rafmacalaba/datause-encoder-data huggingface dataset:
22 The respondents are then divided by the scale of company which is determined by the number of workers. Small scale company 5-19 workers, medium-scale 20-100 workers and large-scale company 100 or more workers. Based on scale the respondents are spread according to the percentage of companies in each output: 85 per cent in large-scale companies, 10 per cent in medium and 5 per cent in the small-scale companies. For the purpose of better analysis, small and medium scale companies have been grouped together. This was particularly necessary given the small sample size of the small scale enterprises.

The spread of respondents according to the type of industry and the scale, can be seen on Table 3. There are 50 respondents in large-sized textile companies, 5 in the medium, and 5 in the small-scale companies. In the garment industry, 130 in large, 15 in medium and 10 in small. In the footwear industry, 70 in the large-sized companies, 10 and 5 in the medium and small-sized respectively. The locations of companies are obtained from Central Bureau Statistics (BPS). A maximum of 5 respondents were interviewed from each company. The respondents usually live around their place of work. Once a respondent is found from a company and has been interviewed, she is asked to give a name and address of a friend from the same company who may in turn be the next respondent. This was the technique used in gathering 300 respondents.

This method has flaws since in practice there are respondents that need to be interviewed even though they are not from the specific randomly-chosen companies of each scale. As a result the respondents are not gathered by random sampling but more by "quota". Secondly, there might be some bias since friends are likely to work in the same section. Thirdly, by dividing the number of respondents according to the scale of the company, the number, especially for the small-scale industry is too small to be critically analyzed. However, allocating more respondents for the small-scale industry would not solve the problem, especially in the textile and footwear industries. This is because there are not many small-scale textile companies and there are hardly any women workers in the small-scale footwear companies (see Table 3).

Out of the 300 respondents, 84.6 percent are permanent and 15.4 percent have some other status. The analysis below is based on the 254 permanent workers interviewed for the survey. It is assumed that differences in wage rates and benefits are due to whether the worker is permanent or not, and the distinction in the 'terms of employment' from based on their status, can influence their awareness level of the work regulations. Therefore, narrowly focusing on the permanent workers (daily or monthly workers) eliminates 'noise' of the data. Furthermore, the identification of factors relevant to the variables can easily be identified.
Table 1: VARMA(AICc) results at PM2 _._ 5 _>_ 6, _R_<sup>ˆ2</sup> = 0 _._ 705, 42 estimated parameters on ( _N −_ max( _p, q_ ) _× T_ ) _×_ 3 = 4068 data points with 372 fixed demeaning components. _AICc_ = _−_ 6895 _._ 750.

|Pollution<br>(pol)|Bottom Expenditures<br>Expenditures<br>(bot)<br>(exp)|
|---|---|
|_φ polt−_1<br>-0.456***<br>|-0.155***<br>0.124*<br><br>|
|(-6.407)|(-2.745)<br>(1.862)|
|_φ polt−_2<br>-0.201***<br>|-0.101***<br>|
|(-6.171)|(-3.215)<br>|
|_φ polt−_3|0.078***<br>|
||(3.081)|
|_φ bott−_1<br>0.101**|-0.119**|
|(2.019)|(-2.02)|
|_φ bott−_2|-0.138***<br>-0.123***<br><br>|
||(-4.645)<br>(-2.655)|
|_φ bott−_3<br>-0.056**|-0.094***<br>-0.156***|
|(-2.362)|(-3.333)<br>(-3.825)<br><br>|
|_φ expt−_1|0.069***<br>-0.277***|
||(2.884)<br>(-4.884)<br>|
|_φ expt−_2|0.159***<br>(4.557)|
|_φ expt−_3|0.072***<br>|
||(3.099)|
|_M polt−_1<br>-0.142*<br>|0.145**<br>-0.196***<br><br>|
|(-1.853)|(2.444)<br>(-2.7)|
|_M polt−_2<br>-0.100**|0.129***|
|(-2.236)|(2.788)|
|_M polt−_3<br>0.068*<br>|-0.085***<br>-0.088**<br><br>|
|(1.806)|(-2.877)<br>(-2.225)|
|_M bott−_1<br>-0.148***|-0.235***<br>0.095**|
|(-2.585)|(-3.855)<br>(2.538)|
|_M bott−_2|0.221***<br>(3.692)|
|_M bott−_3|0.141**<br>|
||(2.354)|
|_M expt−_1|-0.119*|
||(-1.827)|
|_M expt−_2|-0.135***<br>-0.356***<br><br>|
||(-3.658)<br>(-8.198)|
|_M expt−_3|-0.137***<br>(-3.75)|
|_σ_<br>0.109|0.087<br>0.100|
|_ν_<br>3.797|5.031<br>5.721|
|4-lag white-noise _p_<br>1.000|0.085*<br>0.025**|
|_Note:_|_∗_p_<_0.1; _∗∗_p_<_0.05; _∗∗∗_p_<_0.01|
|_Constant omitted, t-statistics in _|_parenthesis for the ARMA components._|

