rafmacalaba/data-use-mentions-extended huggingface dataset:
recent improvements. Like sorghum, millet yields have grown dramatically in neighboring Ethiopia. These comparisons suggest that, by imitating successful policies implemented in countries that have seen rapid improvements in yield, Sudan too can increase agricultural productivity for all its farmers, from small-scale household farming to large-scale commercial enterprises.

Figure 7: Sorghum and Millet Yields in Top Producing African Countries <!-- Start of picture text --> (a) Sorghum Yields (b) Millet Yields<br>3000 3000 Ethiopia Niger<br>Ethiopia Nigeria Sudan Nigeria Sudan<br>2500 2500<br>2000<br>2000<br>1500<br>1500<br>1000<br>1000<br>500<br>500<br>0<br>0<br>Yield, kg/hectare Yield, kg/hectare<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> _Source:_ FAOSTAT 2018.

Agriculture remains the mainstay of most of Sudan’s poor though agricultural productivity is low. Hence, increases in agricultural productivity are expected to reduce poverty. Little is known about householdlevel constraints to productivity gains. By examining the decisions agrarian households make, we hope to shed some light on why these yields are low and how to foster productivity growth for Sudan’s agrarian population. Understanding factors driving and hampering agricultural productivity is crucial for efforts to improve growth and reduce poverty.

This study will primarily use data from the Sudan 2009 NBHS and the 2014/15 NHBPS to provide insights into constraints to agricultural productivity to inform policy reform. This study explores the complexity of rural livelihoods in Sudan, with a focus on agrarian livelihoods, by highlighting regional variations in welfare, agricultural practices and productivity. This study also empirically investigates variation in household-level crop yields and relates this measure of agrarian productivity to household welfare as well as
al. 2001). Tales of hardship caused by out-of-pocket health care payments abound, and survey data appear to be consistent with this. In 1998, 7% of an average Vietnamese household’s total consumption was absorbed by health care spending (World_Bank, SIDA et al. 2001), and in the same year one-fifth of the population spent in excess of 20% of their non-food consumption on health care (Wagstaff and van Doorslaer 2003). In 1998, a single visit to a public hospital by someone from the bottom quintile cost the equivalent of 22% of their _annual_ non-food consumption (World_Bank, SIDA et al. 2001).

During the period in question, the government had three broad measures to reduce the cost of health care at the point of use. First, in 1994 it assumed the responsibility of paying employees in commune health centers, which in the pre-reform system had been the responsibility of communes (Glewwe 2003). Fees to commune health centers were as a result of this change supposed to be non-existent, and in practice the vast majority of people (82% according to one estimate) did not pay fees for commune health center visits in this period (World_Bank, SIDA et al. 2001). Second, a patchwork system of fee exemptions was developed, oriented almost exclusively towards the cost of hospital care. This was intended to be targeted towards the poor. However, in practice the better off have benefited almost as much as the poor, though the targeting appears to have improved between 1993 and 1998 (World_Bank, SIDA et al. 2001). Third, the government introduced a health insurance scheme in 1993, known initially as Vietnam Health Insurance (VHI). Civil servants, state enterprise workers, the military and Communist party officials are all covered at the government’s expense, and private firms with more than 10 employees were and still are required to enroll their workers. A voluntary component of the program exists, but official records show that voluntary enrolment has remained very
|**18%**|**11%**|
|Parent's Education(average)|**-2%**|**-2%**|**12%**|**7%**|
|**13-50 Year Age group**|||||
|Father's Education|**31%**|**11%**|**14%**|**10%**|
|Mother's Education|**34%**|**7%**|**21%**|**12%**|
|Parent's Education(average)|**24%**|**6%**|**16%**|**10%**|

NOTES: (1) Bias is defined as [(Estimate from Full Sample – Estimate from Coresident Sample) *100] / Estimate from Coresident Sample (2) All of the Bias Estimates are Significant at the 5 Percent or Lower Level.

