rafmacalaba/datause-encoder-data huggingface dataset:
rather than freedom per se, that initiated unrest in developing MENA countries. It is therefore not surprising that dissatisfaction with standards of living has the largest and strongly significant negative effect on life satisfaction (table 2, models 3 and 7). On average, the life satisfaction score of dissatisfied respondents is 1.24 points lower than the life satisfaction score of respondents who are satisfied with their living standards in the fully specified model in table 2, model 7.

Second, poor job market conditions are significantly and negatively related to dissatisfaction in developing MENA countries—a result that retains significance even when we include all other subjective variables (table 2, models 4 and 7). The unemployed and underemployed report life satisfaction scores that are, respectively, 0.34 and 0.11 points lower than people in paid employment. Lack of quality jobs is another reason for the discontent and remains a significant factor even after we control for employment status. On average, respondents who indicate dissatisfaction with the availability of high quality jobs report 0.15 point lower life satisfaction than those who are satisfied with job quality (table 2, model 7). Not surprisingly, people working for the government (considered to be the best kind of jobs in MENA) are, on average, significantly happier than people working in the private sector.

Third, we find that dissatisfaction with the education system is associated with life dissatisfaction in developing MENA. Respondents who are dissatisfied with the educational system report 0.17 point lower satisfaction with life than those who are satisfied with the education system (table 2, models 4 and 7).

Fourth, perceptions of inequality of opportunities ( or ‘wasta’), corruption, and crony capitalism are significantly and negatively associated with life satisfaction in developing MENA (table 2, model 5 to 7). Respondents who think that people cannot get ahead by working hard report, on average, a 0.22 point lower life satisfaction score than those who are satisfied with this dimension of life satisfaction. Respondents who believe that corruption is widespread in the government are on average 0.27 point less satisfied with life, although this effect is reduced controlling for other perceptions (table 2 , models 5 and 7). Thus, in MENA, the governance problem is perceived to affect life satisfaction not so much through corruption in government, but through practices that affect all aspects of life and prevent people (and those working in the private sector, more generally) from succeeding even when they make great efforts to excel and do a good job. This result is consistent with the findings in Rijkers, Freund, and Nucifora (2014) and World Bank (2014a).

15
**Figure 2: Trends in profitability of the SOEs in the energy and gas sector; 2005-2010H1**

<!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>2005 2006 2007 2008 2009 2010H1<br>Gross operating profit Net operating profit Net profit<br><!-- End of picture text --> _<mark>Source: Romanian Ministry of Public Finance (2011), “Fiscal Information and Balance Sheets”,</mark>_ _<u><mark>http://www.mfinante.ro/pjuridice.html?pagina=domenii, and authors’ calculations</mark></u>_

**Figure 3: Trends in profitability of the SOEs in the transport and storage sector; 20052010H1**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>2005 2006 2007 2008 2009 2010H1<br>Gross operating profit Net operating profit Net profit<br><!-- End of picture text -->

**_<mark>Source:</mark>_** _<mark>Romanian Ministry of Public Finance (2011), “Fiscal Information and Balance Sheets”,</mark>_ _<u><mark>http://www.mfinante.ro/pjuridice.html?pagina=domenii, and authors’ calculations</mark></u>_

12
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking railway indicators, 2000–05**

||CFMK<br>(DRC)|SNCC<br>(DRC)|CFCO (Congo)|SETRAG (Gabon)|CFM (Angola)|CAMRAIL<br>(Cameroon)|South Africa<br>(SPOORNET)|
|---|---|---|---|---|---|---|---|
|Concessioned (1)/ State run (0)|0|0|0|1|0|1|0|
|Traffic density, freight, 1,000 tonne-<br>km/km|172|214|428|504|469|1,092|5,319|
|**Efficiency**||||||||
|Staff: 1,000 unit tariff (UT) per staff|18|38|221|1,778|580|603|3,037|
|Coaches: 1,000 passenger-km per<br>coach|64|275|3,212|1,891|4,045|4,738|596|
|Cars: 1,000 tonne-km per wagon|257|317|300|902|950|868|925|
|Locomotive availability in %|10|4|27|39|30|26|—|
|**Tariffs**||||||||
|Average UT, passenger, U.S.<br>cents/passenger-km|4.2|3.1|5.6|8.6|—|2.2|—|
|Average UT, freight, U.S. cents/tonne-<br>km|13.7|12.5|10.7|2.5|—|5.2|—|

— = data not available.