xv
local, non-tradable services and perhaps source some local production inputs.

For countries at low levels of economic development, agglomeration externalities arise in part because spatial concentration of demand makes job specialization possible (Fafchamps and Shilpi 2005, Fafchamps 2012). Many goods and services that are provided outside of the market realm in rural areas fall within the market domain in cities (Fafchamps 2011). As a result, many individuals are able to avail non-farm jobs as primary occupation, a fact that is reflected in occupational data. Because specialization allows the emergence of new and better goods and services, towns end up serving their surrounding rural hinterland in non-agricultural products and services – including trade and transformation of agricultural products.

Specialization, market provision, and concentration of demand also mean that firm size can increase to capture economies of scale. It follows that the share of people working for a wage (or running a firm with employees) is larger in towns and cities than in the surrounding countryside, where most people are self-employed or family workers, typically on a farm. Based on this, we expect urban centers to be characterized by a larger share of formal employment and a large proportion of employees or employers and a lower proportion of self-employed and family workers. There should also be more public sector employment because schools, health facilities, and government bureaucracies are often located in small urban centers from which they serve the surrounding countryside.

Since towns and cities have more people in specialized employment (either for a wage or on own account) as opposed to the range of self-provision activities that farmers undertake, we also expect them to have more people looking for employment, that is, a higher proportion of unemployed. In contrast, in a country like Ghana, people living on a farm are often underemployed, at least seasonally, but they rarely report themselves as formally unemployed. For similar reasons, in our dataset, women in rural Ghana often listed farming as their primary occupation while married women in urban areas are more likely to describe themselves as homemaker.<sup>2</sup> We expect the number and size of cities to grow with economic development. The intuition is that higher levels of income shift demand away from basic necessities such as food and shelter, towards more specialized goods and services best provided in an urban environment. In Africa, urbanization seems to also have been fueled by population growth, with much urbanization

> 2For similar evidence from South Asia, see Fafchamps and Shilpi (2005).

6
Numberof earners per household, 2004-2019 20 n = 40 at a 30 rsmon2 2010 * (@) 2004 2007 2009 2012 2014 2016 HMOne Wtwo WThreeormore People" Workingb sole Multiple17 P os, €un 435 Y 3 Tb£25 a 2 =~@ Ls eco TTT “oO ON Db > nk 0 0 A PrPD> SY PrP’SY SY? PPP’EY? OS
The HBS and NPS data were collected over a period of approximately 12 months, which excludes seasonality as a potential source of the mismatch in poverty and inequality indicators. While the survey years are relatively close, the advent of the financial crisis in 2008 might have induced substantial changes in household consumption patterns that may have been captured by NPS 2008/09 and NPS 2010/11, which could explain the increase of poverty between the two rounds.

