rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A2. Characteristics of pioneering versus non-pioneering firms in countries which are FCS and non-FCS** **_(Continued)_**

||Fragile and conf|lict affected states|Other|countries|
|---|---|---|---|---|
||Pioneering<br>firms|Non‐pioneering<br>firms|Pioneering<br>firms|Non‐pioneering<br>firms|
|**Other constraints**|||||
|Firms identifying political instability as a major constraints (%)|55.08|48.66|26.22|23.88|
|Firms identifying political instability as biggest obstacle (%)|21.02|19.43|7.87|10.03|
|Firms identifying corruption as biggest obstacle (%)|9.47|6.68|9.21|8.04|
|**Other firm characteristics**|||||
|Age (average)|15.53|14.88|20.05|17.74|
|Firms of small size (<20) (%)|55.97|67.68|37.20|51.74|
|Firms of medium size (20‐99) (%)|31.96|25.03|37.15|33.10|
|Firms of large size (100 and over) (%)|12.06|7.29|25.65|15.16|
|Firms with a female top manager (%)|10.91|14.45|14.54|16.70|
|Firms with female participation in ownership (%)|24.05|23.58|35.47|31.70|
|Firms with majority female ownership (%)|8.34|12.01|10.35|11.52|
|Firms formally registered at the start of operations (%)|87.96|88.00|88.45|89.56|
|Number of years firms operatied without formal registration (average)|0.66|0.74|0.72|0.66|
|**Ownership status**|||||
|Proportion of private domestic ownership in a firm (%)|77.94|84.24|89.65|93.09|
|Proportion of private foreign ownership in a firm (%)|12.75|8.73|7.43|4.37|
|<br>Proportion of government/state ownership in a firm (%)|0.53|0.34|0.59|0.77|
|Proportion of other ownership in a firm (%)|8.78|6.69|2.33|1.77|
|**Legal and export status**|||||
|Sole proprietorship (%)|47.32|53.24|34.03|35.05|
|Shareholding company with trade shares (%)|6.22|5.15|4.30|3.40|
|Shareholding company with non‐trade shares (%)|24.02|25.99|34.28|42.55|
|Partnership (%)|13.45|10.81|10.33|8.99|
|Limitied partnership (%)|8.28|4.56|15.03|8.07|
|Other status (%)|0.70|0.26|2.00|1.94|
|Exporter (%)|11.09|7.36|18.76|11.30|
|Proportion of total sales that are exported directly (%)|5.16|4.13|9.31|6.01|
|**Sectors**|||||
|Manufacturing (%)|36.91|33.55|63.91|53.94|
|Retail (%)|21.48|26.54|11.51|15.91|
|Other services (%)|41.61|39.91|24.58|30.15|
|Observations|4,302|4,295|31,486|29,230|
|Countries<br>_Source:_Calculations based on World Bank Enterprise Survey.<br>_Note:_Some variables exhibit missing values.||25||93|

32
<!-- Start of picture text --> UNODC ONDCP<br>Coca bush cultivation (hectares) Coca bush cultivation (hectares)<br>250000 250000<br>200000 200000<br>150000 150000<br>100000 100000<br>50000 50000<br>0 0<br>Bolivia Colombia Peru Bolivia Colombia Peru<br>2000 2001 2002 2003 2004 2005 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Source: UNODC <!-- Start of picture text --> Source: ONDCP<br><!-- End of picture text -->

# **Figure 3**

<!-- Start of picture text --> Coca bush cultivation in Colombia<br>UNODC vs. ONDCP<br>(hectares)<br>180000<br>160000<br>140000<br>120000<br>100000<br>80000<br>60000<br>40000<br>20000<br>0<br>1999 2000 2001 2002 2003 2004 2005<br>Colombia UNOCD Colombia ONDCP<br><!-- End of picture text --> Source: UNODC and ONDCP

**Figure 4**

52
opportunity cost of time. Combes and Lafourcade (2005) have also documented the existence of a strong relationship between GTCs and estimates of optimal travel time derived using GIS techniques for France.

