rafmacalaba/data-use-mentions-extended huggingface dataset:
Kenya. Detailed earnings regressions for Ghana revealed that apprenticeships do seem to offer a return in specific sectors, such as construction, especially in rural areas (World Bank 2001b). Similarly detailed analysis on Rwanda shows that the strong positive returns to apprenticeships occur in urban areas and are high for males, reinforcing the idea that occupational segregation hurts female earnings (World Bank, 2011a). But all this evidence suggests that much more analysis, including analysis of panel data sets, is required to fully understand the impact of this training approach.

Finally, in all regressions, the dummies included for region and district explained a substantial portion of the overall variance explained - 25-30% in Tanzania and Ghana, where we had over 100 separate location dummies each. This may partially reflect unmeasured spatial price differences. However, qualitative evidence from focus groups on the role of local conditions (local infrastructure, size of market area, behavior of local governments) suggest that local economic and political development is an important variable in HE success (see for example Kweka and Fox, 2011).

Although we have some highly significant coefficients, the r-squares are low in these regressions, suggesting that either earnings, or the explanatory variables, are not well measured. More specialized data sets properly controlling for selectivity and enterprise capital among other things are needed to substantially improve the earnings analysis.

# **5. Household enterprises and household welfare**

Section 3 showed that HEs are increasing importance as a source of employment and section 4 showed that this activity is attracting prime working age people of both genders - ones likely to have a family to support. Multivariate analysis showed there are substantial returns to primary education for HE owners, making it a good occupational choice for this group. But before policy makers and development strategies target this sector for growth, it is important to establish whether this activity does actually pay off as a livelihood source. This section analyses (i) the role of HEs in household livelihood strategies; and (ii) the relationship between
sophistication as capacity develops. Third, O*NET is a globally recognized database widely used by academics, policy makers, and end users. Finally, O*NET questionnaires are publicly available.

# _PIAAC and STEP_

**The SDS also draws on two other skill measurement efforts the Programme for the International Assessment of Adult Competencies (PIAAC) and the Survey Toward Employability and Productivity (STEP).**<sup>6</sup> The OECD carries out PIAAC in more than 40 high-income countries. The World Bank carries out STEP in selected low- and middle-income countries. Both household surveys have a module capturing the frequency of a set of skills used at work to assess the skills the workforce needs to sustain a productive working life. The surveys also assess key information-processing skills (literacy, numeracy, and problem solving).<sup>7</sup> Neither PIAAC nor STEP have been adopted by countries for regular implementation and, although these surveys collect job titles to standardize them into the International Standard Classification of Occupations (ISCO), neither has been used to inform occupational and skills profiling at a disaggregated level. For instance, the STEP database is publicly available at the sub-major group (2-digit ISCO). Therefore,

> 4 For example, the components on Technology Skills and Tools come from big data analyses, and the Occupational Profiles and Labor Market Information data come from data collected by the Bureau of Labor Statistics.

> 5 However, analysts were preferred for practical considerations (i.e., time, costs, and convenience ) (Tsacoumis and Van Iddekinge 2006) .