**Table 2: General survey characteristics**

||**HB**|**S**||**NPS**||
|---|---|---|---|---|---|
||**2007**|**2011/12**|**2008/09**|**2010/11**|**2012/13**|
|**Survey design**<br>Field work period||||||
|Start date|2007, month 1|2011, month 10|2008, month 9|2010, month 10|2012, month 10|
|End date|2007, month 12|2012, month 10|2009, month 10|2011, month 11|2013, month 11|
|**Sample size: number of**<br>|<br>**interviewed househo**<br>|<br>**lds**<br>|<br>|<br>|<br>|
|Total|10,464|10,186|3,265|3,846|4,901<sup>*</sup>|
|Rural|3,273|4,130|1,751|2,053|2,712|
|Urban|3,735|3,040|480|634|854|
|Dar es Salaam|3,456|3,016|555|626|746|
|Zanzibar|0|0|479|533|589|
|Sample attrition<br>(anel)|n.a.|n.a.|n.a.|3%|4%|
|p<br>**Estimated population si**<br>Eid li|**ze**|||||
|stmate popuaton<br>(Mainland)|38,289,599|42,270,137|35,372,530|43,340,580|43,530,654|
|Estimated population<br>(including Zanzibar)|||36,443,894|44,651,722|44,811,420|

*The increase in the sample is due to tracking and interviewing household members from split households. About 96 percent of NPS 2010/11 households were successfully found and interviewed in 2012/13.

# **B. Consumption data collection methods**

Table 3 compares the methods of consumption data collection between HBS and NPS. The two surveys diverge in several aspects, which induce fundamental differences in survey design, explained below, that are difficult to adjust _ex-post_ and will remain present when comparing the two surveys’ results.

11
Policy Research Working Paper 10683

# **Abstract**

This paper quantifies the impact of drought on household consumption for five main agroecological zones in Africa, developing vulnerability (or damage) functions of the relationship between rainfall deficits and poverty. Damage functions are a key element in models that quantify the risk of extreme weather and the impacts of climate change. Although these functions are commonly estimated for storm or flood damages to buildings, they are less often available for income losses from droughts. The paper takes a regional approach to the analysis, developing standardized hazard definitions and methods for matching hazard and household data, allowing survey data from close to 100,000 households to be used in the analysis. The damage functions are used to quantify the impact of historical weather conditions on poverty for eight countries, highlighting the risk to poverty outcomes that weather variability causes. National poverty rates are 1–12 percent higher, depending on the country, under the worst weather conditions relative to the best conditions observed in the past 13 years. This amounts to an increase in the total poverty gap that ranges from US$4 million to US$2.4 billion (2011 purchasing power parity).

This paper is a product of the Poverty and Equity Global Practice. 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 rhill@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
Prob(Entry) = f(Share of private subsidies , Total Government consumptionijt jt jt , (1) Investment jt , GDP/Capita jt , GDP growth rate jt , Individual level controlsijt ) Where i denotes individuals, j denotes country, and t denotes time. Entry is a dummy equal to 1 if the individual is engaged in nascent entrepreneurial activity. We also include our second definition of entrepreneurship with the additional requirement of having the intention to create 10 or more jobs.

# 3. Results

Column 1 of table 1 presents the results for nascent entrepreneurship and the share of total private subsidies. In both cases we have a negative correlation between the share of government spending in private subsidies and entrepreneurial activity with at least a 5% level of statistical significance. A natural extension would be to see if the components of private subsidies have a significant correlation with entrepreneurial activity. Thus for columns 2 through 5, we present the results for Defense spending, Agriculture, forestry & fishing, Fuel & Energy, Manufacture & mining subsidies respectively. This is the finest level of spending categorization available in the GFS database. All coefficients are negative, with only defense, and Fuel & Energy being statistically significant at the 1 % level. The coefficients for the controls are also consistent with the literature with education and network variables having a positive correlation with entrepreneurial activity.

Table 2 follows the same pattern as table 1 with the exception of the dependent variable which is nascent entrepreneurship with the expectation of creating 10 or more jobs. The rationale for this measure is to weed out individuals who wish to engage in entrepreneurship mainly as a means of self-employment due to the lack of other alternatives. The basic results for aggregate private subsidies and entrepreneurial activity stand; there is a negative relationship that is statistically significant at least at the 10% level.

