**Unit**|**Yield,**<br>2001|**after re**<br>2002|**moval of**<br>2003|**trend of**<br>2004|**50 kg/h**<br>2005|**a/year(k**<br>2006|**g/ha)**<br>2007|**Seven year**<br>**average**<br>**detrended**<br>**yield for**<br>**insurance**<br>**unit (kg/ha)**|**Sample**<br>**variance of**<br>**detrended**<br>**yield for**<br>**insurance**<br>**unit**<br>**(kg**<sup>**2**</sup>**/ha**<sup>**2**</sup>**)**|
|---|---|---|---|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|[10]|
|GJ8_1|648|2,178|4,971|3,458|4,408|2,842|2,979|3,069|2,046,099|
|GJ8_2|1,054|324|4,541|3,716|3,985|3,357|2,451|2,775|2,480,213|
|GJ8_3|715|460|1,647|2,757|1,354|798|1,064|1,256|598,068|
|GJ8_4|656|379|2,805|2,651|2,995|2,428|1,912|1,975|1,113,738|
|GJ8_5|1,954|1,878|3,713|4,210|3,591|2,954|2,555|2,979|811,981|
|GJ8_6|3,077|1,654|4,285|3,272|3,377|2,719|2,133|2,931|747,051|
|GJ8_7|1,000|745|3,010|2,847|2,815|2,509|1,416|2,049|926,698|
|GJ8_8|1,000|745|3,010|2,847|2,815|2,509|1,522|2,064|905,942|
|GJ8_9|1,158|685|3,060|2,811|3,265|2,590|3,375|2,421|1,136,716|
|||||||A|verage:|2,391|1,196,278|
||||||S|ample v|ariance:|366,300||

Third, calculate the Empirical Bayes Credibility Factor for probable yields at the Threshold Yield Collective level using the yield data. For cotton in Gujarat the credibility factors for each district are given in Table 5.

Table 5. Cotton in Gujarat: Empirical Bayes credibility factors for probable yields

|**District**|**Number of insurance**<br>**units in district**|**Empirical Bayes Credibility**<br>**Factor for probable yields,**<br>|
|---|---|---|
|GJ1|10|93%|
|GJ2|11|18%|
|GJ3|4|93%|
|GJ4|13|79%|
|GJ5|11|91%|
|GJ6|12|50%|
|GJ7|2|0%|
|GJ8|9|53%|
|GJ9|8|72%|
|GJ10|8|94%|
|GJ11|14|91%|
|GJ12|14|24%|
|GJ13|11|0%|
|GJ14|10|87%|

A detailed breakdown of the credibility factor for district GJ8 is as follows: 12
|**COUNTRYNAME**|**AVERAGELENGTH**|**NO OFOBS**|**NUMBER OF**|
|---|---|---|---|
||**OF THEPANEL**|**. . **|**SECTORS**|
|**Somalia**|0.0|36|36|
|**South Africa**|11.6|783|77|
|**Spain**|25.2|2083|80|
|**Sri Lanka**|15.1|1015|78|
|**Sudan**|0.0|49|49|
|**Suriname**||0||
|**Swaziland**|3.4|150|34|
|**Sweden**|24.9|1898|80|
|**Switzerland**|8.9|64|8|
|**Syrian**|9.0|32|4|
|**Taiwan**|0.0|45|45|
|**Tajikistan**|14.3|621|43|
|**Tanzania**|18.5|888|73|
|**Thailand**|21.8|885|78|
|**Tonga **|5.3|46|8|
|**Trinidad**|12.8|729|60|
|**Tunisia**|12.6|417|33|
|**Turkey**|24.1|1933|81|
|**Turkmenistan**|4.0|30|6|
|**United States**|24.9|2006|80|
|**Uganda**|0.0|55|55|
|**Ukraine**|14.9|1253|79|
|**United Kingdom**|19.5|1562|81|
|**Uruguay**|18.7|884|73|
|**Venezuela**|12.4|911|80|
|**Vietnam**|3.1|211|67|
|**Yemen**|7.3|66|8|
|**Zambia**|7.9|174|62|
|**Zimbabwe**|17.1|759|42|

