rafmacalaba/datause-encoder-data huggingface dataset:
from others that would also be consistent with this fact and yet point to other explanations that can potentially lead to contradictory policy advice. Therefore, in this paper we try to disentangle the precise way in which asset wealth matters for adoption.

The data, which were collected by the authors in 2002 and 2004, cover all 65 boatowning fishermen of a fishing village where the first fibre boats appeared in 2000. They include a census as well as time series of individual fish sales. These unique data allow us to discard the view that delayed adoption by poor households is due to the smaller scale of their entreprise as each individual operates the same fibre boat. In addition, aversion to income fluctuations is not likely to be important as there is evidence of informal insurance arrangements among boat owners (Platteau, 1985). Since we do find that wealthier entrepreneurs adopt earlier after controlling for individual ability, we conclude that credit constraints faced by relatively poor boatowners are important. To a lesser extent, we find evidence of aversion towards uncertainty about the gains from the new technology.

This paper is related to the work of Platteau (1984) and that carefully document the introduction of mechanized boats in Kerala. They study an example of how globalization drastically changed the rural landscape. In the words of Platteau, _“by revolutionizing the traditional techniques and methods of catching, preserving and distributing fish, the project succeeded beyond expectations in achieving a ‘revolution’ [...] that changed life in many ways.”_ This process exemplifies what Nissanke and Thorbecke (2004) have in mind when they point out that the relationship between globalization and poverty is complex and may even be non-linear as globalization affects poverty through many channels. In contrast, we can assert that the only major change from 2000 to 2004 in the village we study was the introduction of FRPs. This enables us to focus on the effects of a single channel, namely, technology diffusion.

On a more macro level, the paper is interesting because we explore the dynamics of income inequality during the process of technology diffusion. Contrary to some empirical literature on the effects of globalization, we find that inequality follows Kuznets’ wellknown inverted U-shaped curve.<sup>2</sup> Initially, the technological innovation widens the gap 2These studies reject the Kuznets hypothesis, but they focus on the impact of _initial_ inequality on 3
RSEARCH WORKING PAPER _1793_ **FLI(Y**

# $1msu-M-sral: fin jin i

After PoYan i s rem ab'e '-asbilzation and liberalizationi in 19903 the ecourrny aced hree related problems: high wage andi e _lairis:_ a rising number of pensioners and uneirnployvcu worKers, and a budget crisis, especially in 199<sup>1</sup> anc i 7992. De crornbrugghe studies rhe role that w.eage anrd pension p.essures played in this crisis. !He also explainss tht pcrsisrerLeC of the high tax wedge thai: later helped overcon-le **thf** budget :risis.

ri he nosirive effect on revenues of higher wages and higher tax raLtes o'ld not compensatc for both the inevitable IosS in profit taxes anI the cxcessive growth of spending cn -eplacement irncom.

uovu. 7. rfartuas constructed fo. ad sp ndnvg show **tai** rIsing number of sociai benefiiC earners (pensions, ui-e.,.-ipioyment) tc _o_ bbe-'srIble for much of the budgetary burden. But they also show that the better protection of social income (over other income) explains part of the burden.

Part of the employment loss and social spending can be ascribed to the excessive wage recovery of late 1990 and 1991. Insiders set wages ignoring the unemployed and exploiting the pension system in a context of uncertainty about prfi;ts and productivity, at a **time** when there was strong popular support for the protection of replacement income.

Dr Crombrugghe recommends pension reform and caution about wages: the current economic recovery should create jobs, not raise wages, he says, and ensuring equitable opportunities for all requires moderating the incorne claims of the best-protected groups.

