rafmacalaba/datause-encoder-data huggingface dataset:
||**MWK per**|**USD per**|**% of**|**% of**|
|---|---|---|---|---|
||**MT**|**MT**|**DVA**|**SV**|
|Domestic costs|56,682|404.87|75%|48%|
|Duties and tax|18,737|133.83|25%|16%|
|Additional expenses|-|-|0%|0%|
|**Total DVA**|**75,418**|**538.70**|**100%**|**64%**|
|Foreign costs|42,182|301.30|56%|36%|
|**Total SV**|**117,600**|**840.00**|**156%**|**100%**|
|**foreign conv**|**factors(cf)**|**dom**|**estic conv**|**factors(cf)**|
|% foreign|35.87%|tax|as % DVA|0.248|
|foreign cf|1.107||extras|-|

52. **Value chain build-up.** Finally, the table below summarizes the build-up of value chain cost components for hybrid seed. The values for packed seed are measured at the factory gate before distribution to local retailers. As defined by the value chain methodology, values at this level include all costs carried forward from farm production plus farmer profits which are counted at the packed seed stage since this is a cost paid by the factory. As shown, actual farm gate costs only amount to 31% of total SV; farmer profits add a further 10% to total SV; and factory costs (including profit to the seed company) add a further 59% to total SV before retail distribution.

||**PRODU**<br>**FARM G**|**CT**<br>**ATE**|**READY FO**<br>**PACKED**|**R USE**<br>**SEED**|
|---|---|---|---|---|
||MWK|USD|MWK|USD|
|**Domestic Value Added**|||||
|Costs & mark-ups|8,183|58.45|57,306|409.33|
|Official duties & tax|4,797|34.27|18,726|133.76|
|Additional costs|-|-|-|-|
|**Total DVA**|**12,981**|**92.72**|**76,032**|**543.09**|
|Foreign costs|24,806|177.19|41,568|296.91|
|**Total Shipment Value**|**37,787**|**269.91**|**117,600**|**840.00**|

53. As a strategy to reduce the cost of hybrid seed, some further examination of factory costs may be in order. Utility costs (including the cost of power disruptions), taxes on imported seed dressing, and taxes on other business operating expenses are areas where possible savings could be achieved. Rents (i.e. royalties) by the seed company on imported foundation seed also appear significant and could be an area where Malawi research institutes could play a more dynamic and active role in helping to bring down the cost of this important input.

21
# Firm Recovery during COVID-19: Six Stylized Facts<sup>∗</sup>

Xavier Cirera (World Bank) Marcio Cruz (World Bank) Arti Grover (World Bank)<sup>†</sup> Leonardo Iacovone (World Bank) Denis Medvedev (World Bank) Mariana Pereira-Lopez (World Bank) Santiago Reyes (World Bank)

**JEL classification** : D22, L20, L25, O10

**Keywords** : COVID-19, Crisis, Firms, Recovery, Employment, Sales, Uncertainty

∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that have supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible (a complete list of all colleagues, collaborators, and institutions is available in the Appendix). Likewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. We are indebted to Edgar Avalos, Tanay Balantrapu, and Joao Belivaqua Basto for research support, and to Besart Avdiu, Elwyn Davies, Umut Kilinc, Kyungmin Lee, Franklin Okechukwu Maduko, Stavros Poupakis, Jesica Torres, and Trang Thu Tran for their efforts in coordinating BPS data collection and their leadership of complementary BPS research initiatives which have informed this paper. We gratefully acknowledge feedback and advice on survey questionnaires from Nicholas Bloom, Francisco Campos, Ana Paula Cusolito, Mark Dutz, Caroline Freund, Jorge Rodriguez Meza, David McKenzie, Silvia Muzi, and Bilal Zia. We thank Mona Haddad and Caroline Freund in their consecutive roles as Global Director for Trade, Investment, and Competitiveness at the World Bank for their guidance and strategic support of the BPS initiative, and USAID, the World Bank, and the IFC for financial support. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