**Table 5: Robustness Checks for Different Age Ranges: Father-Son, and Mother-Daughter**

||**Ba**|**Bia**<br>**ngladesh**|**ses**|**India**|
|---|---|---|---|---|
||Father-Son|Mother-Daughter|Father-Son|Mother-Daughter|
|**16-60 Year Age group**|||||
|Intergenerational Regression Coeff. (IGRC)|**22%**|**32%**|**6%**|**16%**|
|IntergenerationalCorrelations(IGC)|**9%**|**4%**|**2%**|**7
Figure 6: Cumulative distribution of people management, Latin America <!-- Start of picture text -->
-4 -2 0 2 4<br>People Management Index (PISA data)<br>Private Public<br>1<br>.75<br>.5<br>.25<br>Cumulative Probability<br>0<br><!-- End of picture text -->

Notes: Cumulative distribution of the PISA-based people management index for private and public schools for 8 Latin American countries. The people management index is built out of the school questionnaire from PISA 2012 using the methodology from Anderson [2008]. Sample consists of 3075 schools: 2432 in the public sector and 637 in the private sector.

38
# <u>4.1 Statistical source and variables</u>

The statistical source used for this analysis is the World Bank‘s World Development Indicators (WDI). This database provides more than 800 development indicators, with time series for 209 countries and 18 country groups from 1960 to 2007. From the World Bank‘s World Development Indicators (WDI), we have temporal observations (T=10) for 16 Latin American countries for the period 1996–2006. We are able to form a balanced panel of data. (See descriptive statistics of the variables in Table 1.)

# [INSERT TABLE 1]

Table 2 presents a summary of the key variables used to empirically validate the combined stochastic-inefficiency model (and the control variables used in the second step analysis).

# [INSERT TABLE 2]

# <u>4.2 Empirical results</u>

The indices of TEC, TP, SEC and _G_ 0 are calculated using equations  7 a  14 and the average of these indices for the selected period (2001-2003) is presented in Table 3.

# [INSERT TABLE 3]

Table 3 shows that the major contribution to productivity growth in the Latin American countries is from technological progress. In contrast, the Technical Efficiency Change Indices are relatively low, suggesting that this component does not contribute much to productivity growth.

As to the negative contribution of SEC to productivity growth, we find several explanations with regard to this issue in Ventura-Dias, Cabezas and Contado (1999). These authors find that, with the exception of Mexico, the majority of Latin American economies are fully exploiting comparative advantage rooted in abundant natural resource endowments. Mexico firstly and Central American countries more recently have developer manufacturing activities oriented to the United States market based on a second source of comparative advantage, low-paid unskilled labor. Only Argentina, Brazil and Chile have developed competitive industries that can be classified as raw material processing (pulp and paper, nonmetallic minerals) and scale-intensive industries (steel,
.4|+1.4|235|
|Skilledproduction workers|21.3|62.9|15.9|+3.3|84|
|Unskilledproduction workers|24.6|61.9|13.5|-3.0|71|
|Non-production/service workers|17.9|72.9|9.2|-4.2|251|
|**Wages and compensation (permane**|**nt workers)**|||||
|Total|8.0|58.8|33.2|1.8|242|
|Management|4.9|70.3|24.8|2.2|270|
|Professionals|7.0|58.8|34.2|3.6|234|
|Skilledproduction workers|7.2|46.8|46.0|9.6|83|
|Unskilledproduction workers|4.6|72.8|22.6|2.7|70|
|Non-production/service workers|7.3|60.0|32.7|3.2|250|

Note: all figures are weighted. Source: CRBS 2009.

average. Wages actually increased over the period and also increased in real terms (the inflation rate was - 3.9% between July 2008 and July 2009 - World Bank 2009).

Although the financial crisis affected firms widely, the impact shows clear variation across firm characteristics. Table 2 reports descriptive OLS regressions to identify which firms are more likely to report larger changes in total sales and profits. We include controls for firm size, location, sector, labor productivity, export status and use of finance. Labor productivity is measured by value added per worker and medium and high productivity firms are those firms which have labor productivity levels in the second and third tercile of the productivity distribution. Use of finance indicates the source
experience requirements.