This paper - a jont prodonet rbthe Gfficec of the Senior Vice President, Development Economics and Chief Economist, and Research Advisory Staffis part of 'Fiscal Reform in Poland (RPO 678-96)," a study funded by the Bank's Research Support Budget. Copies of this paper are available free firom the World Bank, i8 18 H Street NW, Washington, DC 20433. Please contact **Mani** Jandu, room N7-032. telephone 202-473-3103, fax 202-522-0304, Internet address m)andulE@-wvorldbank.org. June 1997. (48 pages) _Policy e£ Rxesearch Wokicrg Paper 3eries dissernixnaies the findings of work in progress to encourage the exchange of ideas about 4elo p'nenr .;ssues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers_ **_$ry c,_** _rbe_ 'h.-ars _of the au7hors and should be cited accordingly. T he fildings, interpretations, and conclusions expressed in this paper are entire'tnose t.e -urnors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the_ L _-____ries they .-epre5e,9t._
Policy Research Working Paper 9666

# **Abstract**

Between 2014 and 2016 unprecedented and consecutive climatic shocks ravaged Malawi, one of the poorest countries in the world. The largest ever emergency relief operation in the country’s history ensued. The pathways and extent to which the humanitarian response protected livelihoods remain under researched. This paper uses a unique data set that combines longitudinal household survey data with GIS-based measures of weather shocks and climate conditions and longitudinal administrative data on the World Food Programme’s aid distribution. The paper aims to understand the drivers of humanitarian aid and evaluate the impact of aid and weather shocks on outcomes related to household production and consumption in Malawi. The analysis shows that droughts and floods had consistent negative impacts on a range of welfare outcomes, particularly for households that were subject to sequential shocks. Aid receipt is demonstrated to attenuate such impacts, again particularly for households that experienced the shocks consecutively. Households living in areas subject to a weather shock and with higher World Food Programme aid distribution were more likely to receive food aid, partially explaining the success of aid in mitigating the impacts of shocks. However, there is significant scope for improving the criteria for targeting humanitarian aid beneficiaries.

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 tkilic@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
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Table A.3: Descriptive relationship between self-employment and migration including respondents not employed at baseline, all survey waves

||**Any**<br>**migration**|**Internal**<br>**migration**|**International**<br>**migration**|**Any**<br>**migration**|**Internal**<br>**migration**|**International**<br>**migration**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Self-employed|-0.027***|-0.025***|-0.001|-0.027***|-0.025***|-0.001|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.001)|
|Not working|0.010***|0.007***|0.003***|0.006*|0.008**|-0.000|
||(0.002)|(0.002)|(0.001)|(0.003)|(0.003)|(0.001)|
|Aged 25 to 34|-0.106***|-0.100***|-0.005***|-0.106***|-0.101***|-0.004***|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.001)|
|Aged 35 to 44|-0.146***|-0.137***|-0.007***|-0.146***|-0.138***|-0.006***|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.001)|
|Aged 45 to 54|-0.154***|-0.145***|-0.007***|-0.154***|-0.146***|-0.006***|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.001)|
|Aged 55 to 65|-0.163***|-0.153***|-0.008***|-0.163***|-0.153***|-0.008***|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.001)|
|Years of education|0.006***|0.005***|0.000***|0.006***|0.005***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Female|0.008***|0.014***|-0.005***|0.008***|0.015***|-0.005***|
||(0.002)|(0.002)|(0.000)|(0.002)|(0.002)|(0.000)|
|arcsinh(income)||||-0.000|0.000|-0.000***|
|||||(0.000)|(0.000)|(0.000)|
|Wage worker mean|0.119|0.109|0.010|0.119|0.109|0.010|
|Sample size|185,827|185,773|156,712|185,579|185,525|156,464|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the CFPS 2010 and 2016, ELMPS 1998, 2006 and 2012, IHDS 2005 and 2011/12, IFLS 2000, 2007/08 and 2014/15, MxFLS 2002, 2005/06 and 2009/12, LSMS-ISA 2010/11, 2012/13 and 2015/16, KHDS 1991/94, 2004 and 2010, and PSID 2001, 2007 and 2017. We drop respondents who are not self-employed, paid workers, or not employed in the baseline wave of each survey. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country-wave fixed effects. Columns (4)-(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave. Standard errors in parenthesis are clustered at the respondent level.