> 6 Two previous international initiatives aimed at measuring adult skills in 22 OECD countries: the International Adult Literacy Survey, carried out between 1994 and 1998, and the Adult Literacy and Life Skills Survey (ALL), carried out between 2003 and 2008. Based on these surveys, UNESCO began the Literacy Assessment and Monitoring Programme in 2003, which aimed to measure the literacy and numeracy skills of youth and adults in
>32|8<br>26|2<br>12|2<br>2|
||Cost (% of income per capita)|10.3|3.7|0.2|0.0|
||Minimum capital(% of incomeper capita)|116.6|62.8|0.0|0.0|
|**Hiring and firing workers**<br>|Difficulty of hiring index (0-100)|33|33|11|0|
||Rigidity of hours index (0-100)|40|60|0|0|
|<br>|<br>Difficulty of firing index (0-100)|10|30|10|0|
||Rigidity of employment index (0-100)|28|41|7|0|
||Firing costs (weeks)|30|34|0|0|
|**Registering property**|Number of procedures|9|3|2|1|
||Time (days)|19|3|2|1|
||Cost (%ofpropertyvalue)|2.4|0.9|0.2|0.0|
|**Enforcing contracts**|Number of procedures|34|17|19|11|
||Time (days)|440|154|50|27|
||Cost(% of debt)|14.0|14.1|4.8|4.2|
|**Closing a business**|Time of insolvency (years)|3.3|1.2|2.0|0.41|
||Cost (% of estate)|8.0|8.0|4.0|1.0|
|<br>|<br>Recoveryrate (cents onthe US$)|34.2|52.4|71.4|
0.074)|(0.074)|(0.074)|
|Real Exchange Ratet-1(ln)|0.415|0.470|0.468|0.466|
||(0.545)|(0.544)|(0.542)|(0.543)|
|Exchange Rate Volatilityt-1|-2.525|-1.560|-3.752|-3.798|
||(11.240)|(11.242)|(11.206)|(11.208)|
|Inflationt-1|0.009|0.010|0.010|0.010|
||(0.012)|(0.012)|(0.012)|(0.012)|
|Observations|707|707|707|707|
|Number of countries|90|90|90|90|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

The gas and oil reserves variable is standardized, and the gas and oil index is a price index. Bootstrapped standard errors are in parentheses. *** p<0.001, ** p<0.01, * p<0.05 The effect of political violence is positively moderated by oil and gas prices, indicating that the effect of battle-related deaths on FDI flows to the oil and gas sector depends positively upon the global prices of oil and gas. This dependency implies that during commodity booms when prices are high, MNEs are more likely to enter countries marred by political conflict than when prices are low. Figure 5b shows that only in cases of exceptionally high oil and gas prices do the effects of political violence 37
effects on aspirations.<sup>24</sup> However, once one allows for latent heterogeneity in subjective welfare there are other interpretations of the Knight-Gunatilaka finding, as discussed in the next section.

A number of papers have found evidence that unemployment lowers subjective welfare at given income.<sup>25</sup> This is not what the standard economic model of work-leisure choice would suggest, since unemployment at given income implies greater leisure, which is assumed to yield utility. (The welfare cost of unemployment is seen to be through the lost income.) There may well be an independent disutility of unemployment that is missing from the standard model, possibly associated with the quantity constraints on choice that involuntary unemployment entails or possibly stemming from social status roles played by employment. However, the welfare effect attributed to unemployment may also stem from latent heterogeneity in personality traits, which is one of the issues to which the next section turns.

# **5. Problems in using subjective data in measuring poverty**

Subjective data can offer a direct lens on welfare that is not available in standard objective data. But are subjective questions reliable, in the sense that one gets similar answers under similar circumstances? Studies of SWL or ―happiness‖ have found a worryingly high degree of variability. For example, Kristensen and Westergaard-Nielsen (2007) find that 20% of survey respondents in Europe gave a different answer on job satisfaction when asked twice within the same interview. Krueger and Schkade (2008) studied reported SWL for the same people interviewed two weeks apart and found the serial correlation coefficient was only 0.6.

In the present context, it is not the reliability of the subjective welfare scores themselves that is of greatest concern, but the reliability of the regressions for subjective welfare, as used in calibrating social-subjective poverty lines, or measuring welfare more generally. If the likely errors in subjective welfare data are white noise—classical measurement errors—we would not be concerned; the R<sup>2<
fragility has a positive impact on the likelihood that a firm will borrow from multiple banks (Berger, Klapper, and Udell 2001). However, Ongena and Smith (2000) find mixed support in this regard. They find that firms maintain a higher number of banking relationships in countries with inefficient judicial systems and poor enforcement of property rights, consistent with the theory, but also find more banking relationships in nations with more stable banking systems (i.e., with better bank credit ratings), contrary to the predictions.

