rafmacalaba/datause-encoder-data huggingface dataset:
sector. However, the number of procurement authorities increased by 46%, compared to only a 10% increase in the number of suppliers, suggesting a potentially more competitive procurement process. Additionally, there was an increase in the number of contracts for the median procurement authority and supplier, reflecting the growth in participating organizations.

Interestingly, we also found that the median procurement authority's total contract value increased by approximately 20 million MKD, and the median supplier's total contract value increased by 17.2 million MKD. We observe these changes along with drops in the average tie CRI from 0.49 to 0.42. These findings suggest that the construction sector may be experiencing growth and increased investment under the new government. Nevertheless, a further analysis is necessary to determine the underlying factors driving these changes and to assess their long-term impacts on the sector and the overall economy.

**Table 6: Summary of the Construction Sector Procurement and Network Statistics before and after the government change in North Macedonia.**

||**Jan 2011-May 2017**|**Jun 2017-Dec 2022**|
|---|---|---|
|**A) Procurement Statistics**|||
|**Total contract value**|~ 30 billion MKD|~ 45 billion MKD|
|**Number of procurement authorities**|571|835|
|**Number of suppliers**|1,235|1,360|
|**Median # contracts (PA)**|8|13|
|**Median # contracts (Supplier)**|3|10|
|**Median Total contract value (PA)**|~ 9 million MKD|~ 29 million MKD|
|**Median Total contract value (Supplier)**|~ 1.8 million MKD|~19 million MKD|
|**Average CRI**|0.49|0.42|
|**B) Network Statistics**|||
|**Number of ties**|2,128|3,993|
|**Average node degree**|2.357|3.683|
|**Average path length**|6.575|5.241|
|**Graph Density**|0.001|0.002|
|**Clusters**|158|93|
|**Modularity**(stronger clustering of buyer-<br>supplier ties)|0.852|0.666|
|**Closeness Centrality***(higher values<br>given to buyers/suppliers that are closer to<br>other nodes in the network)|0.1089|0.1723|
|**Betweenness Centrality***(higher values<br>given to buyers/suppliers that create|||
|shorter paths between two nodes)|0.0023|0.0016|

* These aggregated centrality measures are network measures based on the centrality scores of each node in the network, The higher the centrality scores, the more likely for nodes that are further apart to interact together through central agencies/firms

42
model in section 3.<sup>47</sup> Thus higher landlessness comes with a lower poverty rate among those with land, but we do not find a statistically significant impact on the poverty rate among the landless. This suggests that there are both gainers and losers in this group.

Although not strictly consistent with the above tests, it is of interest to see what happens if instead we use the aggregate poverty rate (across both landless and those with land) as the dependent variable. This gives a simple DD test of PILE. With this change, we obtain ˆ β = − .0042 (t-ratio = -10.187).<sup>48</sup> Rising landlessness has been poverty reducing overall and the effect is statistically significant.

To assess the size of this effect, note that the log of the national _H_ fell by 1.008 between 1993 and 2004. Our latter estimate of β thus implies a change in ln _H_ of -0.189 that is attributable to the fact that the landlessness rate rose by 4.5% points over this same period. Thus our estimate of the impact on the aggregate poverty rate implies that 19% of the total reduction in rural poverty is attributable to rising landlessness.

The poverty-reducing effect of rising landlessness is in part distributional. This can be seen if we add (log) mean consumption to the regression — to purge the poverty measure of the effect of differences in mean consumption, leaving the distributional effect. We obtained:<sup>49</sup> About 60% of the landlessness effect is transmitted through mean consumption, leaving 40% transmitted through more pro-poor distribution.

> 47 This is confirmed by regressing the inequality index among those with land on _LL_ (allowing for regional and year effects); we find a regression coefficient of -0.0016, which is significantly different from zero at the 5% level (t=-2.14); there is no such effect on inequality among the landless.

> 48 We dropped highly insignificant regions; with a complete set of regional effects we obtained ˆ β = − .0032 with a t-ratio of -2.778.

> 49 We dropped regional and year effects with very low t-ratios (leaving 2002, RRD, SE and MD). With a complete set of year and region effects the coefficient on _LL_ is -0.018 (t=2.09).