# **4. Estimation of the Wage Equations**

In this section, we discuss in detail the second stage of our methodology, the estimation of the NEG wage equations [3] and [4]. A literature has developed following the seminal work of Hanson (1997, 2005) and Redding and Venables (2004) which consistently finds a strong positive relationship between RMA and nominal wages, and this has been used to provide supporting evidence for NEG models. This includes previous work for China by, _inter alia_ , Au and Henderson (2006), Bosker _et al._ (2010), Hering and Poncet (2010a, 2010b), and Moreno-Monroy (2008), although they typically confine themselves to the use of prefecturalcity data, whereas our sample also includes other types of prefectural-level regions (see Appendix A). Overall, with a sample size of 331 prefectures, this makes for a more comprehensive geographic coverage of China's territory, which is central to our purposes. Furthermore, previous NEG wage equation studies for China have focused on the estimation of wage equations using either aggregate data (Bosker _et al._ , 2010; Moreno-Monroy, 2008) or data for the urban sector only (Au and Henderson, 2006; Hering and Poncet, 2010a). However, our theoretical model predicts that there should be a positive relationship between RMA and nominal wages not only in the urban sector, but also in the rural sector. The estimation of equation [4], therefore, is important for establishing the adequacy of our underlying theoretical framework, and provides an important contribution in its own right.<sup>13</sup> To transform equations [3] and [4] into forms that are suitable for estimation we first assume that (relative) labor efficiency levels in both the urban and rural sectors of a region have two components – a deterministic component and an unobservable stochastic component – so that, for region _i_ ,  _iU_ <sup>_A_</sup> _iU_<sup>_e_</sup> _i_ and  _iR_ <sup>_A_</sup> _iR_<sup>_e_</sup> _i_ , where _AU_ and _A R_ denote the deterministic components for the urban and rural sectors respectively, and  and  are random variates which capture the stochastic components. Commencing with equations [3] and [4] and given * _U U U_ * _R R R wi_   _i wi_ and _wi_   _i wi_ , taking (natural) logarithms gives:

> 13 Without explicitly deriving it from a formal NEG model, Emran and Hou (2008) estimate a relationship between rural household consumption and measures of access to both domestic and international markets for China.

16
forested areas, CAR registration may actually aid deforestation (Jung _et al._ 2021). Intensification rather than deforestation can be encouraged by access to non-agricultural opportunities, as was the case with rural electrification in Brazil over the 1960-2000 period (Szerman _et al._ 2022).

# **4. Urban land policies**

Africa now is one of the regions with the fastest rate of urbanization globally (Henderson & Turner 2020) and its urban population is expected to triple to more than 1 billion by 2040 (Collier 2017). Urbanization can bring benefits through skill-based human capital externalities that foster productive jobs based on input sharing, labor market pooling, and knowledge exchange (Rosenthal & Strange 2020), thus attracting skill and increasing land values. But urbanization also imposes costs from congestion, contagious diseases, and crime (Duranton & Puga 2020) that may outweigh benefits from proximity unless countered by public investment in services and mobility, ideally financed from land price appreciation due to urbanization that will boost global competitiveness (Venables 2017).

# **4.1 Land taxation and urban local service delivery**

In developing countries with large informal sectors, a narrow tax base often limits the amount of taxes that can be collected centrally, implying that for many local governments, land taxes are a key source of revenue. Given the immobility of land, land taxation allows raising revenue to finance local public goods in a way that is progressive (Bonnet _et al._ 2021) and not distortive (Schwerhoff et al. 2020). Experts concluded that taxation of urban land and property is Africa’s largest source of untapped revenue (Collier et al. 2017).

Property taxes increase owners’ incentives to use land effectively (Oates & Schwab 1997), discourage land speculation (Norregaard 2013), and reduce vacancy rates (Segú 2020). They encourage compact urban development (Song & Zenou 2006; Ermini & Santolini 2017) and, in an open economy, can be outputenhancing (Kalkuhl & Edenhofer 2017). Local governments’ ability to raise local revenue provides the basis for accessing the municipal bond market to, among others, generate resources for large infrastructure investment. As local public goods will increase land values (Gonzalez-Navarro & Quintana-Domeque 2016; Coury et al. 2021) and services financed using property tax proceeds benefit local residents, property taxes strengthen the social contract between state and taxpayers (Besley & Persson 2014). Collection of property tax increased participation and accountability even in fragile low-capacity African settings (Weigel 2020) with willingness to pay such taxes enhanced by better service delivery (Kresch _et al._ 2023).

