rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Sample Split by Visit and Interview Typ**

||**Visit 1**|**Visit 2**|**Total**|
|---|---|---|---|
|**Standard**Interviews (S)|1,428<br>_Sample A_|1,394<br>_Sample B_|2,822|
|**Short**Interviews (L)|1,394<br>_Sample B_|1,428<br>_Sample A_|2,822|
|**Experiment**Sub-Sample<br>Out of Short Interviews(E)|372|393|765|
|**Implied**Records in Analysis (S+E)|1,800|1,787|3,587|

|**Table 2:**|**Module & I**<br>_Stand_<br>|**nterview Durations**<br>_ard Interview_<br>|_Experim_<br>|_ent Interview_<br>|
|---|---|---|---|---|
|_Module_|_Module_<br>_Duration_|_Time Elapsed_<br>_Prior to Module_|_Module_<br>_Duration_|_Time Elapsed_<br>_Prior to Module_|
|Household Roster|9|--|9|--|
|Education, Health & Labor|24|9|--|--|
|Housing|6|34|2|9|
|Food Consumption|16|40|2|11|
|Food Security|2|58|--|--|
|Non-Food Consumption (All 3 Modules)|9|61|2|13|
|Durable Goods|3|71|2|16|
|Farm Implements, Machinery, and|||||
|Structures|3|75|--|--|
|Household Enterprises|2|79|--|--|
|Children Living Elsewhere|1|83|--|--|
|Other Income|2|86|--|--|
|Gifts Given Out|1|88|--|--|
|Social Safety Nets|2|90|--|--|
|Credit|2|93|--|--|
|Subjective Assessment of Well-Being|5|96|2|21|
|Shocks and Coping Strategies|5|101|3|18|
|Child Anthropometry|1|106|--|--|
|Filter for Agriculture & Fishery|||||
|Questionnaires|1|108|--|--|
|**Total Interview Duration **<br>**Note:**Median durations are reported in minutes.|Education,|**109**<br>Health & Labor were s|eparate module|**23**<br>s but were not time-|

**<mark>Note:</mark>** <mark>Median durations are reported in minutes. Education, Health & Labor were separate modules but were not time-</mark> stamped separately - there were time stamps only at the beginning of the Education module and at the end of the Labor module.

18
are smaller than men’s and are concentrated in low productivity sectors (World Bank, 2011). Existing research confirms that women face multiple constraints to business profitability and growth (Buvinic and Furst-Nichols, 2014).

Experiments in development economics have concluded that in comparison to male entrepreneurs, female entrepreneurs tend to invest a lower share of available capital in their businesses, which contributes to gender differences in business size and profits. Recent research on the effects of distributing small grants to female micro-entrepreneurs shows limited positive impact of cash grants on microenterprise profits (De Mel et al., 2008; Fafchamps et al., 2014; Berge et al., 2015). In particular, a study in urban Ghana found that female entrepreneurs with above average initial profits converted in-kind grants into increased profits, but that comparable entrepreneurs given cash were not able to do so. On average, male entrepreneurs generated additional profits from both cash and in-kind grants (Fafchamps et al., 2014).

Economists originally explained this gender differential in investment by citing timeinconsistent preferences—the difficulty that people face in prioritizing future gains over current consumption—and pressures to share cash with others in the household or social network (Fafchamps et al., 2014). But, Bernhardt, Field, Pande, and Rigol (2017) recently reanalyzed <mark>data from the Ghana experiment as well as two others that attempted to relieve capital constraints faced by microentrepreneurs. The original studies had all documented gender gaps in investment behaviors (De Mel et al., 2008; Field et al., 2013; Fafchamps et al., 2014). The reanalysis finds that when</mark> women are the sole enterprise owner within the household, capital shocks do lead to increased profits. However, when their husbands also own enterprises, women’s capital is invested into their husband’s business rather than their own (Bernhardt et al., 2017). Relatedly, in a study in Sri Lanka, De Mel et al. (2008) determine that women’s decision-making power and 7
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Table 7: Eigendecomposition, Eigenvectors for Principal Components (2007, 2011, 2014 and 2020)