# River

## **Achievements**

While it is important to reconstruct the road and rail networks, the very valuable role that can be played by river transportation should not be overlooked. The vast Congo River traverses the DRC, linking two of its main cities (Kinshasa and Kisangani), while its innumerable tributaries crisscross much of the country.

## **Challenges**

About 15,000 km of the Congo River and its tributaries are navigable, or potentially so with a certain amount of regular dredging and relatively modest investments in quays and signaling. The capital and maintenance costs per kilometer of navigable waterway are a fraction of those per kilometer of road. Even under today’s less than ideal navigation conditions, the cost of moving freight along the Congo River— around 5 cents per tonne-km—is only a third of the cost of moving freight by road or rail. Although the river network does not cover all routes of interest, and river transport is comparatively slow, it has clear economic advantages and the potential to recover its historically large role in the DRC’s transport network.

13
policy when comparing routine, cognitive tasks and routine, manual tasks. The results suggest that regulatory enforcement does not differentially impact the routine cognitive versus manual skill composition of tech-intensive industries.

**Analytical versus interactive skills** Labor market regulations tend to favor workers employed in the middle of the skill distribution—those workers employed in non-routine, manual tasks. Even still, in the final column of Table 5.2, we consider whether labor market regulations promote certain non-routine, cognitive tasks over others. The answer is a clear no.

# **7. Conclusions and Policy Implications**

There is a strong concern around the globe that technology is increasingly replacing routine tasks, displacing lower-skilled workers. Labor market institutions exist to protect workers from shocks but, by increasing costs, labor policy may also constrain firms from adjusting the workforce to fully benefit from technology adoption. We assess the link between access to digital technologies and the demand for skills in the largest Latin American country, Brazil. Between 1996 and 2006, Brazil underwent a period of strong growth in internet service provision, as well as increased enforcement of labor market regulations at the subnational level. Our empirical strategy exploits administrative data to assess the extent to which the adoption of digital technology affects employment and the skill content of jobs at the local level. In addition, we investigate whether the stringency of labor regulations influences this adjustment, by comparing the effect across industries subject to different degrees of labor market regulatory enforcement.

Exploiting the fact that industries vary in the degree of reliance on digital technologies, our estimates suggest that digital technology adoption leads, in the short-run, to a reduction in employment in local labor markets. Tech-intensive industries reduce their reliance on both non-routine and routine tasks in the aftermath of the technology shock, but the decrease in 31
illiteracy even further are hampered by the fact that the many of the illiterates are adults the result of years of educational neglect. Efforts to improve adult literacy have been undertaken, but with poor results, because it is more difficult to teach basic skills to adults than to children. Interestingly, in 1999, slighly fewer females are illiterate than males—24.1 percent of females are illiterate as compared to 25.4 percent of males. Similarly, those aged 10 and older have on average 4.1 years of formal education in Bahia—1.5 years less than Brazil as a whole. Even among young adults, educational performance is poor. The education deficit, including that of quality, has a spatial dimension in Bahia. Of the illiterate over age 15, the majority live in the rural areas. In 1999, 40 percent of the rural dwellers could not read or write compared to 8 percent in the metropolitan region of Salvador (Rebeiro 2001).

Life expectancy has increased during the last three decades in Bahia and slightly more than in Northeast region as a whole (see Figure 4.2). However, men in Bahia, as in many parts of Brazil, have significantly lower life expectancy than women. There are no health programs oriented toward men.<sup>7</sup> Other areas of concern include the high levels of alcohol and substance use and abuse by men, and the links between alcohol use and violence.

**Figure 4.2:    Life Expectancy, 1970-2000**

<!-- Start of picture text --> 80 .0<br>N o rthe ast<br>70 .0 B a hia<br>B ra zil<br>60 .0<br>50 .0<br>40 .0<br>30 .0<br>20 .0<br>10 .0<br>0 .0<br>197 0 1980 19 91 200 0<br>Source: Human Development Indicators.<br><!-- End of picture text -->

# **POVERTY DEPTH**

Although Bahia has made substantial progress in reducing the share of the population living in extreme poverty, the problem remains broad and deep. The P0

> 7 Health programs—including sexual and reproductive and general health programs—have traditionally been directed at women. For example, prevention programs have been established for breast and cervical cancer, but no parallel efforts have been made to address prostate cancer among men. Similarly, reproductive health programs have tended to exclude men. As a result, contraceptive use among men is minimal. Of particular concern is that most men do not report using condoms, which puts them and their partners at risk of contracting HIV/STDs.