Traditionally, transaction taxes (‘stamp duties’) were the preferred instrument to collect such revenue from urban properties as they do not require costly creation of comprehensive tax maps or property valuation. Although satellite imagery and mass valuation techniques make land value capture easier, the regulatory environment has been slow to adjust and in Africa transaction taxes account for the lion’s share of transfer 26
**Table 3: Labor force participation rates by age-group, gender and sector,**

**1987-91(in percent).**

|**Age-groups**|**Rural**|**Urban**|**Male**|**Female**|**Total**|
|---|---|---|---|---|---|
|**1987**||||||
|7-16|43.77|14.81|36.72|30.58|33.74|
|17-25|66.26|46.58|60.26|58.01|59.08|
|26-45|78.18|71.02|79.81|72.00|75.51|
|46-60|76.90|70.84|78.60|71.73|74.86|
|>60|61.61|42.64|66.19|46.63|56.31|
|Total|62.00|44.45|58.23|53.59|55.80|
|**1988**||||||
|7-16|32.40|13.48|28.46|24.29|26.45|
|17-25|66.49|48.85|63.04|57.67|60.14|
|26-45|80.75|76.76|86.82|72.81|79.27|
|46-60|75.46|75.25|82.59|69.83|75.39|
|>60|60.72|50.82|68.00|49.10|58.35|
|Total|57.87|47.49|57.70|51.39|54.33|
|**1991**||||||
|7-16|47.54|6.30|36.78|30.54|33.78|
|17-25|81.29|36.26|60.31|67.44|63.99|
|26-45|97.05|86.25|94.50|91.93|93.01|
|46-60|94.17|86.09|95.64|88.60|91.79|
|>60|76.45|54.66|82.45|59.47|71.00|
|Total|75.02|47.27|65.41|65.42|65.41|

**Source: Authors calculations from GLSSI, 2, 3.**

**Note: Labor force participation is defined as the ratio of Individuals who are either wage workers or self-employed to the working population ( i.e. those between 15-64 yrs of age) Source: GLSS 1,2,3**

# **2.1. Determinants of Labor force participation**

Table 4 presents the results from the estimates of the labor force participation equation using maximum likelihood probit model. Age is included in the participation equation to reflect the effects of human capital investments on wages which will effect participation. As expected, age has a positive effect on work in all the three years. An interpretation is that as age increases, the level of human capital acquired increases and the offered wage goes up. An increasing wage, holding all else constant will increase the probability of participation. Age-squared is included as a regressor to pick up the possible non-linearities in this relationship. The significance of the squared terms in all the three periods supports the hypothesis of non-linear effects of age on the probability of participation. Education variable has mixed results. In fact, a person with no schooling 12
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|||Household<br>Dietary<br>Diversity<br>Score (HDDS)|The HDDS is based on 12 aggregated food groups<br>proposed by FANTA and the score ranges from 0 to<br>12. The NNS has 16 food groups: Cereals & products,<br>Vitamin A rich vegetables & tubers, white tubers &<br>roots, dark leafy green vegetables, other vegetables<br>including wild vegetables; vitamin A rich fruits; other<br>fruits including wild fruits; meat (fresh and processed);<br>organ meats (iron-rich); eggs; fish and shellfish;<br>legumes, nuts and seeds; milk and milk products, oils<br>and fats; sweets; spices condiments and beverages.<br>Aggregate groups are created for vegetables, fruits and<br>|Yes|No|
|---|---|---|---|---|---|
|Household|||meats.|||
|access to|NNS Form|||||
|diverse<br>|1.6|Household||||
|foods/diet||with HDDS >=<br>10|Household has a total HDDS score of at least 10|Yes|No|
|||**Household**|**Household consumed protein rich foods any**|||
|||**with protein**<br>**rich diet**|**number of times in past 7 days (meat & eggs &**<br>**milk products)**|**Yes**|**Yes**|
|||Household<br>with protein<br>intense diet|Household consumed protein rich foods at least 3<br>times in past 7 days (meat & eggs & milk products)|Yes|No|
|||Household<br>with vitamin A<br>rich diet|Household consumed vitamin A rich food in past 7<br>days|Yes|No|
|Mother’s||Mothers||||
|access to<br>diverse<br>foods/diet|NNS 24<br>hour-recall|dietary<br>diversity score<br>(MDDS)|NA|No|No|
|Household<br>|i|The fraction of<br>household||||
|access to<br>diverse|Not n<br>questionnaire|calories<br>|NA|No|No|
|foods/diet||derived from<br>starchy stables||||