42
**Table C.2:** Manipulation Checks: Community Monitoring (CM)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|(1) CM meetings by PLAN, Concern, or IRC|0.435|0.269|0.421|0.115|506|
|took place||||||
||(0.497)|(0.047)<sup>_∗∗∗_</sup>|(0.050)<sup>_∗∗∗_</sup>|(0.053)<sup>_∗∗_</sup>||
|(2) How many CM meetings took place?|0.897|1.055|1.631|0.483|498|
||(1.218)|(0.142)<sup>_∗∗∗_</sup>|(0.156)<sup>_∗∗∗_</sup>|(0.157)<sup>_∗∗∗_</sup>||
|(3) Was village informed of meeting outcomes?|0.411|0.266|0.403|0.127|506|
||(0.493)|(0.049)<sup>_∗∗∗_</sup>|(0.054)<sup>_∗∗∗_</sup>|(0.055)<sup>_∗∗_</sup>||

_Notes:_ Treatment effects are estimated using ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table C.3:** Manipulation Checks: Non-Financial Awards (NFA)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|(1) Clinic staff heard of NFA?|0.476|0.331|0.190|0.473|254|
||(0.502)|(0.063)<sup>_∗∗∗_</sup>|(0.073)<sup>_∗∗∗_</sup>|(0.064)<sup>_∗∗∗_</sup>||
|(2) Clinic participated in NFA?|0.155|0.428|0.198|0.657|254|
||(0.364)|(0.061)<sup>_∗∗∗_</sup>|(0.067)<sup>_∗∗∗_</sup>|(0.063)<sup>_∗∗∗_</sup>||

_Notes:_ Treatment effects are estimated using ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A15
A poor business environment in urban Kinshasa may be at the heart of underemployment. The DRC remains among the most challenging countries in the world to do business. The 2017 Doing Business Report ranks the country 184 of 190 and notes that it performs poorly across all indicators. Similarly, the 2016–2017 Global Competitiveness Index ranked the country 129 of 138. A lack of basic services and core infrastructure—coupled with a weak regulatory environment— coalesces to impede firm entry and the creation of good-quality jobs. This results in a proliferation of informal self-employment jobs with low productivity, and of erratic piecemeal activities with irregular working hours.

# **2.3. Job characteristics**

A large proportion of jobs in Kinshasa are private, informal, and/or self-employment jobs. In 2018, public sector jobs accounted for only 14 percent of jobs in Kinshasa.<sup>4</sup> Around 60 percent of jobs are informal, in that they do not offer a national identification number (IDN) and/or do not keep formal accounting books (Table 3). About half the jobs in Kinshasa are wage jobs, while the other half are self-employment jobs. Not surprisingly, informal jobs are predominantly selfemployment jobs. However, formal jobs are composed of both wage and self-employment jobs. In other words, all self-employment jobs are not necessarily informal, as 32 percent of formal jobs are actually self-employment.

**Table 3: Formality and occupation types in Kinshasa**

||**%**|
|---|---|
|Formal and wage|23.9|
|Formal and self-employment|14.2|
|Informal and wage|9.1|
|Informal and self-employment|43.4|
|Formal total|40.3|
|Informal total|59.7|
|Wage total|45.8|
|Self-employment total|44.9|

_Source_ : Kinshasa Survey 2018.

_Note:_ Occupation types other than wage and self-employment (such as employer and family worker) are not shown.

The majority of jobs in Kinshasa are service sector jobs. With a small fraction of jobs in agriculture (5.9 percent) and manufacturing (7.8 percent), about 80 percent of jobs in Kinshasa are in the service sector (Table 4).<sup>5</sup> Kinshasa remains largely a local city—as opposed to a global city that is open to regional and global trade, markets, and investments—and it produces fewer internationally tradable goods and services (60 percent) than other world cities (Lall, Henderson, and Venables 2017).

> 4 Unless otherwise noted, this section only looks at jobs of the working-age population.

> 5 For this analysis, the service sector includes commerce; transportation, storage, and communication; financial, insurance, and real estate; public administration; and other services.