33
Policy Research Working Paper 10694

# **Abstract**

To what extent do attitudes and perceived norms around household roles hinder the emergence of more gender-equal distributions of labor in Morocco? Moroccan women undertake a disproportionate share of unpaid household and care labor and participate in the labor force at low rates. Yet everyday practices are shifting, and normative expectations may be as well. From an online survey of predominantly urban, employed Moroccans, this paper finds that respondents aspire for men to be equal contributors in care tasks. Yet, unpaid labor burdens remain highly unequal, respondents disfavor men taking primary responsibility for cooking or cleaning, and women’s share of household labor correlates with perceptions of what men prefer more than with individuals’ actual preferences. Results from a conjoint survey experiment measuring preferences around employment and the household division of labor confirm respondents’ interest in more egalitarian relations in principle, but also suggest that strong preferences for a male breadwinner family model will continue to drive an unequal distribution of labor at home.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 carolynbarnett@arizona.edu.

_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
some market power, but we believe the shares are small enough that this is not a first-order concern.

# II DATA AND DESCRIPTIVE STATISTICS

The empirical analysis uses daily logbook data from the three fishing cooperatives noted above, Pichilingue, Sargento, and Abreojos. Daily data on catches from fishing teams in each cooperative were recorded from January 1, 2007, to December 31, 2009. Catch records include a team identifier (for Abreojos and Pichilingue only), the common name of the species caught, the weight of the catch (kilograms), and price per kilogram offered by the cooperative (pesos). The composition of the species fished by the cooperatives partially reflects the biogeography of the Pacific vs. the Gulf coast of B.C.S.; however, there is still substantial overlap, thereby allowing a comparison of the behavior of the different types of cooperatives for a given species.

The logbook data have information on the prices cooperatives paid to their fishermen but, unfortunately, do not have information on the price the cooperative sold the catch at in the market. Using the _Sistema Nacional de Informacion e Integracion de Mercados_ (SNIIM), available from the Mexican government, we have collected data on daily market prices in La Paz for as many species and dates as possible.<sup>5</sup> Using the dates in the cooperative logbooks, these market prices are matched to the cooperative purchases. In cases where a market price is not available for a particular date, the average price for the corresponding week or month is substituted instead (depending on availability).

To examine whether market prices in La Paz are driven by external forces that are exogenous to supply factors in the vicinity of La Paz, we use the SNIIM to obtain market prices from La Nueva Viga, a large national fish market in Mexico City connecting sources to distributors. The La Nueva Viga data contain information on marine fish, crustaceans, freshwater fish, and mollusks/others. B.C.S. is listed as a source only for the fourth category. This, coupled with the fact that other sources of La Nueva Viga catch have only a partial overlap of species with La Paz, limits the number of species that can be matched to the logbook data. In cases where a market price is not available for a particular date in the logbook, the average price for the corresponding week or month is again used. All cooperative and market prices are converted into 2010 Mexican pesos using a Consumer Price Index obtained from the OECD.

We aim to understand how cooperative pricing responds to natural variation that alters population growth rates. The Oceanic Niño Index (ONI) is a three-month running mean of an 5. Available at http://www.economia-sniim.gob.mx/i_default.asp.

1
_Segmentation_ . In the terrain analysis, road characteristics are calculated by segment, which is defined somewhat arbitrarily according to the network geometry. While the start and end of a segment may not necessarily define a homogenous feature, errors in network geometry only exacerbate the problem.

## **Scale difference between Spatial resolution of Digital Segmentation example gROADS and Open Street Map Elevation Model**

_Figure 11 Examples of Challenges with Input Datasets_

# 6.3. The Highway Development and Management Model, HDM-4

Road user costs (RUC)<sup>10</sup> and speed (km/hour) are calculated for roads of different attributes using the World Bank’s Highway Development and Management Model (HDM-4). This software package was built to evaluate and assess road maintenance, improvement, and investment projects. Inputs into HDM-4 include both road characteristics (network type, terrain, surface, and pavement condition) and vehicle fleets information (traffic classes). For the purposes of this study, the vehicle fleet data were collected at the country level with a heavy truck taken as the representative vehicle. For each of the possible combinations of road characteristics and traffic class (6,804 in total), we calculated in HDM-4 a unique road user cost and travel speed estimate.