The textual analysis follows that of <mark>Deming</mark> and Kahn (2018) and relies on a dictionary-based approach. The authors study variation in skill demand for professionals across firms and labor markets.<sup>20</sup> They categorize a wide range of keywords found in US job ads into 10 general skill categories and thereby create a skill dictionary (see Appendix B.I). Based on this, we create our own dictionary, which comprises 15 skill categories. For each skill category, there is a set of skill requirements (or sub-categories) which can be identified by keywords or key phrases. In total, the skill categories rely on 152 skills requirements, which are defined by 764 keywords across three languages. We draw on their dictionary of keywords and extend it in various ways documented in Appendix B.II. Finally, the dictionary of 15 skill categories is also mapped to a more aggregate framework

> 19 Note that this takes into account “trimming” the number of vacancies per posting at the 99th percentile to correct for artificial increase of total of vacancies due to outliers among job postings. In addition, a rough deduplication was performed by KosovaJob in parallel with data collection. Thus, to assess the extent of our deduplication rate, we make use of our own, daily web scraped data for three of the four portals for the overlapping period, i.e. November and December 2018. Comparing initial counts from the scraped data with the deduplicated figures from the data used in this paper suggests a deduplication rate of 20 percent vis-à-vis the raw counts. Thus, at least for November and December 2018, our deduplication rate is consistent with rates estimated, for instance, in <mark>Boselli et al. (2018).</mark>

> 20 Focusing particularly on cognitive and socio-emotional skills, the authors find positive correlations between each skill and external measures of pay and firm performance.

- 11 -
bias due to unequal group sizes – as the model also assumes equal group sizes (Cameron et al., 2008; Oshchepkov and Shirokanova, 2020). Unequal group sizes are part and parcel of our data set since we have a few metropolitan areas with many observations and many metropolitan areas with a limited number of observations. Because our goal is to estimate the impact of the group level variable – urban density – the Mundlak model seems to be the best reasonable choice. In addition, the Mundlak estimation accounts for omitted variable bias by incorporating average firmlevel characteristics in the model and thus factoring in regional firm demography and socioeconomic conditions.

Although the Mundlak model addresses part of the endogeneity problems in agglomeration economics models, this estimation technique does not control for endogeneity arising from reverse causality between firms’ productivity, agglomeration economies, and mobility. Specifically, the location choices of firms resulting in lower or higher levels of urban density and mobility in some places could be induced by the migration of firms between places of low productivity to places of high productivity (Melo et al. 2009). The most common technique to deal with this reverse causality is to isolate the effect that runs from urban density to labor productivity by means of a two-stage least squares (2SLS) estimation. Based on earlier work of (amongst others) Ciccone and Hall (1996), Combes et al. (2010), and Chauvin et al. (2017), we used historical and geography variables which can be thought to only affect labor productivity through their effect on current urban density: (1) population residing in the metropolitan area in 1975 and (2) average altitude of the main center. The 1975 historical urban density variable is constructed in exactly the same way as our 2015 variable, using the delineation of that period, obtained from the Global Human Settlement Layer. The altitude variable is also obtained from the Global Human Settlement Layer and is measured as the number of meters above sea level. We perform Wu-Durban-Hausman tests after our estimations to gauge
(including distribution), and job and labor turnover. A much smaller number of studies have evaluated productivity effects. There has been very little on the effects of different labor market institutions on social cohesion, and any relevant evidence must be deduced from (mostly) indirect indicators.

The review of labor market institutions will look at the impacts listed below.<sup>10</sup> Note that the level of evidence is very uneven.

# _Living standards_

For most households, employment is the primary source of income for improving living standards, providing economic security, and avoiding poverty. This set of indicators looks at the impacts of labor market institutions on access to employment and earnings from work.