> †Corresponding author, email: agrover1@worldbank.org
if anything, the richest economies tended to have smaller, not greater growth collapses.<sup>7</sup> Third, Table 2 shows regressions of growth collapses on income dummies. Again, the dummies for advanced and emerging economies are not statistically different from each other. If anything, emerging economies suffered greater collapses. On the contrary, low-income economies did suffer smaller collapses.<sup>8</sup> These findings stand in contrast with the performance of emerging and advanced economies around previous crisis episodes. The upper-right panel of Table 2 shows regressions of growth collapses on income and regional dummies during the Russian and Asian crises of 1997–1998, which we use as benchmark. During those crises, emerging and low-income economies across all regions suffered large growth collapses, while advanced economies suffered only modest growth decelerations. In addition, collapses do not seem to be associated to the level or the square of GDP per capita. The results in Table 2 for both the 1997-1998 and 2008-2009 crises are robust to weighting observations by population and by pre-crisis GDP measured in U.S. dollars. Moreover, the results are robust to excluding some or all European economies, as they may disproportionately affect the coefficients for advanced economies.

The timing of the 2008–2009 crisis renders annual data on GDP inadequate for a thorough analysis of the economic cycle that includes, for instance, the duration of downturns: peaks and troughs in economic activity do not necessarily coincide with calendar years. Hence, we turn to data on IP at a monthly frequency. These data allow us to construct different measures of the severity and duration of the recession that cannot be built using data at the annual frequency. Figure 3 displays the evolution of IP indexes across income groups relative to

> 7 This result also holds if we drop the economies with growth collapses greater than 19 percentage points.

> 8 As a consistency check, we report in Table 1 several measures of GDP growth, collapse, and recovery. For example, we arrive at a similar conclusions if we define growth collapse as real GDP growth in 2009 minus the average real GDP growth rate during 2000–2007.

10
Table 1. Debt in major country groups

|||(Perc|Levels<br>ent of GD|P)||Chan<br>(Percentage po|ges<br>ints of GDP)|
|---|---|---|---|---|---|---|---|
||2010|2015|2018|2019|2020|2010-15|2015-20|
|World||||||||
|Government debt|76.7|79.7|82.3|83.7|98.7|3.0|19.0|
|Private debt|137.0|144.9|147.8|149.1|164.6|7.8|19.7|
|Total debt|213.7|224.5|230.1|232.8|263.3|10.8|38.7|
|Advanced economies||||||||
|Government debt|97.4|103.2|102.8|103.9|122.8|5.8|19.5|
|Private debt|166.6|162.6|164.2|164.8|179.2|-4.1|16.6|
|Total debt|264.1|265.8|267.0|268.7|302.0|1.7|36.2|
|EMDEs||||||||
|Government debt|37.4|43.5|51.8|54.0|63.1|6.1|19.6|
|Private debt|80.8|117.3|122.6|125.5|142.1|36.5|24.8|
|Total debt|118.2|160.8|174.4|179.6|205.2|42.6|44.4|
|EMDEs excluding China||||||||
|Government debt|38.6|44.7|50.4|51.9|60.6|6.1|15.9|
|Private debt|57.7|67.7|67.9|68.2|75.6|10.1|7.9|
|Total debt|96.3|112.4|118.3|120.1|136.2|16.2|23.8|
|LICs||||||||
|Government debt|39.8|51.3|58.7|59.3|68.4|11.6|17.0|
|Private debt|12.5|12.3|12.6|12.6|13.2|-0.1|0.9|
|Total debt|52.2|63.6|71.4|72.0|81.5|11.4|17.9|

Source: Kose et al. (2021).

Note: Data are available for up to 192 countries, including 39 advanced economies, 153 EMDEs, and 24 LICs.

32
As graphically exhibited in Figure 6, students with household characteristics correlated with low re-enrollment probabilities are much more likely to enroll due to the treatments. The savings treatment causes a 5.6 percentage point increase in reenrollment and the tertiary treatment causes an 8 percent increase. Those students most likely to re-enroll show no effect at all. This difference is statistically significant at the five percent level (p-value of 0.045) for the savings treatment and the ten percent level (p-value 0.06) for the tertiary treatment.

Because we have data for all of the students in our sample, we can assess the comparability of our sample of 68 schools by re-estimating the treatment effect just on those 68 schools and comparing the estimates to those obtained from the entire sample. The estimates from the students initially enrolled in the 68 schools are provided in Panel B. While the baseline re-enrollment rates are higher in these schools,<sup>15</sup> the estimated treatment effects are very similar to those in Panel A.