5
|Gasoline|5.6|5.6|11.4|11.3|17.3|17.2|23.4|23.3|29.6|29.5|
|---|---|---|---|---|---|---|---|---|---|---|
|Biodiesel<br>Bioethanol|10.0<br>10.0|16.0<br>10.2|26.5<br>26.4|35.1<br>26.7|55.9<br>55.8|70.2<br>56.2|116.3<br>116.0|145.7<br>116.6|277.8<br>276.7|367.4<br>277.9|
|**Table B7: Compensated subsi**|**dy to sales**|**of biofue**|**ls(20%)**||||||||
|||**P1**||**P2**||**P3**||**P4**||**P5**|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|4.0|8.1|8.1|12.3|12.3|16.7|16.7|20.9|20.9|
|Trade Balance|5.1|5.1|10.4|10.4|15.9|15.9|21.4|21.4|26.9|26.9|
|Fiscal Result|2.1|2.1|4.3|4.3|6.5|6.5|8.6|8.6|10.7|10.6|
|Rate of Unemployment|9.2|9.2|6.9|6.9|4.6|4.6|2.2|2.2|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|3.9|8.0|8.0|12.1|12.1|16.4|16.4|20.5|20.5|
|Richest Household|3.6|3.6|7.2|7.2|10.9|10.9|14.7|14.7|18.4|18.4|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|4.2|8.4|8.4|12.8|12.8|17.3|17.3|21.8|21.8|
|Manufactures|4.4|4.4|8.8|8.8|13.4|13.4|18.1|18.1|22.7|22.7|
|Services|3.9|3.9|7.9|7.9|12.1|12.1|16.3|16.3|20.4|20.3|
|Soy|4.1|4.1|8.3|8.3|12.6|12.6|17.0|17.0|21.5|21.4|
|Maize|4.1|4.1|8.4|8.4|12.7|12.7|17.2|17.2|21.7|21.7|
|Sugar Cane|4.4|4.4|8.9|8.9|13.5|13.5|18.3|18.3|22.9|23.0|
|Rest of agriculture|4.2|4.2|8.5|8.5|12.9|12.9|17.5|17.4|22.1|22.0|
|Soybean oil|4.9|4.9|9.9|9.9|15.0|15.0|20.2|20.2|25.4|25.3|
|Diesel|5.5|5.5|11.1|11.1|16.8|16.8|22.7|22.7|28.7|28.6|
|Gasoline|5.6|5.6|11.4|11.4|17.3|17.3|23.4|23.4|29.6|29.5|
|Biodiesel|10.0|32.5|26.5|60.4|55.9|115.7|116.3|253.4|277.8|820.1|
|Bioethanol|10.0|32.7|26.4|60.5|55.8|115.8|116.0|253.1|276.7|815.9|

36
Within country, employers generally demand the same skill sets for managers and workers, though higher-order cognitive skills are more stressed for managers. In Lebanon, communications and teamwork are the two top-rated skills by employers of managers and workers, though communications is ranked first for Lebanese managers and teamwork for workers. Similarly, within the top-ranked nontechnical skills in the Philippines, problem-solving and leadership emerge for managers while independent work and teamwork are identified for workers.

There is not a global pattern of the greatest skills gaps for managers. Employers in the UK, Russia, and Philippines, report that socio-emotional and higher-order cognitive skills are the largest skills gap among managers, specifying the shortage of strategic management, problem solving, planning, time management, initiative, leadership and decision-making (UKCES 2012, Vasiliev et al. 2013, diGropello 2010). Indonesian and Lebanese employers feel that their managers need greater domination of the English language (higher-order cognitive skill) while Botswanan employers name the ability to use technology in the workplace as the most lacking skill (diGropello 2011, World Bank 2012, Vasiliev 2013).

Worker skill gaps are observed in technical, higher-order cognitive, and socio-emotional skills. Employers in the UK, Indonesia, and Botswana<sup>51</sup> report that job-specific skills are most lacking among their workers (UKCES 2012, diGropello 2011, Vasiliev et al. 2013). While in Russia and Lebanon, the most lacking skills are higher-order cognitive and socio-emotional: problem-solving, independent work, and conscientiousness (Vasiliev et al. 2013, World Bank 2012). Russian employers identify as second the gap in professional skills. Higher-order cognitive and socio-emotional skills gaps also emerge as very important for UK workers, including planning, organization, problem-solving, and strategic management skills (UKCSE 2012). Basic cognitive skills gaps (numeracy) are particularly noted by employers in Lebanon (World Bank 2012).