|||Principal Components - Eigenvectors||
|---|---|---|---|
||(1)|(2)|(3)|
||Principal Component 1|Principal Component 2|Principal Component 3|
|Panel A: 2007||||
|Social Security Benefts|0.48|0.17|-0.17|
|Minimum Wage Compliance|0.59|-0.08|0.06|
|Child Labor|0.44|0.15|-0.06|
|Relative underpayment|0.49|-0.17|0.13|
|Compensatory moonlighting|0.01|0.03|0.97|
|Labor stability|-0.01|0.79|0.06|
|Incidence of overqualifcation|-0.02|0.53|-0.02|
|Panel B: 2011||||
|Social Security Benefts|0.47|0.17|-0.22|
|Minimum Wage Compliance|0.59|-0.09|0.12|
|Child Labor|0.45|0.17|-0.16|
|Relative underpayment|0.48|-0.18|0.19|
|Compensatory moonlighting|0.01|0.05|0.90|
|Labor stability|-0.01|0.71|0.21|
|Incidence of overqualifcation|-0.02|0.62|-0.13|
|Panel C: 2014||||
|Social Security Benefts|0.48|0.12|-0.17|
|Minimum Wage Compliance|0.58|-0.10|0.06|
|Child Labor|0.47|0.17|0.02|
|Relative underpayment|0.46|-0.16|0.07|
|Compensatory moonlighting|0.00|0.02|0.96|
|Labor stability|-0.01|0.71|0.15|
|Incidence of overqualifcation|-0.01|0.65|-0.14|
|Panel D: 2021||||
|Social Security Benefts|0.49|0.04|-0.22|
|Minimum Wage Compliance|0.60|-0.03|0.07|
|Child Labor|0.40|0.36|0.03|
|Relative underpayment|0.50|-0.19|0.07|
|Compensatory moonlighting|-0.02|0.33|0.71|
|Labor stability|-0.04|-0.39|-0.66|
|Incidence of overqualifcation|-0.04|0.75|0.04|

_Notes:_ Based on data from EH 2007-2021.

18
# **Economic Growth and Crime against Small and Medium Sized Enterprises in Developing Economies**

## **1 Introduction**

A distinction exists between crime experienced by firms and households. The former typically face both a higher risk and greater damage from crime than the latter (Hopkins 2002). Within the developing world context, rising crime experienced by firms can have direct consequences. The already vulnerable business climate in developing economies can further deteriorate. Crime may also retard the growth and performance of small and medium firms. Given that developing economies tend to be plagued by corruption and limited public resources to control crime, a key question is whether the burden of crime, especially for small and medium sized enterprises (SMEs), will be alleviated as developing economies grow. The relationship between crime experienced by firms and economic growth has been under-researched. While some studies analyze specific aspects of crime and corruption in SMEs such as tax payments (BaliamouneLutz and Garello, 2014) or how the quality of institutions shapes the performance of SMEs in developing countries (Aidis et al., 2012; LiPuma et al., 2013), this study fills the gap in the literature with a specific focus on how economic growth directly affects crime, such as robbery, vandalism, and theft, experienced by small and medium enterprises in developing economies.

A fairly substantial theoretical and empirical literature already exists exploring the effect of economic growth or labor market conditions on crime (Fajnzylber et al, 2002; Donohue and Levitt, 2001; Gould et al, 2002; McDowall and Loftin, 2009; Bushway et al., 2012). Typically, most empirical studies on crime in developing economies either use cross-country crime data 2
Policy Research Working Paper 5945

# **Abstract**

The paper investigates the relation between social capital and life satisfaction focusing on the distinction between bonding and bridging. Using the latest version of the combined World and European Values Surveys, the authors first address the question of measurement of social capital by means of a multi-step factor analysis. Through this procedure, they nd that proxies typically used for social capital tend to polarize around two dimensions interpreted as bonding and bridging. These two dimensions are in fact associated with a single latent variable with opposite signs suggesting that they describe two sides of the same latent variable rather than two independent latent variables. The authors call this latent variable the l _ocus of socializing_ and use it to explore the relation between social capital and life satisfaction across world citizens and across groups of similar countries. The results indicate that people with extreme bonding or bridging attitudes are less happy than people with more balanced attitudes. Unlike the literature on social capital and economic growth that finds bridging attitudes more desirable than bonding attitudes, they nd that bonding attitudes are at least as important as bridging attitudes for life satisfaction. This suggests that the social capital dimensions important for economic growth may not necessarily coincide with the social capital dimensions important for life satisfaction.

This paper is a product of the Economic Policy, Poverty and Gender Unit, Middle East and North Africa Region. 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 author may be contacted at pverme@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
Mendelsohn, R. and N. Seo. (2006) “An Integrated Farm Model of Crops and Livestock: Modeling Latin American Agricultural Impacts and Adaptation to Climate Change” Draft.

Ricardo, D. (1817), “On the Principles of Political Economy and Taxation”, John Murray, London.