Panel C contains the estimated treatment effects for students in the 68 schools who gave a baseline survey, repeating the exercise from Panel B of Table 5 using the enrollment data. While the point estimates for the savings, tertiary, and basic treatments in Suba all have positive point estimates, the magnitudes are again smaller than for the full sample, but not statistically significantly so. Those students least likely to reenroll show the largest response to the treatments. For these students, the savings treatment causes an increase in enrollment of 4.3 percentage points and the tertiary treatment causes an increase of 4.8 percentage points, statistically significant at the five and ten percent levels respectively.

Unlike the previous panels, Panel D contains the same estimates, but with our selfreported enrollment measure from the follow-up survey. The results demonstrate the importance of not relying solely on self-reported data to assess incentive programs. Comparing these estimates to those in Panel C, it seems that families significantly over estimate the enrollment rates of these students relative the administrative measure. This over-estimate is significant enough that almost all of the students report attending school. This then seems to create a ceiling effect that prevents us from detecting a treatment effect using the self-reported data.

Panel E also contains estimates using self-reported data but using the self-reported attendance measure for students’ attendance in 2006. Again, families seem to dramatically

> 15 These schools are all public schools.  As a result, the difference in reenrollment levels may due to the relative completeness of the data than to actual student behavior.

- 24 -
|**Table 6.2:**<br>|**Real Ave**<br>**1995 and**|**rage Ea**<br>**2001**|**rnings,**|
|---|---|---|---|
|**_Brazil_**|**1995**|**2001**|**Change**<br>**1995-01**<br>**Percent**|
|Formal|796.1|692.8|-13.0|
|Informal|393.0|393.2|0.0|
|Self Employed|633.8|533.5|-15.8|
|**_Northeast_**||||
|Formal|584.7|533.1|-8.8|
|Informal|270.2|268.0|-0.8|
|Self Employed|370.5|335.5|-9.4|
|**_Bahia_**||||
|Formal|604.1|594.8|-1.5|
|Informal|282.61|256.11|-9.4|
|Self Employed|419.53|364.07|-13.2|
|_Source: _Author’s calc|ulationbased o|n PNADs19|95 and2001.|

In the longer term, the slowdown in Bahia’s population growth (see Section 2) will affect poverty through its broader effects on the labor market. The rapid population growth experienced in previous decades has resulted in an elastic supply of unskilled labor. As a result, wage levels have remained low -- except for high skill, well educated workers --even in times of high economic growth. Bahia’s lower fertility rate will eventually moderate this factor. Declines in the growth of the labor force will reduce the supply of labor. This may result in increasing pressure on wages, which in turn may prompt the substitution of capital for labor, increasing labor productivity, and setting off a virtuous cycle of rising wages and rising productivity.

The state government should support the federal government’s attempts to reform the labor code in order to reduce costs of employment creation in the formal sector. The labor code in Brazil relies more on a rigid legislative code than on collective bargaining. The reform of the labor code is the best way to encourage increased formal employment, which should also help reduce the formal/informal divide. By shifting that balance, Brazil could reduce labor turnover, increase productivity, decrease payroll costs, expand the formal labor market and social security coverage, and lessen the reliance on litigation. Possible measures would entail realigning incentives for hiring, retaining, and firing workers and relaxing rules, including mandated minimum non-wage benefits, to leave more to decentralized collective bargaining.

Child labor is still a severe problem in Bahia as well as in the Northeast as a whole. International evidence shows that child workers tend to be poor and complete fewer 29
inappropriate in other contexts (such as in the US), it worked well for this study - all of the women who were selected from this sampling frame did in fact engage in sex-for-money.

Though SHCP had been relatively successful in enrolling women into their program, it was possible that women who joined the peer groups were not representative of the average sex worker in the area.<sup>14</sup> To construct as representative a sample as possible, we identified other women who met SHCP’s definition of a sex worker but who were not involved in the peer groups. To do this, the enumerators visited each peer group and asked members to list all the women she knew who fit SHCP’s description of a sex worker, whether they were in a peer group or not. After compiling this list, duplicates were eliminated by the peer group educators. The resulting “universe” of sex workers we identified for our sampling frame from this method thus includes all women in the peer groups as well as all other women identified by any peer group member.