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<!-- Start of picture text --> 1000<br>883<br>800<br>600<br>506<br>502<br>400 374<br>200<br>159<br>0<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> <!-- Start of picture text --> PRT ial<br> ONK iil<br>BGR ad<br>swe =a<br>cYP al<br>AUT<br>cz:<br>gC tz!<br>|<br>ad<br>ND ad<br>|<br>1 ee<br>0 5 10 15 20 25<br><!-- End of picture text -->

<!-- Start of picture text --> Fats and oils (15) i<br>Edible prepared meat, fish, etc. (16) i<br>Beveragesand vinegar (22) iz<br>Animal products (05) ia<br>Food & animal feed residues (23) iz<br>Coffee, tea and spices (09) imma<br>Cereals(10) =m<br>Edible fruits & nuts (083) =m<br>Fish and crustanceans (03) =a<br>Oil seeds and misc. grains (04) —<br>Dairy and other edible products (14) mmm<br>licanas()) =—<br>Vet (2) OOX—d<br>iM vHey _ l=<br>0 200 400 600 800 1000 1200<br><!-- End of picture text -->
more subscriptions to the _Encyclop ́edie_ , capturing higher order human capital, grew faster after the dawn of the industrial revolution. Waldinger (2012) shows that the dismissal of scientists in Nazi Germany led to a permanent decrease in growth.

To date, however, neither the historical prevalence of Romer’s research engineers or Mokyr’s engineers and mechanics, nor Rosenberg’s and Nelson’s systems of innovation have been quantified in a globally comparable form. That is, the profession has no systematic historical evidence on allegedly _the_ central actor in modern economic growth.<sup>9</sup> In this paper we, first, make this very basic contribution. We establish the stylized facts surrounding the relative density of engineers at the end of the 19th century at the national level for the Western Hemisphere and representative benchmark countries of Europe, and the same at the sub-national level for six countries in the Americas. This is done in a systematic way drawing on graduation records, membership in professional societies, and census data. We see these estimates, in the first instance, as a measure of what they are: the stock of higher level scientifically oriented human capital directly linked to productive activity available at the beginning of the Second Industrial Revolution. Second, because our national measures are based on graduates of domestic engineering schools, they also proxy for the universities and institutions that support them, and for which data are more elusive (see Nelson, 2005). Further, we are sympathetic to the use in Murphy et al. (1991) of engineering density as a proxy for “good entrepreneurship,” perhaps more specifically the advancement of the long-run cumulative process of developing entrepreneurial technological depth and accompanying cultural factors discussed by Phelps (2013). In sum, we see it as a broad proxy for innovative capacity. We document an extraordinary variance in engineering density across countries of very similar income levels in 1900-for instance Argentina, Chile, Denmark, Sweden and the Southern US- that correlates to their divergent income positions today.

> 9Collected articles in Fox & Guagnini (1993) have examined the evolution of engineering capacity in several advanced countries although the comparability of the measures across countries is not always clear, and there is no attempt to establish a link to economic performance.

4
public health innovations were introduced over the relevant time periods, and detailed historical data is scarce.

# **3. Data**

This paper is built around data from the Demographic and Health Surveys (DHS). Starting in the early 1980s, more than 200 surveys have been conducted in over 70 countries. In this study, we use all standard format surveys that are publicly available and have complete information regarding diarrhea, child mortality, and access to sanitation and water infrastructure. This leaves us with a total of 172 surveys in 70 countries as summarized in Table A.1 in the Appendix.

**Figure 1: Child diarrhea**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>SSA<br>0.15 OTHER<br>RURAL<br>0.1<br>URBAN<br>0.05<br>0<br>0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57<br>Age in Months<br>Child Diarrhea in %<br><!-- End of picture text --> In line with most of the existing literature, the first dependent variable we use in our analysis is child diarrhea. Most DHS surveys ask female respondents whether any of their children under the age of 5 had diarrhea over the two weeks preceding the interview. As Figure 1 illustrates, the likelihood of a child being reported with diarrhea rapidly increases with age early on, and peaks at 11 -15 months of age in both rural and urban areas. As Figure 1 also illustrates, 8/36
The results enabled a global assessment of risk levels and the identification of areas where the potential for disaster impacts is large. Second, the _Global Assessment Report 2009_ (UN-ISDR 2009) is a multiple agencies effort that developed the Global Disaster Hotspots further by using enhanced modeling techniques and improved data layers. An update of this 2009 global risk analysis was released in the _Global Assessment Report 2011_ (UN-ISDR 2011).