Source: INDSTAT 4 2009 Revision 2 and INDSTAT 4 2012 Revision 3<sup>27</sup> datasets from the United Nations Industrial Development Organization (UNIDO) <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2&Lg=1</u> 19
**Table 5.7. Outstanding equity issues by infrastructure providers on the Johannesburg Stock Exchange**

|Corporate issuer|Sector|Market<br>capitalization of<br>issues (US$ millions)|% share of all<br>corporate equity<br>issues|
|---|---|---|---|
|_Transport infrastructure construction_||||
|Aveng Ltd|Transport infrastructure construction|1,909.7||
|Basil Read Hldgs Ltd|Transport (roads) construction|128.5||
|Group Five Ltd Ord|Transport (roads) construction|653.5||
|Wilson Bayly Hlm|Transport infrastructure construction|691.2||
|Esor Ltd|Transport infrastructure construction|102.8||
|Sanyati Holdings Ltd|Transport (roads) construction|60.2||
|_Transport services_||||
|Comair Ltd|Aviation/commerical air|138.6||
|Cargo Carriers Ltd|Trucking/shipping services|28.7||
|Grindrod Ltd|Marine transport|1,048.9||
|Imperial Holdings Ltd|Transportation services|4,931.9||
|Millionair Charter Ltd|Aviation/commerical air|0.4||
|Mobile Industries Ord|Transportation services|93.6||
|Super Group Ltd|Transport/shipping services|670.9||
|Trencor Ltd|Transport/shipping services|693.2||
|Value Group Ltd|Transport/shipping services|89.4||
|Spectrum Shipping|Marine transport/shipping|27.4||
|Total transport||11,268.8|1.8|
|_Telecommunications_||||
|Allied Technologies|Telecoms & equipment|941.6||
|Mobile Telephone Networks|Telecoms|22,766.3||
|Shawcell Telecomm Ltd|Telecoms|12.9||
|Telkom South Africa|Telecoms|10,991.1||
|Africa Cellular Towers|Telecoms|44.0||
|Celcom Group|Telecoms|29.1||
|Total Telecoms||34,785.0|5.6|
|_Electricity generation_||||
|Murray and Roberts|Construction including in power generation|1,909.5||
|Ipsa Group Plc|Electricity generation|56.0||
|Total electricity generation||1,965.4|0.3|
|_Water and sanitation_||||
|Enviroserv Holdings Ltd|Waste and disposal services|129.5||
|Total water and sanitation||129.5|0.02|
|Issues by entities in infrastructure sectors as<br>% of all equity issues|||7.7|

Source: Johannesburg Securities Exchange. Note: “Transport infrastructure construction” includes construction of roads, dams, harbors, power stations.

56
Policy Research Working Paper 6426

# **Abstract**

Most migration surveys do not ask about the legal status of migrants due to concerns about the sensitivity of this question. List randomization is a technique that has been used in a number of other social science applications to elicit sensitive information. This paper trials this technique by adding it to surveys conducted in Ethiopia, Mexico, Morocco, and the Philippines. It shows how, in principal, this can be used both to give an estimate of the overall rate of illegal migration in the population being surveyed, as well as to determine illegal migration rates for subgroups such as more or less educated households. The results suggest that there is some useful information in this method: higher rates of illegal migration in countries where illegal migration is thought to be more prevalent and households who say they have a migrant are more likely to report having an illegal migrant. Nevertheless, some of the other findings also suggest some possible inconsistencies or noise in the conclusions obtained using this method. The authors suggest directions for future attempts to implement this approach in migration surveys.

This paper is a product of the Finance and Private Sector Development 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 author may be contacted at dmckenzie@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
# **Does Job Polarization Explain the Rise in Earnings Inequality?**

# **Evidence from Europe**<sup>*****</sup>

Maurizio Bussolo<sup></sup> Ivan Torre<sup>¥</sup> Hernan Winkler<sup>§</sup> JEL Codes: D31, J21, J24, J31 Keywords: Inequality, Polarization, Decomposition

> * 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.  The World Bank, e‐mail: mbussolo@worldbank.org

> ¥ The World Bank, e‐mail: itorre@worldbank.org

> § The World Bank, e‐mail: hwinkler@worldbank.org