Although employers identified the same preferred skill sets for their workers and managers, the skills gaps differ. Greater technical skills gaps were identified among workers. While socio-emotional skills gaps are stressed across the employers’ workforces, leadership and management gaps are most named for managers while initiative and independent work are more desired in workers. Among higherorder cognitive skills gaps, problem-solving gaps consistently emerge for workers (US, Philippines) with fewer employers identifying severe higher-order cognitive skills gaps among their managers.

# **4.8  Less v. more educated  workers**

> 51 Elementary occupations, plant/machine operators, craft/trade, skilled agriculture, services and sales, clerical support, technicians, professionals, managers.

24
Overall, these results suggest that a considerable fraction of distress events can be correctly predicted using a very small set of just three explanatory variables: debt burdens, policy, and shocks. Moreover, the predictive power of the combined specification is considerably better than any univariate prediction, suggesting that it is important to take all three factors into account when assessing the likelihood of debt distress. Before we turn to the policy and operational implications of this finding in the last section of this paper, we first subject this basic specification to a number of robustness checks.

# **4.2  Robustness of Core Specification:  Alternative Measures of Debt, Policy and Shocks**

First we examine how robust our results are to alternative measures of debt burden, policies, and shocks. Table 5 reports results using three alternative debt burden indicators: the nominal value of debt relative to exports, debt service relative to current government revenues, and debt service relative to non-gold reserves. The first three columns correspond to the sample covering the 1980s and 1990s, and the second three columns correspond to the larger sample including the 1970s. In all cases we find that all three alternative debt indicators are significant, although again we see that the flow measures of debt service are much more significant than the stock measure of the face value of debt. Interestingly, however, comparing the first column of Table 5 with the fifth column of Table 4, we see that the present value measure of debt stocks is a better predictor of debt distress than simply the face value of debt. In all specifications in Table 5, we continue to find that policies and institutions as proxied by the CPIA are a highly significant determinant of debt distress, and in most cases growth also remains significant, although its significance is weaker than in our core specifications in the previous table.

In Table 6 we consider alternative measures of policies and shocks. In the first two columns we use the time-invariant measure of institutional quality from Kaufmann, 18
<!-- Start of picture text --> Namibia Pe<br>Botswana po<br>Swaziland po<br>South Africa Pe<br>Tanzania po<br>Zambia po<br>Malawi Pe<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> <!-- Start of picture text --> Botswana Po<br>Swaziland Po<br>Namibia |<br>Zambia | t—“‘;‘CSCSCs*C<br>South Africa FP<br>Malawi po |__|<br>Tanzania P|<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text -->
# **Text S1: Methods for the missing data models**

We performed a missing data analysis for the primary outcomes of age-specific anemia and sustained attention and for the secondary outcome of spelling, for which we identified a negative effect of the IST intervention in Class 1 children. In order to gain power and account for missing data, we used a likelihood-based approach and fitted random effects models to the one-year and two-year follow-up data simultaneously. We included the following variables in all models: age, sex, literacy group, school-mean exam score and a baseline measure, where possible. The logit link was used for binary outcomes to obtain odds ratios of the intervention effect. As a consequence, the intervention effects from these models are not directly comparable to the population-averaged risk ratios obtained from the GEE model. As a consequence, we also present results from the unadjusted models for comparison to the models additionally adjusted for predictors of missingness. Time was modeled as a categorical variable in all models so that we did not assume a specific linear effect of time. Specifically, we allowed the IST effect to differ at the two time-points by including an interaction between IST and time. We additionally adjusted for variables seen to predict missingness. See Tables S1 and S2 for predictors of missingness for health outcomes at follow up 1 and follow-up 2 respectively, in both the control and intervention groups. Tables S3and S4 show the equivalent data for educational outcomes at follow up 1 and follow-up 2 respectively. Specifically we adjusted for SES, parental education and baseline educational level as measured by baseline spelling score (standardized by subtracting year-group baseline mean and scaled by year-group SD) in both the health and educational outcome analyses. By accounting for predictors of missingness in the models, we can obtain valid estimates of the intervention effect in the presence of missing follow-up data. Furthermore, we gain power by simultaneously modeling the follow up 1 and follow-up 2 data. For results of missingness analyses for health and education outcomes please see Tables S5 and S6.

31