## **Road conditions definition in HDM4**

(http://siteresources.worldbank.org/EXTAFRSUBSAHTRA/Resources/SSATPWP89A-RONET.pdf) For paved roads, the road condition classes are defined as follows:

- Very Good: Roads in very good condition require no capital road works.

- Good: Roads in good condition are largely free of defects, requiring some minor maintenance works, such as preventive treatment or crack sealing.

- Fair: Roads in fair condition are roads with defects and weakened structural resistance, requiring resurfacing of the pavement (periodic maintenance), but without the need to demolish the existing pavement.

> 10 RUCs are defined as the unit cost of using a road expressed in dollars per ton-kilometer and consist of vehicle operating costs (VOCs), which reflect the cost of operating a vehicle, and value of time costs (VOTs), which reflect the cost of time associated with using a vehicle. VOCs include the costs of fuel, lubricants, tires, maintenance parts and maintenance labor, crew time, depreciation, interest, and overhead. The VOTs include the cost of passenger time and the cargo time. Each of these components is calculated separately as an output of the HDM-4 model.

27
Table 1: Notations in estimation

|Regressand||
|---|---|
|ln(importijrt)|the natural logarithm of the imports of country j from country<br>i in industry r and year t|
|Fixed e¤ects||
|�irt|exporter-industry-year|
|�jrt|importer-industry-year|
|�ijr|exporter-importer-industry|
|Regressors||
|HARijrt|the number of harmonization directives between i and j in<br>industry r and year t|
|HAR_Mijrt|the number of harmonization directives between j and any<br>country other than i in industry r and year t|
|HAR_Eijrt|the number of harmonization directives between i and any<br>country other than j in industry r and year t|
|MRA_ROijrt|1 if an MRA with rules of origin exists between i and j in<br>industry r and year t, and 0 otherwise|
|MRA_RO_Mijrt|1 if an MRA with rules of origin exists between j and any<br>country other than i in industry r and year t, and 0 otherwise|
|MRA_RO_Eijrt|1 if an MRA with rules of origin exists between i and any<br>country other than j in industry r and year t, and 0 otherwise|
|MRA_NROijrt|1 if an MRA without rules of origin exists between i and j in<br>industry r and year t, and 0 otherwise|
|MRA_NRO_Mijrt|1 if an MRA without rules of origin exists between j and any<br>country other than i in industry r and year t, and 0 otherwise|
|MRA_NRO_Eijrt|1 if an MRA without rules of origin exists between i and any<br>country other than i in industry r and year t, and 0 otherwise|
|RTAijt|1 if an RTA exists between i and j, and 0 otherwise|
|RTA_Mijt|1 if an RTA exists between j and any country other than i,<br>and 0 otherwise|
|RTA_Eijt|1 if an RTA exists between i and any country other than j,<br>and 0 otherwise|

16
- (a)  Graduates will demonstrate knowledge of mathematics, science and engineering.

- (b)  Graduates will demonstrate an ability to identify, formulate and solve engineering problems.

- (c)  Graduates will demonstrate an ability to design and conduct experiments, analyze and interpret data.

- (d)  Graduates will demonstrate an ability to design a system, component or process as per needs and specifications.

- (e)  Graduates will demonstrate an ability to visualize and work on laboratory and multidisciplinary tasks.

- (f)  Graduate will demonstrate skills to use modern engineering tools, techware and equipment to analyze problems.

- (g)  Graduates will demonstrate knowledge of professional and ethical responsibilities.

- (h)  Graduate will be able to communicate effectively in both verbal and written form.

- (i)  Graduate will show the understanding of impact of engineering solutions on the society and also will be aware of contemporary issues.

- (j)  Graduate will develop confidence for self-education and ability for life-long learning.

- (k)  Graduate who can participate and succeed in competitive examinations.