- Access to jobs/good jobs

> 10 Note that there is a potential overlap in the outcome variables for each category.  Some indicators listed in this section could arguably be put into another category.  This overlap is especially relevant for distributional and equity variables that are relevant for both living standards and social cohesion.
force as proxied by population to be influenced by: (a) wages; (b) wages in neighboring rural areas; (c) labor availability in the city’s hinterland; and (d) local public services and consumer amenities. The first of these factors is assumed to be most important as workers respond to wage compensation as measured by average per capita income in a city. Assuming that fertility and mortality rates do not vary significantly across Brazilian cities, this should translate in population growth in cities that offer better returns to labor. Results reported by Timmins (2005), using migration data from the 1991 Brazilian Population Census, support these assumptions. He showed that equilibrium residential choice is affected positively by wage compensation in a city and negatively by migration distance.

Table 13 provides results of OLS regressions of the determinants of annual average population growth rates. The first three columns of coefficients give estimates for long term trends during the 1970 to 2000 period, with initial (1970) conditions of income, market potential and infrastructure provision as independent variables. The last three columns provide the same information with a more short term focus on the 1991 to 2000 period, with 1991 data as the baseline. Income generally has a significant and positive effect on population growth during the decade of the 1990s. This suggests evidence in favor of the hypothesis that migrants go to the cities with high initial per capita incomes. A Chow test shows no statistical difference between the Northern and the Southern regions. However, there is no significant relationship in the long term between 1970 and 2000, possibly because the fortunes of individual cities have fluctuated significantly during those three decades.

30
br>30<br>20<br>25 20<br>15<br>20<br>10 15 15<br>5 10<br>10<br>5<br>0<br>0 5<br>0<br>China US India Korea Brazil<br><!-- End of picture text --> Source: Author‘s calculations based on data from World Bank, World Development Indicators database.

That shift in economic weight is likely to produce major benefits for the world economy, with positive effects for both high-income and developing countries. For high-income countries the growth of emerging economies will expand markets for their exports of capital goods and intermediate goods. For many developing countries that are still major producers of agricultural and natural resource commodities, higher consumption and production levels in the new growth poles will continue to support adequate prices for their commodity exports. In addition, firms and governments in emerging economies will provide funds for infrastructure and natural resource investment in developing countries.
37/ could be associated with a cycle of short run employment losses, inflation reducing the real value of the minimum wage and restoring employment, and then an increase in the minimum wage starts the process all over. In a growing economy with complete coverage, the net effect of a one-time increase in the minimum wage is to reduce the rate of growth of 32/Minimum wages in Jakarta, for example, in 1994 are about 1.5 times their 1991 value. Wage trends in 1993 Sakemas data show that nominal manufacturing wages increased at about 11 % between 1991 and 1992. Assuming the same rate of growth for the next 2 years and assuming manufacturing growth rates in Jakarta are the same as the Indonesia wide rates, this would make the ratio slightly above the 1991 ratio. At any rate, with annual indexation to the CPI, this ratio is unlikely to decrease without real wage increases.

_3/Although in Chile real minimum wages have grown at 7% per annum since 1989 while average wages have grown at 4% per annum in real terms.

34/These are approximate calculations based on available published tabulations from the Sakemas household surveys. In Chile, about 5-6% of workers are reportedly at the minimum wage and almost no workers are below the minimum wage. In Bell (1994) 1988 household survey data showed that 2.16% of formal sector workers were below the minimum wage, 24.18 were below 1.5 times the minimum wage and 48.02% were below twice the minimum wage. For full time male workers in the informal sector, the corresponding numbers were 16.48, 44.74 and 62.94 respectively.

35/See Bell, 1994 for minimum wages relative to income distributions in Mexico and Columbia. The numbers for Indonesia appear to be more like the Columbia numbers where distortionary impacts of minimum wages were found. Note that the Columbia numbers were from manufacturing firm data and may underestimate the number of workers at the lower end of the income distribution.
in TFP across countries. Here, management practices are found to explain at least a fifth of within-country heterogeneity in firm performance, thereby suggesting that firm- and sectorspecific factors are at least as important as the general business environment in shaping firm performance (Bloom et al., 2016).

