rafmacalaba/datause-encoder-data huggingface dataset:
were considered for inclusion. Of these, 140,000 families within 37 municipalities were selected for participation in the intervention (World Bank 2013).

In 2009, Red Unidos expanded to 1.5 million families across Colombia, covering 1,102 municipalities (Agencia Nacional para la Superación de la Pobreza Extrema (ANSPE) 2012). For this second phase, the Colombian government “broke” the link with _Familias en Acción_ , and selected families based on poverty status as measured by the SISBEN, in addition to internally displaced families.

Finally, in 2012, eligibility was only driven by the SISBEN score. By the end of 2012, 10,376 _Cogestores Sociales_ (i.e. social workers) were following 1,404,847 families in 1,102 municipalities. Similarly to _Chile Solidario,_ each beneficiary family agrees on a Family Plan and works towards achieving the 45 conditions deemed as crucial to attaining minimum welfare conditions (see below). By the end of 2012, 1,252,499 families agreed upon a Family Plan; 900,502 families had at least one visit by a _cogestor_ social to follow up on their agreed goals; and 157,064 (51,976) families surpassed poverty according to the multidimensional poverty (income poverty) measure. Among these, 35,017 families surpassed poverty according to both the multidimensional and poverty measures, and graduated from _Red Unidos_ .

# **Service modalities**

_Red Unidos_ is based on two main principles: the service and the families work together to improve family well-being, and accomplish minimum welfare conditions; and, to reach these objectives, the service provides personalized ( _tailor-made_ ) family counseling interventions. Similar to _Chile Solidario_ , there are 45 welfare conditions used by _Red Unidos_ to assess households’ wellbeing, grouped along nine dimensions: (1) identification, (2) income and labor, (3) education and training, (4) health, (5) nutrition, (6) housing, (7) family dynamics, (8) financial inclusion and savings; and (9) access to justice. A tenth dimension is attention to internally displaced families (see Table 4). In addition, there were initially 22 basic territorial conditions (requirements), which the municipalities had to fulfill, in order to promote the accomplishment of the 45 welfare conditions or goals for the families (see the Appendix for a detailed list of territorial conditions). Since 2011, however, these territorial conditions are not binding anymore.

20
_Table 2.3 Educational Distribution_ _<u>by Gender, 1988, 1997 and 2004 (percentage)</u>_

|_Educational_<br>_Group_|_Primary_<br>_incomplete_|_Primary_<br>_complete_|_Lower_<br>_secondary_<br>_complete_|_Upper_<br>_secondary_<br>_complete_|_University_<br>_complete_|
|---|---|---|---|---|---|
|_1988_||||||
|Male|19.0|30.1|24.5|14.6|11.8|
|Female|17.3|22.2|23.2|29.1|8.2|
|Total|18.5|27.7|24.1|18.9|10.7|
|_1996 _||||||
|Male|13.3|26.2|27.6|17.8|15.1|
|Female|12.3|20.9|21.8|29.2|15.8|
|Total|12.9|24.4|25.7|21.6|15.4|
|_2002_||||||
|Male|10.1|23.3|31.5|18.8|16.2|
|Female|9.7|19.2|25.4|28.1|17.6|
|Total|10.0|21.9|29.5|21.9|16.7|

_Source:_ Calculations based on the ENEU survey (third quarter).

_Figure 2.2 Cumulative Educational Distribution by Gender, 1997_ <!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>University Uppeer Sec Lower Sec Prim Comp<br>Educational Level<br>Male Female<br>%<br><!-- End of picture text --> _Source:_ Calculations based on ENEU data.

13
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

## **Achievements**

Burkina has made significant progress toward ensuring that all of its population can access improved water. Close to 97 percent of the population gets water through some type of technology, leaving 2.7 percent of the population with surface water. This compares extremely favorably against other African LICs, where, on average, one-third of the population is relying on surface water on a regular basis (table 8). Focusing on what the World Health Organization (WHO) Joint Monitoring Program (JMP) defines as improved water services (piped water, stand posts, and protected wells/boreholes), Burkina has shown impressive progress over the past decade. Overall access to drinking water improved from 59 percent in 2003 to 74 percent in 2007, reaching 79 percent in 2009 (figure 12a).