> 9 A related empirical study finds that banks charge higher rates to customers with limited financing options during economic recessions (Santos and Winton 2005).

> 10 One potential explanation for this result is that banks in unconcentrated sectors are smaller and firms might need many relationships to satisfy their needs.

> 11 A second study using German survey data finds that multiple lending relationships increase collateral requirements (Harhoff and Korting 1998).

8
on average 2-3 months. Individuals received unemployment benefits during the course of their studies. Workers in the training component tended to have higher education levels and to be female. The wage subsidy component of the program was structured so that the subsidy was increasing in the time the worker stayed with the firm. The public works component was targeted at the longer-term unemployed and many of the jobs available were low-skill jobs working on infrastructure improvements. Workers in either the wage subsidy program or the public workers program had an incentive to participate in the wage subsidy program at least 6 months to qualify for another 12 months of unemployment benefits.

The ALMP programs are evaluated using a difference-in-difference matching approach, where the main outcome of interest is labor force status. The samples are drawn from the 18th wave of the Polish Labor Force Survey that included a supplement with four years of historical information on individual labor market histories (monthly from 1992 to 1996). The treated group were individuals who were offered participation in the programs by their local labor office and who accepted the offer. Sample sizes in the three types of programs (training or retraining, intervention works or public works) were 241, 532 and 93. The control group consisted of 7,784 individuals who had been registered at least once as unemployed since January 1992. The matching procedure pairs treated individuals with control individuals who have the exact same labor force history and are matched on certain demographic characteristics (gender, marital status, education, region and age). The matching impact estimates turn out to be sensitive to which variables are used in the matching analysis, for example, to whether the selection of matches also takes into account local labor market conditions.

The impact estimates indicate that the training/retraining program increased the average employment probability for both men and women. Participation in the non-training ALMP programs did not affect women‟s employment probabilities but had a negative effect on men‟s employment probabilities, which the authors attribute to benefit churning rather than stigmatization of intervention and public works participants
and on other types of violence and sensitive topics to better understand what drives misreporting. Other potential areas for additional research include studying how to best design and administer the list experiment to minimize bias, and developing alternative measurement methods for efficient causal research on IPV which, according to face-to-face survey data, is estimated to affect one in three women globally (García-Moreno et al., 2013).

> have made more of a difference. See Appendix B for a further discussion on IPV measurement method trade-offs.

> 33DHS data on IPV cover more than 60 developing countries and are cited in more than 5,000 papers on google scholar. If DHS IPV data are measured with substantial, systematic, and context-specific bias as this paper suggests is likely, this has significant implications for the use of such data.

21
for the subsectors.

**Table 1: Example of Sector Classification**

|**Company Code**|**Business Lines**|**Main Business (imputed)**|
|---|---|---|
|**(illustrative)**|**(ISIC4)**||
|**100**|A0110|A – Agriculture, Crop and Animal|
|**100**|A0111|production.|
|**100**|A0112||
|**101**|B0710|B – Industry, Mining of Metal Ores|
|**102**|A0110|Multiple Sectors|
|**102**|B0710||
|**102**|C1102||

_<u>Note:</u>_ ISIC = Industrial Standard International Classification.

## **Tax records**

Tax declaration data is available at the company level from 2008 to 2012 on a yearly basis. This Paper makes use of declarations for Corporate and Personal income taxes, employees’ withholding taxes, value‐added tax (VAT), and taxes on imports.

Although company level information is not available prior to 2008, a master list of all taxpayers is available, by year of registration, from the tax office.

16 In more technical terms, the main activity is the mode of the registered business lines for each business. “Broad activities” are defined to match Rwanda national accounting practices and are therefore slightly different from ISIC level 1 definitions.