The four sets of practices that comprise an overall management score in the World Management Survey include: operations, performance monitoring, incentives, and target-setting (Bloom and Van Reenen, 2007). Questions in the operations category seek to understand the production process and the adoption of lean manufacturing techniques, while those in monitoring category solicit responses on the types of production and input data that are collected as well as how the performance of capital and workers is measured. Questions on practices relating to the hiring and firing of workers as well as the criteria for bonuses and promotions comprise the incentives category. Finally, the target-setting category evaluates practices relating to defining and communicating organizational goals, setting a time frame for implementation, whether or not these goals are demanding, and if there are consequences for failing to achieve them. In prior studies, these scores are averaged to obtain an overall management score.

In addition to focusing on the composite management score, studies have also considered specific categories of management practices. Weaknesses in management practices, for example, relating to data driven performance monitoring cripples the overall management score in Croatia (Grover et al., 2019). Monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and track them periodically over time. By comparison, Croatia firms’ average score on practices relating to target setting and incentives is relatively closer to that of United States firms. Incentive practices relate to how managers and non-managers are awarded bonuses and promotions. The findings for Croatia and the Russian Federation regarding the weaknesses of these subindices are similar, that is, monitoring practices are weaker (Grover et al., 2019; Grover and Torre, 2019). By comparison,
- IPCC 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: agricultural, forestry and other land use, National Greenhouse Gas Inventories Program, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). IGES, Japan.

- Macedo, I.C., et al. Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy 2008: 32(4).

- Menichetti E., and M. Otto. 2009. Energy balance and greenhouse gas emissions of biofuels from a life-cycle perspective. in R.W. Howarth and S. Bringezu (eds). Biofuels: Environmental Consequences and Interactions with Changing Land Use, Proceedings of the Scientific Committee on Problems of the Environment (SCOPE) International Biofuels Project Rapid Assessment, 22-25 September 2008, Gummersbach Germany, Pages 81-109.

- OECD. Biofuels Support Policies: An Economic Assessment. Organisation for Economic Cooperation and Development (OECD). Paris, 2008.

- Ogg, CW. Environmental challenges associated with corn ethanol production. Geopolitics of Energy 2008: 30(1):13-18.

- RFA. The Gallagher Review of the Indirect Effects of Biofuels Production. Renewable Fuels Agency (RFA). East Sussex, UK, 2008.

- Searchinger, R. Heimlich, R.A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, T. Yu. Use of US Croplands for Biofuels Increased Greenhouse Gases Through Land-use change, Science Express (Feb. 7, 2008)

- Timilsina, Govinda R. and Ashish Shrestha (2010). Biofuels: Markets, Targets and Impacts, World bank Policy Research Working Paper 5364, The World Bank, Washington, D.C.

- Timilsina, Govinda R., John C. Beghin, Dominique van der Mensbrugghe and Simon Mevel, 2010. The
6%|
|Navoi|1.2%|11,952|<br>2.6%|0.9%|8,596|<br>2.0%|
|Karakalpakstan|0.9%|16,523|<br>3.6%|0.1%|990|<br>0.2%|
|Kashkadarya|0.3%|9,856|<br>2.2%|1.0%|28,374|<br>6.4%|
|Samarkand|6.4%|229,920|<br>50.5%|0.1%|5,322|<br>1.2%|
|Syrdarya|1.6%|13,215|<br>2.9%|0.2%|1,759|<br>0.4%|
|Surkhandarya|0.0%|-|<br>0.0%|0.5%|12,923|<br>2.9%|
|Tashkent city|2.8%|67,297|<br>14.8%|9.4%|230,527|<br>52.3%|
|Tashkent|0.7%|20,139|<br>4.4%|1.2%|33,633|<br>7.6%|
|Khorezm|0.3%|5,231|<br>1.1%|5.3%|94,764|<br>21.5%|
|Total|1.5%|454,936|<br>100.0%|1.4%|440,764|<br>100.0%|

_Notes: Author’s Calculations. Reported estimates derived from mahalla records and