Schenkler, W., Hanemann, M., and Fisher, A.: 2005. “Will US Agriculture Really Benefit From Global Warming? Accounting for Irrigation in the Hedonic Approach” _American Economic Review_ **95:** 395-406 Seo, Niggol S., R. Mendelsohn, and M. Munasinghe, (2005), “Climate Change and Agriculture in Sri Lanka: A Ricardian Valuation”, _Environment and Development Economics_ 10: 581-596.

Washington, W., et al., “Parallel Climate Model (PCM): Control and Transient Scenarios”. _Climate Dynamics_ 16(2000): 755-774.

World Development Indicators. 2004. World Bank. Washington D.C. http://devdata.worldbank.org World Resources. 2005. World Resources Institute. Washington D.C.

25
|**6 digit HS**<br>**code**|**Description of the product line**|**MFN tariff**<br>**rate**|
|---|---|---|
|640320|Footwear with outer soles of leather, and uppers which consist of leather straps|34|
|20733|Of ducks, geese or guinea fowls :-- Not cut in pieces, frozen|34|
|620343|Trousers, bib and brace overalls, breeches and shorts :-- Of synthetic fibres|33|
|570490|Other|33|
|610729|Nightshirts and pajamas :-- Of other textile materials|33|
|611592|Other :-- Of cotton|33|
|420222|Handbags, whether or not with shoulder strap, including those without handle :--|33|
|630239|Other bed linen :-- Of other textile materials|33|
|20130|Boneless|33|
|481420|Wallpaper and similar wall coverings, consisting of paper coated or covered, on|33|
|610322|Suits :-- Of cotton|33|
|630259|Other table linen :-- Of other textile materials|33|
|620193|Other :-- Of man-made fibres|33|
|160250|Of bovine animals|33|
|392610|Office or school supplies|32|
|910599|Other|32|
|20726|Of turkeys :-- Cuts and offal, fresh or chilled|32|
|620930|Of synthetic fibres|32|
|20230|Boneless|32|
|611710|Shawls, scarves, mufflers, mantillas, veils and the like|32|
|630311|Knitted or crocheted :-- Of cotton|32|
|620292|Other :-- Of cotton|32|
|620891|Other :-- Of cotton|31|
|620640|Of man-made fibres|31|
|620212|Overcoats, raincoats, car-coats, capes, cloaks and similar articles :-- Of cotto|31|
|640391|Other footwear :-- Covering the ankle|31|
|610120|Of cotton|31|
|610190|Of other textile materials|31|
|150710|Crude oil, whether or not degummed|31|
|20220|Other cuts with bone in|31|
|420330|Belts and bandoliers|31|
|650692|Other :-- Of furskin|31|
|620342|Trousers, bib and brace overalls, breeches and shorts :-- Of cotton|31|
|420232|Articles of a kind normally carried in the pocket or in the handbag :-- With out|30|
|611512|Panty hose and tights :- Panty hose and tights :-- Of synthetic fibres, measurin<br>|30|
|620432|Jackets and blazers :-- Of cotton|30|
|610821|Briefs and panties :-- Of cotton|30|
|20120|<br>Other cuts with bone in|30|
|630260|Toilet linen and kitchen linen, of terry towelling or similar terry fabrics, of|30|
|220860|<br>Vodka|30|
|20210|Carcasses and half-carcasses|30|
|240210|Cigars, cheroots and cigarillos, containing tobacco|30|
|240220|Cigarettes containing tobacco|30|
|240290|Other|30|

41
2. **General Management Practices** : The proportion of 40 different management practices being used by the firm, coded as zero for firms that close. These are sub-divided into five areas and component indices:

- (a) **Commercial Practices** : The proportion of the following 11 practices implemented in the past 12 months: a marketing and sales plan aligned with business strategy; a market segmentation approach with customers; market research identifying current and potential customers in the domestic market; market research identifying potential retailers and wholesalers in the domestic market; use a customer relationship management (CRM) system to record customer information; had meetings with customers to get feedback on products and services; promoted products and services by advertising and brand improvements; clearly defined the role of sales staff in hiring process; provided sales training courses for sales staff; created incentive schemes for sales staff; measured key performance indicators (KPIs) for commercial practices. Figure C2 shows treatment impacts on each individual component.