35
|**cv**|**Pove**<br>**Poverty**<br>**rate**|**rty mappin**<br>**Standard**<br>**error**|**g - Cens**<br>**cv**|**us 2014**<br>**Absolute**<br>**difference**|
|---|---|---|---|---|---|---|---|
|**TUNISIA**|**15.2**|**0.227**|**0.015**|**15.1**|**0.356**|**0.024**|**0.1**|
|**Greater Tunis**|5.3|0.365|0.069|6.1|0.050|0.008|0.8|
|**North-East**|11.6|0.600|0.052|11.9|0.223|0.019|0.3|
|**North-West**|28.4|0.762|0.027|25.8|0.353|0.014|2.6|
|**Centre-East**|11.4|0.535|0.047|11.7|0.197|0.017|0.3|
|**Centre-West**|30.8|0.742|0.024|29.3|0.392|0.013|1.5|
|**South-East**|18.5|0.635|0.034|17.
respondents were jobless for around 0.2 additional months compared to rural respondents, but the

> 15 There is similar reduction in the coefficient on tertiary education when we control for pre-COVID-19 employment status, in the smaller sample for which we have this information (results available from the authors). Hence the concentration in wage employment and in particular industries both account for the disproportionately job loss among highly educated workers.

> 16 We use industry of work from the pre-COVID-19 face-to-face survey because the HFPS in Ethiopia did not collect data on industry of work. This means the sample is reduced to respondents who reported, in the HFPS, that they were working pre-COVID-19 and who were working in the last face-to-face survey. Results in column 1 are similar if we limit the analysis to this more restrictive sample.

13
rely upon data from crop-cutting exercises or from purchasing companies.

# **3 Data**

## **3.1 Primary outcomes**

We focus on five outcomes: the amount of cultivated area, fertilizers, pesticides, seeds, and a risk index. This choice is guided by both theoretical and practical considerations. Conceptually, a well-established theory of change outlines that insurance provision could either increase or decrease farmer risk-taking depending on financial constraints and risk preferences.<sup>18</sup> We therefore focus on production decisions involving a certain degree of risk-taking. Practically, because our empirical framework requires outcomes to be measured in similar ways across studies, the set of outcomes is constrained by data availability. It would be interesting to analyze outcomes such as loan subscription, irrigation, and labor expenditures, however, these

> 18See Karlan et al. (2014) for a theoretical model of investment decisions under financial and risk constraints.

10
# Appendix 2: Timeline

April 12, 2012: Pilot program officially launched and firms invited to apply June 25, 2012: Deadline for firms to apply to the program June 11, 2013: Diagnostic phase starts October 30, 2013: Diagnostic phase ends November, 2013: Random assignment to treatment status 2013: World Management Survey administered to subsample of 72 firms with 40+ workers, as well as to random sample of 180 firms representative of Colombian manufacturing sector March-November 2014: Individual Consulting Intervention September 2015-April 2016: Group Consulting Intervention November to December 2015: Round 1firm data collection (individual, group and control treatment) January to February 2016: Round 2 of firm data collection (individual and control treatment) March to April 2016: Round 3 of firm data collection (control treatment) June 2016: Round 4 of firm data collection (group treatment) November 2016: Second round of World Management Survey administered November 2017-July 2018 : Last round of firm data collection from firms Note: firm data collection would collect all months of data available from firm records during in-person firm visits. Timing of when this was extracted from firms varied according to CNP’s contractual agreements, in which they were paid for batches of data collection at a time.

Administrative data on employment are available from the PILA from January 2013 through December 2018.

44
and use data about school reopenings or reconstruction after the Khmer Rouge period to make progress on this strategy. However, this approach would only be valid if the geographical variation in education proves not to be correlated with geographical variations in mortality or other outcomes.

# **VI.  Conclusions and Further Research**

This paper studies the long-term impact of genocide during the Khmer Rouge regime (1975-79) in Cambodia. This study uses the siblings’ mortality data from the Cambodia Demographic and Health Survey in 2000 for quantitative analysis. It shows that excess mortality was extremely high and heavily concentrated during the 1974-1980 period. Adult males were the most likely to die, indicating that violent death played a major role. The paper also establishes that individuals from a family with an urban or an

> 15 A similar approach is developed by Giles, Park and Zhang (2004) for disruptions to education during the Chinese Cultural Revolution. Approaches based solely on birth cohort effects are Meng and Gregory (2002 (b)) for China during the Cultural Revolution and Ichino and Winter-Ebmer (1998) for Austria and Germany after World War II.