Ten out of the 11 NBA Program Outcomes were included in the questions (some in an abbreviated form). Thirteen skills from previous employer surveys, notably from (Kleinke, 2006) were added. These were in particular skills often referred to as soft skills or core skills or employability skills, such as integrity, self-motivation, team skills etc. Further three specific skills were added, namely ―Basic computer‖, ―Advanced Computer‖, and ―Customer Service Skills‖. Lastly, another three skills ―Technical Skills (programming)‖ ―Communication in English‖ and ―Entrepreneurship Skills‖, were included as per request of employers.

Definition of skills and a common understanding of what a skill is, poses a problem for comparability and interpretation. Given the survey has to be relatively short to ensure an acceptable response rate, the questionnaire did not define each skill. In most instances, an additional explanation of example is provided in parenthesis. However, it is possibly that employers may have perceived the meaning of the skills differently. In addition, some of the skills are overlapping in the sense if a person possess skill a, then they are strongly expected also to possess an element of skill b. One such example is ― _Self-motivated_ ‖ and ― _Self-discipline_ ‖. However, there is no widely accepted categorization of skills that are exhaustive and non-overlapping. Hence, an overlap is unavoidable in our view.

> work on multidisciplinary tasks‖, while the ABET criteria (d) asks for the graduates to ―function on multidisciplinary teams‖.  Although, the difference is subtle, the ABET criterion directly asks for team-skills, while the NBA does not.

9
|**Table 1. Ebola Mortality and Agricultural Labor**||Hir|ed/Kuu labor|per total labor|for:|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Hired/|Kuu labor per total labor|<br>Clearing|Planting|Management|Harvesting|
|Log all burials per 1000 population|-0.010<br>(0.010)|-0.017<br>(0.009)**|-0.027<br>(0.010)***|-0.003<br>(0.012)|-0.016<br>(0.011)|
|Severely affected counties (dummy)|0.140<br>(0.024)***|0.164<br>(0.028)***|0.152<br>(0.026)***|0.124<br>(0.026)***|0.131<br>(0.027)***|
|Distance weighted to nearest major road (km)|0.000|-0.000|0.000|-0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Head of household is female|0.033|0.127|-0.010|0.019|-0.022|
||(0.030)|(0.033)***|(0.031)|(0.024)|(0.032)|
|Household head age|-0.000|-0.001|-0.000|0.000|-0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Head education level dummies (ref= no education)||||||
|Household head has primary education|-0.071|-0.050|-0.087|-0.039|-0.043|
||(0.031)**|(0.038)|(0.031)***|(0.018)**|(0.032)|
|Household head has secondary education|-0.034|0.021|-0.049|-0.021|-0.049|
||(0.027)|(0.031)|(0.029)*|(0.023)|(0.028)*|
|Household head has tertiary education|0.080|0.117|0.123|0.129|-0.121|
||(0.069)|(0.066)*|(0.081)|(0.131)|(0.094)|
|Constant|0.328<br>(0.039)***|0.401<br>(0.051)***|0.351<br>(0.040)***|0.119<br>(0.035)***|0.281<br>(0.041)***|
|Mean of dependent variable|0.389|0.508|0.411|0.195|0.331|
|Observations|3122|3122|3122|3122|3122|
|Fstat|7.182|11.900|6.969|3.397|4.987|
|Prob > Fstat|0.000|0.000|0.000|0.001|0.000|
|Adjusted R-squared|0.061|0.078|0.057|0.039|0.038|

15
- Years of Schooling

- Literate/illiterate (Self report, simple test or existing data)

- Beliefs and knowledge about initiation of breastfeeding

- Beliefs about complementary feeding – timing, types, control of intake

- **_Workload and Time Availability of Caregiver_**

- Observed time in child care  (observed in sample of time or continuously)

- Recalled time in child care (24 hour recall)

- Quality of care during work time (characteristics of alternate caregivers (e.g. age, gender)

- **_Social Support for Caregiver_**

- Availability of alternate caregivers

- Community support (assessment of community institutions for child care‐feeding programs, child care programs)

- **_Psychosocial Care_**

- Caregiver/child interactions – naturalistic observation of caregiver and child for a short period (code variables such as delay to respond, type of response, level of vocalization by caregiver and child)

- Child appearance (rating of appearance either in a public place or over a period of visits)