10
- 14 -

# **Table 2.** **<u>Number of observations (firms) by sector and country</u>**

||Burundi|Kenya|Rwanda|Tanzania|Uganda|Total|
|---|---|---|---|---|---|---|
|Textile and garment|34|117|6|62|17|236|
|Agro business|43|158|28|133|226|588|
|Chemical, metal and other|19|76|12|47|105|259|
|energy-intensive industries|||||||
|Other manufacturing|31|81|13|40|49|214|
|Service sector (retail, hotel,<br>restaurant, and IT)|174|160|149|78|127|688|
|Other industries (construction,<br>transport,etc.)|8|21|5|13|16|63|
|Total|309|613|213|373|540|2,048|

# **Table** **<u>3. Summary statistics</u>**

|Variable|Abb.|Obs.<br>|Mean<br>|Std. Dev.|Min<br>|Max|
|---|---|---|---|---|---|---|
|Total operating cost|C|2048|1,276,698|8,823,662|<br>97|306,000,000|
|Factor share: Labor|SL|2048|0.381|0.272|<br>0.002|1.000|
|Energy|SE|2048|0.052|0.075|<br>0.000|0.921|
|Fuel|SF|2048|0.012|0.031|<br>0.000|0.439|
|Water|SW|2048|0.004|0.016|<br>0.000|0.349|
|Transport|ST|2048|0.014|0.034|<br>0.000|0.331|
|Communications|SC|2048|0.025|0.054|<br>0.000|0.620|
|Wage (average per full time employee)|WL|2048|1960|4588|<br>11|150107|
|Energy cost|WE|2048|1.573|55.317|<br>0.000|2500.000|
|Fuel cost|WF|2048|0.046|1.237|<br>0.000|55.833|
|Water cost|WW|2048|0.019|0.558|<br>0.000|25.000|
|Transport cost|WT|2048|0.022|0.144|<br>0.000|4.609|
|Communication cost|WC|2048|0.194|1.160|<br>0.000|24.000|
|Other input cost|WM|2048|4.424|40.210|<br>0.000|1013.889|
|Output (total sales)|Y|2048|2,747,458|29,800,000|<br>610|1,200,000,000|
|Number of days without electricity supply|ZE|2048|8.7|7.7|<br>0.0|100.0|
|per year|||||||
|Number of days without water supply per|ZW|2048|1.3|4.2|<br>0.0|30.0|
|year|||||||
|<br>Number of inventory measured by days of|ZT|2048|23.2|38.5|<br>0.0|730.0|
|production|||||||
|Country dummy variable:|||||||
|Kenya||2048|0.299|0.458|<br>0|1|
|Rwanda||2048|0.104|0.305|<br>0|1|
|Tanzania||2048|0.182|0.386|<br>0|1|
|Uganda||2048|0.264|0.441|<br>0|1|
|Sector dummy variable:|||||||
|Textile||2048|0.115|0.319|<br>0|1|
|Agro business (food and wood products)||2048|0.287|0.453|<br>0|1|
|Chemical and metal||2048|0.126|0.332|<br>0|1|
|Other manufacturing||2048|0.104|0.306|<br>0|1|
|Service||2048|0.336|0.472|<br>0|1|
**Table 6b Effect of mining on revenues and spending patterns (continuous production intensity in mining communes)**

||||**Capit**<br>|**al expenditu**<br>|**res**<br>||**Cu**<br>|**rrent expenditu**<br>|**res**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**<br>**Revenue**|**(2)**<br>**Total **|**(3)**<br>**Education **|**(4)**<br>**Health **|**(5)**<br>**Water **|**(6)**<br>**Others**|**(7)**<br>**Total**|**(8)**<br>**Salaries**|**(9)**<br>**Others**|
|Cum. gold produced|116***|327***|190***|13|33|57|124***|19***|71***|
||(12)|(94)|(30)|(11)|(42)|(60)|(9)|(3)|(3)|
|Poverty (%) 1998|-3|65|11|36***|46|-47|-8|1|-2|
||(12)|(93)|(29)|(11)|(41)|(59)|(9)|(3)|(3)|
|Enrollment (%) 1998|-20|125|8|9|42|36|-4|7|-0|
||(18)|(137)|(43)|(16)|(60)|(87)|(14)|(5)|(5)|
|Rainfall (mm) 98-08|1*|-37***|-6***|-2***|-10***|-19***|0|0|-0**|
||(1)|(5)|(1)|(1)|(2)|(3)|(0)|(0)|(0)|
|Urban|1,393**|7,878|-1,159|23|1,743|7,377**|2,207***|1,156***|482***|
||(676)|(5,284)|(1,670)|(625)|(2,334)|(3,374)|(527)|(195)|(176)|
|Observations|692|692|692|692|692|692|692|692|692|
|R-squared|0.129|0.117|0.078|0.040|0.044|0.089|0.227|0.115|0.434|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 28
Policy Research Working Paper 8196