Overall, we identified 1,205 women in this manner. Since the population of Busia Town is only 44,196, and assuming that the age distribution of the population fits that of the 2003 Kenyan Demographic and Health Survey (in which women aged 15-49 make up 21.9 percent of the rural population), we estimate that roughly 12.5 percent of women in Busia earn some income from transactional sex.<sup>15</sup> From this sample frame, we randomly sampled women to participate in the project (stratified by the peer group in which they were identified). Initially, we sampled 248 women. Of these, seven women refused to participate in the project, and another 49 dropped out before data

> 14. However, Robinson and Yeh (2011) show that there is no difference in observable characteristics between those in the peer groups and other women who supply transactional sex.

> 15. Though estimates of the size of the transactional sex market are hard to come by, this figure is higher than that in other studies (for instance, Morison et al. 2001 or Venditte et al. 2006). We attribute this in part to the fact that Busia is a hotspot for commercial sex within Kenya, and in part because our enumerators (and SHCP) were known and trusted by the sex workers in the community. As a result, we were likely able to identify a relatively large percentage of the women in the market.

13
**7 Appendix 2: Gender Heterogeneity**

Table B1: Results Pooled: Earnings and Work - Heterogeneity by gender

||(1)<br>Earned Income<br>Last 7 Days|(2)<br>Main Job Earnings<br>Last 7 Days (USD)|(3)<br>Main Job Hours Worked<br>Last 7 days|
|---|---|---|---|
|Offered Training|0.04|8.34<sup>_∗_</sup>|3.01<sup>_∗∗_</sup>|
||(0.03)|(4.46)|(1.49)|
|Offered * Female|0.04|-5.40|0.75|
||(0.04)|(5.65)|(2.08)|
|Control Mean: Men|0.77|40.27|30.34|
|Control Mean: Women|0.60|29.57|24.99|
|Observations|2,597|2,597|2,597|
|p-value female|0.02|0.40|0.01|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

Table B2: Results Pooled: Work Categories - Heterogeneity by gender

||(1)<br>Employee|(2)<br>Apprentice/Intern|(3)<br>Employer|(4)<br>Self-Employed|
|---|---|---|---|---|
|Offered Training|0.02|0.00|-0.01|0.01|
||(0.03)|(0.03)|(0.01)|(0.02)|
|Offered * Female|0.03|0.02|0.01|-0.01|
||(0.05)|(0.04)|(0.01)|(0.03)|
|Control Mean: Men|0.42|0.22|0.04|0.17|
|Control Mean: Women|0.30|0.21|0.03|0.19|
|Observations|2,597|2,597|2,597|2,597|
|p-value female|0.11|0.49|0.92|0.89|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

40
# **‘Green’ growth, ‘green’ jobs and labor markets**

## Dr Alex Bowen<sup>a</sup>

a Principal Research Fellow, Grantham Research Institute on Climate Change and the Environment and Centre for Climate Change Economics and Policy (CCCEP), London School of Economics and Political Science.

### **JEL:** J23, J24, Q01, Q54, Q56

**Keyword:** sustainable development, climate change, green job, green growth

**_Acknowledgements:_** This paper was produced as background material for the report on Green Growth managed by the Office of the Chief Economist of the Sustainable Development Network.  I would like to thank Uwe Deichmann, Stéphane Hallegatte, Michael Spackman and particularly Mike Toman for helpful comments.  I remain responsible for all errors and omissions.
P:c,Planning, and Ruchw |

World Devehopnwnt Report Social security systems usually cover less than 10 percent of the population of developing countries. Yet in a number of countries, these systems make up a large proportion of public sector revenues and expenditures.

Most social security systems in developing nations are nnning substantial surpluses. If the systems are to meet future obligations, reserve funds and surpluses should be invested in safe assets with real positive rates of retum.

But many countries lack well-developed financial markets and good domestic opportunities for productive investment. The government usually controls social sec'nrity systems - and often uses the surpluses to finance government deficits.

Economists are divided over how social security systems affect pri-,ate saving and capital accumulation, which leads to long-term growth. They also disagree on the inefficiencies which social security systems introduce into labor markets.

It is clear, however, that investing social security funds in government securities increases the risk of the social security system's insolvency.

Governments facing fiscal difficulties find it tempting to expand the money supply and drive up inflation. This cuts government obligations to social security reserve funds by reducing the real value of the investments in government bonds.