Compared to other groups of factors associated with childhood undernutrition, indicators of household food security and food diversity have the greatest differences associated with family income and mother’s education. Overall, 1 in 5 children under-2 live in food secure households, and children in the richest quintile are five times as likely to be food secure than children in the poorest quintile. Children in the richest quintile are also nearly twice as likely to live in households with access to a greater diversity of foods compared to children in the poorest quintile. Household access to animal source foods is also associated with income: 72 percent of children under-2 among the richest quintile are in households that consumed protein rich foods in the past week, compared to 37 percent of children in the poorest quintile. Even fewer children under-2, less than 5 percent in the poorest two deciles, live in households that regularly consume protein-rich food sources (from meat, eggs and milk, at least 3 times in the past week) compared to over 30 percent in the richest two deciles (Figure 20). The average number of times weekly that the poorest 20% of households consume milk or milk products is 2.7, compared to 4.8 times for the richest 20% of households. For eggs, the difference is 2 versus 3.7 times 33
higher for lower rated bonds. For instance, for Ghana which is rated B- by S&P (one notch above high default risk), the spread could jump from around 509 basis points if issued under U.S. law to 685 if issued under U.K. law (Figure 9).

**Figure 9: Impact of governing law difference on bond spreads**

_Spread‐to‐benchmark (bps)_ <!-- Start of picture text --> 700<br>650<br>600<br>550<br>500<br>450<br>400<br>350<br>300<br>BB+ BB BB‐ B+ B B‐<br>UK law US law<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations<br><!-- End of picture text --> Next, we turn our attention to first-time issuances. We created a dummy identifying issuances from governments without a history of issuing dollar bonds in these two markets. The results are reported in table 8. The coefficient on the first-time issuer dummy is insignificant in the pre-crisis period and only weakly significant post-crisis. The results confirm the findings from the benchmark specification that U.K. governing law spreads are higher. Also, first-time issuance is not important enough to fully explain the observed spread differences substantially.

16
|||Yes|||No||
|---|---|---|---|---|---|---|
||Public<br>DMS|In-<br>house|Comm<br>.|Public<br>DMS|In-<br>house|Comm.|
|**Doesyour system:**|||||||
|Connect to other software such as Excel,Matlab,etc.?|15|6|5|1|3|2|
|Decompose composite currencies,such as SDR,into original currencies?|7|4|1|8|5|5|
|Produce risk indicators (e.g. average time to maturity/refixing, share of debt to be<br>refixed/refinanced,etc.)?|10|6|5|6|3|2|
|Provide analytical tools toperform market risk or scenario analysis?|11|6|3|5|3|4|
|Support credit risk management functions?|6|3|5|10|6|2|
|**Doesyour system:**|||||||
|Monitor instruments for the entire lifecycle (e.g. the recording of transactions and<br>maintenance until maturity)?|16|9|7|0|0|0|
|Generate SWIFT messages?|2|4|4|14|5|3|
|Allowyou to enter/trackpayment invoices from creditors?|7|3|4|9|6|3|
|Send alerts based on external events(e.g. upon receivingapayment invoice)?|2|4|2|14|5|5|
|Check calendars for holidays at the time of deal entry?|11|7|5|5|2|2|
|Support Straight-Through-Processing?|4|7|7|11|2|0|
|Produce consolidated/individual reports?|15|9|7|1|0|0|
|Supportyour cash managementplanning?|8|9|4|7|0|3|
|Connect with the Treasurymanagement system if any?|2|5|5|12|4|2|
|Connect with the financial management information system(FMIS)?|3|1|2|11|8|4|
|Import market rates(e.g. interest rates,FX rates,etc.)?|6|8|6|9|1|1|
|Record audit trails?|12|7|5|3|2|2|
|Include securityfeatures(e.g. automatic backups,data validation controls,user hierarchy)?|14|9|6|1|0|1|
|Have a technical support and maintenance agreement/framework?|13|9|7|2|0|0|
|**Doyou have the appropriate systems technical support?**|12|9|7|3|0|0|
|**Doyou thinkyour DMS needs updating? **|10|5|5|4|4|2|
|**Doyou have resources available to updateyour debt management system?**|8|8|6|5|1|1|

34
# 1. INTRODUCTION

Development effectiveness has been at the center of the economic debate since at least the establishment of the Bretton-Woods institutions. With $16 trillion in aid delivered since then, this is by all means an important issue.<sup>1</sup> Despite all efforts, however, the knowledge gap on what really works in development remains large. The literature investigating the relationship between country growth and aid flows has for the most part failed to provide satisfactory answers to the question of whether aid is effective and under what conditions. This is in part because aid measured in dollars and cents implicitly assumes that aid is a transfer of resources, a noisy proxy for the bundle of technical and financial assistance through which aid is delivered.