- (b) **Operational Productivity Practices** : The proportion of the following 10 practices implemented in the past 12 months: communicated strategic goals to the production plant leader and team; evaluated the plant lead and team according to strategic goals; identified and managed bottlenecks in the company’s capacity; identified and described production processes using Value Stream Mapping (VSM); developed the 5S methodology in the production plant; developed continuous improvement methods (Kaizen, Takt time) in the production process; standardized the work sequence by breaking down every component of the production process; implemented preventive and corrective measures; measured key performance indicators (KPIs) on plant performance; developed improvement programs for KPIs. Figure C2 shows treatment impacts on each individual component.

- (c) **Labor Productivity Practice** s: The proportion of the following 9 practices implemented in the last 12 months: defined a talent management plan aligned with the strategic plan; developed communication practices among the different processes of the company; promoted a culture of measuring work environment; improved workspaces for employees (ergonomics, illumination); developed programs leading to changes in organizational climate, such as communication workshops; administered satisfaction surveys with employees; developed workshops and trainings regarding absenteeism; created a rewards system or recognition program based on employee performance; measured KPIs for labor productivity.

- (d) **Quality Practices** : The proportion of the following 6 practices implemented

52
<!-- Start of picture text --> Raven scores<br>By age and by wave<br>Baseline Endline<br>= ce<br>x=<br>a=<br>N<br>52 57<br>xFA “eeBO<br>Be £<br>2 Qa<br>§o 8<br>o<br>t+<br>+<br>o| i of<br>5 6 7 8 9 10 11 12 13 14 15 5 6 7 8 9 10 11 12 13 14 15<br>excludes outside values excludes outside values<br>1 point for correct response, 0 for wrong. 3 sections of 6 questions.<br>Section stops if third successive wrong response.<br><!-- End of picture text -->

<!-- Start of picture text --> raven test scores<br>by age, treatment, wave and school enrollment<br>Baseline Endline<br>©<br>o<br>B om<br>2 2<br>cw Cc<br>oO _ a oO o<br>Ler é Le<br>£ ao £ poo<br>Qs a a) gw a0 a<br>fo) a fo) ae eco<br>3 ao 3 a<br>3 A i ot oe<br>cs oe ie IZ a<br>g© “Xe g Ut<br><x LAE In / r<br>4 /<br>a7 7<br>aAwav<br>56 7 8 9 10 11 12 13 14 15 5 6 7 8 9 10 11 12 13 14 15<br>Age Age<br>——— Treated, schooled ——~—~—- Treated, not schooled<br>— Control, schooled -—---- Control, not schooled<br>1 point for correct response, O for wrong.<br><!-- End of picture text -->
of ninis over this period; in fact, the share of male ninis increased marginally during those years (for both age groups). And over the same period, the increase of 14 percent in the total number of ninis (1.8 million individuals) is entirely explained by the increase in the number of male ninis. Simultaneously, the number of female ninis has fallen in the case of those aged 15 to 18 (decreasing by 5.6 percentage points, or over 400,000 individuals) and stayed relatively constant for those aged 19 to 24. These trends, which are likely explained by the general increase in women’s education levels and labor-force participation rates in the region, combine to produce a shift in the gender composition of ninis towards men.

**Figure 10: Total number and percentage of youth who are ninis in LAC by gender and age, 1992-2010**

Panel A: Percentage of youth who are ninis in LAC by gender, (15-18 years) Panel B: Number of ninis in LAC by gender, (15-18 years) <!-- Start of picture text --> 25.4 24.9 5.8 6.0 5.9 6.0 5.9<br>23.3<br>20.5<br>19.8<br>18.4 18.3 17.6 4.1 4.1 4.0 3.8<br>3.7<br>15.6 15.5<br>11.1 11.7 12.0 11.0 11.5 1.9 1.9 2.1 2.2<br>1.7<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Panel C: Percentage of youth who are ninis in LAC by Panel D: Number of ninis in LAC by<br>gender, (19-24 years) gender, (19-24 years)<br>41.1 41.2 12.1 12.3 12.3<br>37.7 11.1<br>35.6 10.6<br>33.5<br>8.5 8.8 9.1 9.0 8.8<br>27.3 27.0 26.5<br>24.3 23.4<br>12.6 12.2 14.7 12.9 13.1 3.0 3.2 3.5<br>2.2 2.3<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>25.0 6.0<br>5.0<br>20.0<br>4.0<br>15.0 Ninis (Millions) 3.0<br>Ninis (% relevant population) 2.0<br>10.0<br>40.0 12.0<br>10.0<br>30.0 8.0<br>6.0<br>20.0 Ninis (Millions)<br>4.0<br>Ninis (% relevant population)<br>10.0 2.0<br><!-- End of picture text --> Panel D: Number of ninis in LAC by gender, (19-24 years) Source: authors’ own computation with data from CEDLAS and The World Bank (2015) 17
been studying the indirect links. For example, Claessens, Demirguc-Kunt, and Huizinga (2001) and Bayraktar and Wang (2004 and 2005) show that foreign banks play a statistically and economically significant role in improving the efficiency and competitiveness of domestic banks by reducing costs, profits, and net interest margins. Thus, foreign banks are expected to increase economic growth by improving the functioning of domestic banks, as well as the development level of financial markets. Recent studies have shown strong linkage from the opening of services trade (including banking sector) to the productivity of the manufacturing sector, and thus the growth impact of opening trade in services (Arnold, Javorcik and Mattoo 2006, Eschenbach and Hoekman, 2005; Eschenbach and Francois 2005).