There is also conrsant pressure for many govenmmen **t** s oincrease benefits without increasing contributions. Although such actioi is expeaient, it often proves unsustainable. A govenmment's ability to resist such pressure thus has a direct bearing on the long-term success of the system.

One system that stands out as sound, with wisely invested reserves, is the regulated private social security system in Chile.

This is a background paper for the 1988 World Development ReporL Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rhoda Blade-Charest, room S13-060, extension 33754.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations aie less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright i: 1988 by the International Bank for Reconstruction and Development!The World Bank
<!-- Start of picture text --> Figure 1. Provisions as Share of Total Credit (%)<br>3.0<br>2.5<br>2.0) a<br>1,5<br>10<br>0.5<br>0.0<br>1a 1999 2000 2001 20e2 2003 2004 2005 2006 2007 2<br>—Total Provisions — Specific Provision<br>=~ Generic Provision —<= Statistical Provision<br><!-- End of picture text -->
significantly below the average for Kenya as a whole. The Human Poverty Index (44.3%) for Nyanza is amongst the highest in Kenya. It is estimated that over 240,000 people in the coastal areas of Nyanza were directly or indirectly employed by the fishery in 1995 (Figure 12).

It is widely acknowledged that aggregate income increased significantly within the Lake Victoria fishery as a whole through the 1980s and 1990s (Reynolds and Greboval., 1989; Greboval and Mannini, 1992; Harris _et al_ ., 1995; Namisi, 2000). The fishery also supports a range of support sectors, for example vessel construction and repair and fishing equipment manufacture. Thus, it has been estimated that the total economic benefit of the fishery at the end of the 1990s was around KSh7.21 billion (US$91.8 million) (Bokea and Ikaiara, 2000)<sup>26</sup> .

**Figure 11. Landed volume of fish in Kenyan waters of Lake Victoria by species, 1990-2002:**

<!-- Start of picture text --> 250000<br>200000<br>150000<br>100000<br>50000<br>0<br>1990 1993 1994 1995 1996 1997 1998 1999 2000 2001 2001<br>Nile Perch Other<br>Tonnes<br><!-- End of picture text --> _Source: Fisheries Department._ Although infrastructure remains poorly developed in the region bordering the Kenyan shore of Lake Victoria, there have been some improvements as a direct result of the Nile perch fishery. Most notably, feeder roads to main beaches have been established or improved in order that industrial processing facilities can source from remote landing sites (Harris _et al_ ., 1995). In turn, this has permitted access to other vehicles, including public transport and perhaps ironically, afforded greater access to local markets.

The expansion of the Nile perch fishery initially had a dramatic impact on the productivity of the fishing effort in Kenyan waters of Lake Victoria. Thus, the catch per unit effort (CPUE) increased from seven tonnes per boat in the late 1970s to around 30 tonnes per boat in 1991 (Figure 13) (Reynolds _et al_ ., 1995 **)** However, more recently, the expansion in the number of

> 26 Whilst aggregate measures suggest the economic benefits of the Nile perch fishery have been considerable, there is much less consensus on the distribution of this additional income and the extent to which the communities have benefited<sup>26</sup> .  It is suggested that the significant income from the fishery quoted in official documents is not reflected in the lifestyles of the local fisher-folk (Jansen, 1997; Ong’ang’a, 2002).

32
# Administrative Valuation of Soviet Agricultural Land: Results Using Lithuanian Production Data

# by Karen Brooks*

# Table of Contents

|Price Reform and Land Valuation|5|
|---|---|
|Lithuanian Producer Prices|5|
|Output Prices, Land Quality, and Profitability|7|
|Marginal Value Products of Land with Current and Reformed Prices|9|
|Border Prices at Which Exchange Rate?|12|
|Price Reform and Demand Side Constraints|14|
|Conclusions|16|
|Tables|17|
|References|25|
|Endnotes|26|

* Agriculture and Rural Development Department, the World Bank and Department of Agricultural and Applied Economics, University of Minnesota, St. Paul, MN 55108.

This work was supported by the exchange agreement between the Center for Agricultural and Rural Development, Iowa State University, the Lithuanian Scientific Research Institute for Agricultural Economics in Vilnius, and the National Science Foundation. Willis Peterson and Benjamin Senauer provided comments on an earlier draft. I particularly thank Robert Dorfman for his useful comments.