Far from being a direct transfer, aid is delivered through development projects and general budget support, each representing historically 86 and 14 percent of the total.<sup>2</sup> Both of these instruments bundle technical assistance with financial flows, the former affecting the effectiveness of the latter. Recent evidence (Denizer et al. 2013) finds that most of the variation in aid performance is explained by project characteristics (and not by country conditions). This suggests that the relevant question may not be whether aid is effective but what quality of projects and project characteristics make aid effective.

With $3.5 trillion committed to developing countries since 1970,<sup>3</sup> the World Bank is a large development actor, and one that invests much effort in the quality of projects, from peer reviews to quality enhancing processes. Still, about a quarter of projects are assessed to have been of unsatisfactory quality at entry<sup>4</sup> and a quarter rated unsatisfactory at exit. Projects also experience implementation delays with lags of six to seven quarters between project approval and first disbursement and continued gaps between actual and planned disbursements thereafter (World Bank 2014a, 2014b).

In 2005, the Bank started an institutional effort to do impact evaluation (IE) to generate knowledge in the context of national policies and programs in developing countries (Figure 1). This is also when the Bank started tracking the link between IE and its projects.<sup>5</sup> Over the next few years, and starting

> 1 Net official development assistance and official aid received (current U.S. dollars). Source: data.worldbank.org.

2 http://aiddata.org.

3 IBRD loans and IDA credits (DOD, current U.S. dollars). http://data.worldbank.org.

> 4 Quality at entry is assessed by the Quality Assurance Group (QAG) on the basis of a sample of projects approved by the Board in the previous fiscal year. Reports are shared with management for remedial action.

> 5 Before 2005, there were one to five studies a year evaluating World Bank projects. After 2005, the Bank started a concerted effort to evaluate Bank projects and track the link between projects and IEs. The number of IEs related to projects jumped to about 20 a year after 2005.

2
**_Famniy Plannig Success In No Chties_** _In Zaire_

_24_ distributors who dropped out of the program were replaced, so as to ensure full coverage of the Matadi neighborhoods. Second, closer attention could have been paid to the "low performers," those who remained in the program but were relatively inactive. While attempts were made to address both these points, they could have been pursued more aggressively.

With regard to all service providers, PRODEF included, it would have been usetul to focus more attention on the quality of care. Much of the effort of the natiunal program was directed simply to getting services in the field, possibly at the expense of monitoring closely the technical quality of the services. This entails not only closer supervision, but a specific look at the elements which constitute quality of care.

When PRODEF was originally designed in 1980, there was considerable skepticism among Zairian officials that any change in contraceptive use could be achieved. PRODEF/Matadi was designed (1) with an aggressive strategy (household distribution) intended to bring about change, and (2) with a strong research componen **t** o measure change if in fact it occurred. While this project has provided evaluation data on changes in contraceptive prevalence which are lacking in many emerging programs in Africa, it may have been at the expense of reduced attention to service delivery.

# HI. Kananga: Clinical Services and Social Marketing

## **A.** Characteristics of the City and Its Population

The city of Kananga is located 800 kilometers east of Kinshasa, a one-hour flight by plane or a four-day trip over difficult roads. The city-fifth largest in Zaire with a population of 299,000 as of 1984-was built in the early 1900s as a railroad town. Today it is the administrative capital of the West Kasai region and an educational center with numerous poorly-equipped secondary schools and several university-level institutes. Kananga workers find few jobs available outside the government and educational system; unemployment is high. Most households "make do" by farming fields on the city outskirts and by petty trading.

Most Kananga women have attended school and are literate. In the Contraceptive Prevalence Survey of 1982 [31, 63 percent of the women aged 13 to 49 said they could read, including substantial proportions even at older ages (44 percent at ages 35-39 and 29 percent at ages 40-44). In a 1987 citywide survey [18], researchers asked the women to read a short passage in Tshiluba; 70 percent of the women were able to read it. The 1987 survey found that 48 percent of the women aged 13-49 had had at least some secondary schooling; an additional 44 percent had had primary schooling.

A considerable number of the women in the 1987 survey did some kind of work outside their homes, including over a third of the women in their 20's, two-thirds of women in their 30's, and three quarters of women in their 40's. Of those with outside work, two thirds were in some kind of trading, while an additional one-fourth did farming. Only a very few had salaried employment.