The aim of this paper is to empirically reinvestigate the direct and indirect links between foreign bank entry and economic growth, using a more advanced econometric technique, which is named generalized method-of-moments (GMM) dynamic panel estimators. This econometric technique has been recently used in the growth literature as an alternative to cross-sectional estimators.<sup>5</sup> More specifically, we use a system estimator introduced by Arellano and Bover (1995), which combines a specification in first differences with the one in levels.<sup>6</sup> The advantage of this GMM methodology is that it takes care of the econometric problems caused by unobserved country-specific effects and endogeneity of the independent variables in lagged-dependent-variable models such as economic growth regressions. The inclusion of both cross-country and time-series data introduces additional information about the over-time change in growth and its determinants, and, thus, helps us get more precise results.

> 5 See Levine, Loayza, and Beck (2000), Easterly and Levine (1997), Chang, Kaltani, and Loayza (2005). 6 Blundell and Bond (1997) show that this methodology improves both efficiency and consistency in Monte Carlo simulations.

4
# **Appendix S2: Differences between poverty and inequality measures**

The rationale for using bootstrap to evaluate the significance of differences between poverty and inequality indices is provided by Biewen (2002). Asymptotic normality of the interdecile, interquartile, mean logarithmic deviation, and Theil indices can be established using the delta-method (Green, 2000). Kakwani (1993) provides the same results for the headcount poverty and the poverty gap indices. For the Gini index, see Xu (2007). In this Appendix, we reproduce in Tables S2.1 and S2.2 the estimated differences between inequality indices, based on per capita consumption, and between poverty indices, based on per adult equivalent consumption. Similar results for inequality indices based on per adult equivalent consumption are available upon request but not shown.

**Table** **<u>S2.1: Differences between inequality indices from Table 3</u>**

||Gini|90/10|75/25|Mean log<br>dev|Theil-T|
|---|---|---|---|---|---|
|All consumption|2.56***|1.02***|0.10|0.05***|0.07***|
||(0.21)|(0.23)|(0.07)|(0.00)|(0.01)|
|Non-food consumption|4.44***|6.91***|0.68***|0.15***|0.18***|
||(0.33)|(1.00)|(0.21)|(0.01)|(0.02)|
|Food consumption|1.22***|0.55***|0.10**|0.02***|0.02***|
||(0.13)|(0.13)|(0.04)|(0.00)|(0.00)|

Sources: « Pauvreté et Structure Familiale » (PSF), authors' calculations. Note : Test of differences between inequality indices computed using per capital household consumption and per capita cell consumption, presented in Table 3. N=1762, Bootstrap standard errors (250 replications). *,**,***: significant at the 10%, %, 1% level.

**Table S2.2: Differences between poverty indices from table 6**

||Nutrition p<br>Headcount<br>index|overty line<br>Poverty gap<br>index|Basic needs<br>Headcount<br>index|poverty line<br>Poverty gap<br>index|
|---|---|---|---|---|
|National|1.56***|0.88***|3.51***|1.78***|
||(0.45)|(0.12)|(0.64)|(0.15)|
|Dakar|0.29|0.16***|3.56***|1.63***|
||(0.39)|(0.06)|(1.14)|(0.22)|
|Other urban areas|2.07**|0.95***|3.43***|1.62***|
||(1.01)|(0.31)|(1.26)|(0.30)|
|Rural areas|2.19***|1.34***|3.51***|1.95***|
||(0.81)|(0.20)|(0.99)|(0.24)|

Source: « Pauvreté et Structure Familiale » (PSF) survey, authors' calculations. N=1762 households and 4293 cells. Note : Test of differences between poverty indices computed using per capital household consumption and per capita cell consumption, presented in Table 6. Bootstrap standard errors (250 replications) between parentheses. *,**,***: significant at the 10%, 5%, 1% level.

16