- Caregiver’s understanding of motor milestones

# **2.2.2 Available Indicators for child care**

Of the ideal components we have information only on a few of the caring behaviors, namely, some information on breast‐feeding and complementary feeding. Table 4 lists the ideal and the available measures. In our measure of adequate care, initial breastfeeding for immediate skin‐to‐skin contact has to have occurred within the first hour after birth.<sup>12</sup> For children under the age of 6 months, adequate care consists of exclusive breastfeeding. For children 6 to 8 months of age we require complementary feedings. All children under 24 months are required to be breast‐fed. Although the surveys have information on the educational level of the mother, the presumed caregiver, there is no consensus on how to translate that information to a measure of the mother’s care giving abilities and thus we do not include it in our measure.

**Table 4: Components of Care**

|**IDEAL**|**AVAILABLE**|
|---|---|
|Workload and time availability of caregiver|No|
|Social support for caregiver|No|
|Psychosocial care|No|
|Caring Behaviors: Breast‐feeding|Yes|

> 12 For Bolivia and Peru initial breastfeeding can only be identified in the first 100 minutes (instead of 60 minutes) and the measure is not available in the IFPRI Bangladesh data.

14
area. Evidence for this negative externality is offered by the fact that in the 1987/88 cotton season, there was a complete devastation of the crop in the Krishna and Guntur districts. This was because of a failure to control the cotton bollworm. Subsequent bioassay experiments revealed that this failure arose from the substantial resistance of H. armigera to the SPs being commonly used in that area (McCaffery et al., 1989). In fact, on the basis of available evidence, it is generally agreed that the Guntur area now forms a localized "hot spot", i.e., a reservoir of Heliothis strains, resistant to SPs.

We will now describe how the creation of this "hot spot" in Guntur has led to another sort of externality. Since H. armigera is highly polyphagous we know that it can feed not only on cotton but also on pigeonpea, chickpea, and sorghum as well as some vegetable (cabbage, cauliflower, tomatoes, okra) and on fruit crops. These are major crops in the semi-arid South Eastern parts of India, being grown on the hundreds of thousands of, largely family operated farms in that area. There is evidence (see Section IV) to suggest that in the latter part of the year, from October to December, the Heliothis moths undertake extensive long range migration (helped by the prevailing winds) from the coast to the inland areas of the state. By migrating as much as 250-300 Kms inland, H. armigera ^as become a major pest of pigeonpea, chickpea, and sorghum, as well as cotton, on the farms around Hyderabad (Refer to Map 1). Samples of H. armigera taken from the pigeonpea and chickpea fields of ICRISAT (International Crop Research 7
# **Figure 14 TFP growth by region**

(percent a year) <!-- Start of picture text --> 1998~2013 1998~2007 2007~2013<br>Central East<br>North NorthEast<br>NorthWest South<br>SouthWest<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: Authors’ estimates based on NBS’s Industrial Enterprise Survey.

# **2.4 The declining efficiency of aggregate investment and credit**

As TFP growth deteriorated, GDP growth has relied much more on investment in physical capital, but such investments have brought much lower returns to growth than in the past. In response to the global financial crisis, China implemented a large public sector stimulus which concentrated on investments in infrastructure and housing. Given that public infrastructure is no longer a major constraint on growth (see next section), government investment now faces the challenge of diminishing returns. China’s incremental capital-output ratio (ICOR) has risen significantly in recent years, pointing to a declining impact of investment on growth (Figure 15).

# **Figure 15 ICOR and real GDP growth**

(yoy, percent) <!-- Start of picture text --> GDP growth ICOR<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 1999 2003 2007 2011 2015 2018<br><!-- End of picture text --> Source: NBS; authors’ calculations.

Note: The ICOR is calculated as the ratio of average real gross fixed capital formation in periods _(t-1)_ and _(t)_ to the change in real GDP between _(t)_ and _(t-1)_ .

Much of the increase in the economy-wide ICOR can be explained by lower returns to capital in infrastructure and real estate. During the years of rapid deterioration in the ICOR, the composition of investment in China changed significantly. The share of capital formation in the business sector declined, while the shares of infrastructure and housing increased (Figure 16). At the same time, the capital-output ratio in the infrastructure sector almost doubled, and 15