12
from different remote sensing products. Again, when weather data is critical to the identification strategy, results should not be sensitive to the choice of remote sensing product. While it is unlikely that the choice between CHIRPS, CPC, ARC2, and TAMSAT will matter, the choice of temperature product could matter in ways that are difficult to predict.

While our analysis is based on agricultural production by LSMS-ISA households in six countries, we believe our conclusions are applicable to and valid in other countries in tropical regions and to other outcomes besides agricultural production. While results are specific to specific contexts, in our case that context is extremely broad. None of our results hinge on a single outcome, a single model, a single year, a single country, or a single region. Our analysis looks at multiple crops using several model specifications. Our data span more than a decade, and come from countries in Eastern, Western, and Southern Africa with different variation in agro-ecolocigal zones and different 46
Pc icy Rv uACti-i WORKIMiŽ PAPER 2075

# Summary findings

Wodon providcs two extensions to Yitzhaki and Lerman's group decomposition of the Gini index.

First, he analyzes stratiflcation (within the group) and inequality (between groups) along several dimensions at once. This makes the determinants of inequality more understandable.

Second, he derives the imnact on the Gin] of marginal changes in income or consumption by group. This can be used to evaluate targeted redistributive policies or to assess the impact of exogenouLs shocks by group.

He applies the analysis to data fromi Bangladesh, with a focus on how inequality affects land ownership, education, and occupation.

Education appears to be a stronger determinant of inequality than occupation, with land ownership ranking third. Marginal targeted transfers and taxes have more effect on redistribution when applied to education (from the well-educated to the illiterate) or occupation groups (fronm officials and managers to tenants and agricultural workers).

This paper-a product of the Poverty Reduction and Economic Management Sector Unit, South Asia Region-was written as part of background work for the Bangladesh poverty assessment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jillian Badami, room MC10-336, telephone 202-4580425, fax 202-522-2428, Internet address jbadami(a.worldbank.org. Policy Research Working Papers are also posted on the Web at http:f/www.worldbank.org/html/dec/PublicationsfWorkpapers/home.html. The author may be contacted at qwodond(k.worldbank.org. March 1999. (33 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas alout deveinpolent issues. A7n ohecede of the series is to get the findings osnt qUickly, even if the presentations are less than full polished. sse papers carry the names of the authors and should he cited accordinigly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. 7They do anot
*|
|Zambia|131|109|21|200|51|21|**_533_**|
|Kenya|104|100|76|153|81|31|**_545_**|
|**_Total_**|**_828_**|**_441_**|**_189_**|**_995_**|**_390_**|**_95_**|**_2938_**|

Source: Author’s own elaboration from enterprise surveys The ICT section provides information on computer and software use and different types of internet use.<sup>12</sup> In the next sub-sections, we provide a detailed overview of computer and internet use for the six African countries of our sample.

# **5.2 Computer use**

_On average, around 2/3 of African firms have at least one employee using a computer regularly_ . Table 3 shows the percentage of firms in these six countries which has at least one person using a computer regularly. On average, 62.9% of African firms in these countries has a regular computer user in 2013, percentage comparable to Australian firms in 1997/98, according to Loundes (2002). However, differences are substantial across countries, since it is only 40.9% in Tanzania yet it reaches to nearly 90% in Kenya. Recent data from the European Union (EU) shows that 97% of firms located in this region has a computer. Therefore, we may infer that Kenya is more similar to European countries than to other parts of Africa.

_This percentage is greater in services in most of the countries, yet differences are small on average. A_ frican countries show similar pattern as EU countries: Services having a higher percentage of firms with computer than manufacturing. Zambia is the only country showing a percentage in manufacturing higher than in services. Across sectors, the manufacturing sector shows more variance than services. While the minimum