National level: An example of a comprehensive multi-hazard risk index that assigns overall risk values on a national level is the _Disaster Risk Index_ (DRI) (Peduzzi et al. 2009). The DRI calculates three factors on a national resolution for 200 countries: risk of mortality, the relative vulnerability of each hazard type, and the physical exposures of populations to hazard. Another example for a risk assessment on national scale, covering a multitude of countries, is the study by McGranahan et al. (2007), which ranks countries according to their population shares in the low elevation coastal zones.

Urban level: With the rise of megacities, risk assessments have increasingly taken place at the city-level, identifying inner-city areas of high risks and loss potential (e.g., World Bank 2010b). However, only a few limited initiatives exist which assess the overall risk of numerous cities in the form of an index to compare and rank cities with each other. Efforts in this area to date have been confined to relatively limited sets of locations and hazards. The Munich Reinsurance Group developed the _Natural Hazard Index for Megacities_ for 50 cities with high global economic significance (Munich Re 2005). The index has an economic emphasis and is geared towards the risk of material losses which is suitable from an insurance perspective. Hanson et al. (2011) ranked 136 port cities around the world that have more than one million inhabitants. The study examines the risks of coastal areas due to storm surge and high winds, taking into account predictions of climate change, subsidence, and population growth. Brecht et al. (2012) determined the impact of sea level rise and intensified storm surges in developing countries and highlight the major cities worldwide that are located in storm-surge zones. Furthermore, methodologies have been developed that propose indicators to estimate the overall risk of cities. Indicators include, for example, population density or number of hospital beds (e.g., Davidson 1997, Cardona 2005). These methodologies 6
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Policy Research Working Paper 9128

# **Abstract**

This paper assesses the relationship between the length of recall and nonrandom error in agricultural survey data. Using data from the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture in Malawi and Tanzania, the paper shows that key input and output variables are systematically related to the length of the recall period, indicating the presence of nonrandom measurement error. With longer recall periods, farmers report greater quantities of harvest, labor, and fertilizer inputs. Farmers list fewer plots as the recall period increases. The paper argues that it is plausible that farmers overestimate plot-level outcomes, or they forget some of their more marginal plots due to longer recall periods. The analysis also finds evidence of measurement error related to the length of recall in common measures of agricultural productivity. The size of the recall effect typically varies between 2 and 5 percent per additional month of recall length, which is economically significant. With data reliability affecting policy effectiveness, improving agricultural survey data quality remains an important concern. Mainstreaming objective measures where possible and reducing the risk of recall error through shorter recall periods appear to be promising avenues to improve the quality of key variables in agricultural surveys.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at pwollburg@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
likely to have had those siblings die, and bias in the mortality estimates would be reduced; if the correlation were negative then mortality estimates would be biased even further downwards. We account for this potential correlation by following the methodology proposed by Gakidou and King (2006) and weight each sibling observation by the inverse of the number of surviving female siblings at the time of the survey (including the respondent).<sup>5</sup> Specifically, each observation contributes 1/si in the calculation of D and P in equation (2), where si is the number of surviving female siblings in a family. As illustrated with the solid points in Figure 1 for the 1990-1994 period and the population aged 15-59, the general effect of this adjustment is to increase the estimate of adult mortality.<sup>6</sup> A third potential bias could emerge if all of the sisters in a family die and therefore none appear in the survey. Excluding such families would tend to bias mortality estimates downward since high mortality sets of siblings would be excluded from the estimation sample. We again followed the approach suggested by Gakidou and King (2006) and estimated the number of “missing” siblings who have died. The approach consists of estimating the relationship between the number of siblings who have died and the total number of surviving children in each family, and then projecting out of sample to a case where there are zero surviving children. This approach is data intensive in that it requires a substantial number of observations to reliably estimate a relationship between the number of survivors and the number of children who have died. Moreover, it requires imposing strong assumptions about the functional form of the relationship between those two quantities. Our experience, illustrated with the hollow points in Figure 1 for the 1990-1994 period for the population aged 15-54, the effect is quite small at most (relatively lower) mortality levels and only meaningful in the case of very large mortality events. Indeed, the points on the right of the graph are data from Rwanda—in the midst of a genocide at that point. While feasible to implement this approach for the adult population as a whole, our experience was that this adjustment became very unstable when considering more narrow age

> 5 Note that we also weight each sibling observation by the survey weight associated with the responding sibling assigned on the basis of the original sampling strategy.  Therefore, each sibling observation is weighted by the survey weight divided by the number of surviving female siblings.

> 6 Appendix 1 provides a simulation of how various approaches to weighting affect the results and reduce the biases, particularly when mortality and the number of siblings are correlated.  In addition, it shows how including the respondent herself in the mortality calculations is an important part of removing bias in the estimation of mortality.

6