# **Abstract**

This paper develops new estimates of export quality, based on bilateral data, which are far more extensive than previous efforts. The data cover 166 countries and hundreds of products over 1962–2014. The analysis finds that quality upgrading is particularly rapid during the early stages of development. There is significant cross-country heterogeneity in the growth rate of quality. Within any given product line, quality converges over time to the world frontier. Institutional quality, liberal trade policies, foreign direct investment inflows, and human capital all promote quality upgrading, although their impacts vary across sectors. The results suggest that reducing barriers to entry into new sectors can allow economies to benefit from rapid quality convergence over time.

This paper is a product of the Jobs Cross Cutting Solution Area. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jromero1@ 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
Lindley, D.V. (1986), “Another Look at an Axiomatic Approach to Expert Resolution,” _Management Science_ , Volume 32, Issue 3, pp 303-306.* Lindley, D.V. (1985), “Reconciliation of Discrete Probability Distributions,” _Bayesian Statistics_ 2, pp.375-387.* Lindley, D.V. (1983), “Reconciliation of Probability Distributions,” _Operations Research,_ Volume 31, Issue 5, pp.866-660.* Lipscomb, J., Parmigiani, G., and Hasselblad, V. (1998), “Combining Expert Judgment by Hierarchical Modeling: An Application to Physician Staffing,” _Management Science_ , Volume 44, Issue 2, pp 149-161.* Mendel, M. and Sheridan, T. (1989), “Filtering Information from Human Experts,” _IEEE Transaction Systems, Man and Cybernetics_ , Volume 36, pp. 6-16. * Morris, P.A. (1986), “Observations on Expert Aggregation,” _Management Science_ , Volume 32, Issue 3, pp 321-328.* Morris, P.A. (1983), “An Axiomatic Approach to Expert Resolution,” _Management Science_ , Volume 29, Issue 1, pp 24-32.* Morris, P.A. (1977), “Combining Expert Judgments: A Bayesian Approach,” _Management Science_ , Volume 23, Issue 7, pp 679-693.* Morris, P.A. (1974), “Decision Analysis Expert Use,” _Management Science_ , Volume 20, Issue 9, Theory Series, pp 1233-1241.* Mosleh, A., Bier, V.M., and Apostolakis, G. (1988), “A Critique of Current Practice for the Use of Expert Opinions in Probabilistic Risk Assessment,” _Reliability Engineering and System Safety_ , Volume 20, pp. 63-85.* Mosleh, A., Bier, V.M., and Apostolakis, G. (1987), “Methods for the Elicitation and Use of Expert Opinion in Risk Assessment,” NUREG/CR-4962, PLG-0533, US Nuclear Regulatory Commission (micro fiche) Mosleh, A. and Apostolakis, G. (1986), “The Assessment of Probability Distributions from Expert Opinions with an Application to Seismic Fragility Curves,” _Risk Analysis_ , Volume 6, No. 4, pp 447-461.* Myung, I.J., Ramamoorti, S., and Bailey, A.D.Jr. (1996), “Maximum Entropy Aggregation of Expert Predictions,” _Management Science_ , Volume 42, Issue 10, pp 1420-1436.* Parenté, F.J. and Anderson-Parenté, J.K. (1987), “Delphi Inquiry Systems,” _Judgmental Forecasting_ .* 15