**Table 8. Benchmarking water and sanitation indicators**

||Unit|Low-income<br>countries|**Burkina Faso**|Middle-income<br>countries|Unit|
|---|---|---|---|---|---|
|||Mid-2000s|**Early 2000’s**|**Late 2000s**|Mid-2000s|
|Access to piped water|% pop|10.5|5.7|4.2|52.1|
|Access to stand posts|% pop|16.2|13.1|17.2|18.9|
|Access to wells/boreholes|% pop|38.3|67.6|76.0|6.0|
|Protected wells/boreholes|% pop||40.6|52.4||
|Access to surface water|% pop|37.4|13.4|2.7|13.0|
|Access to flush toilets|% pop|4.9|1.8|4.4|40.8|
|Access to improved latrines|% pop|9.9|13.2|21.9|1.4|
|Access to traditional latrines|% pop|50.1|13.2|16.0|30.4|
|Open defecation|% pop|40.3|71.7|58.5|14.3|
|Revenue collection|% sales|62.7|85.0|94.5|100.0|
|Distribution losses|% production|34.3|16|18|26.8|
|Cost recovery|% total costs|56.0|100|100|80.6|
|Labor efficiency|connections per<br>employee|158.6|125|200|368.7|
|Total hidden costs as % of revenue|%|162.7|33|6|140.4|
|||**Burkina Faso**|Countries<br>water re|with scarce<br>sources<br>O|ther developing<br>regions|
|||Mid 2000s||||
|Residential tariff|U.S. cents per m<sup>3</sup>||**76**|60.26|30 600|
|Nonresidential tariff|U.S. cents per m<sup>3</sup>||**215**|120.74|. – .|

_Source:_ Derived from the AICD national and utility database (www.infrastructureafrica.org/aicd/tools/data).

Progress in providing improved sanitation accelerated after 2003. According to the JMP, less than 20 percent of the Burkinabè population had access to improved sanitation in 2003, while close to two-thirds of the population engaged in open defecation practices. By 2009 close to 50 percent of the population were using latrines and, a small percentage, flush toilets (figure 12b). Yet it is widely accepted that Burkina is running short of meeting the MDG in sanitation.

22
5

**Figure 4: High and Low-Income Commodity Importing Economies – Complementarity and Competition with Low-Income Commodity Exporting Economies**

||**High-income importing**<br>**economy**|**Low-income importing**<br>**economy**|
|---|---|---|
|Pollution and energy<br>intensity|High preference to<br>outsource to exporting<br>economy|Indifferent to location|
|Complementary or<br>competitive industrial<br>structures|Complementary – focus on<br>technologies with high<br>barriers to entry|Competitive – importers<br>also have low technology<br>industrial structures|
|Labor costs|High wages militate<br>against labor intensive<br>processing|Low wages facilitate<br>labor-intensive processing|

Although these factors may be pervasive across sectors, their intensity will of course differ. Some sectors produce much more differentiated goods than others (organic fair-trade bananas compared to commodity bananas). Sectors and countries also differ in their technological intensity and producers are located in a variety of different forms of operating conditions.

In this paper we explore the examples of two soft-commodity sectors - cassava in Thailand (Section 2) and timber in Gabon (Section 3). Our market reference point is the shift in final market from the high-income EU market to the low-income China market. In both cases we explore how this shift in final market is reflected in the extent and nature of standards incorporated in the value chain, and in the capacity of low-income commodity exporters to upgrade to high value added and more technological complex links in their value chains.

# **2. THE THAI CASSAVA VALUE CHAIN**

Cassava is a widely grown crop globally. The major reason for this ubiquity is that it grows on relatively poor quality land and is relatively drought-resistant, thus serving as a food-crop-of-last-resort in many countries. But it is also an important intermediate product, feeding into the animal-feed, bioethanol and the starch markets. Since raw cassava is poisonous to human beings, bulky and perishable, the trade in cassava necessarily occurs in processed form. In 2008, global trade in cassava products was $1.124bn, and although the largest producers were Brazil and Nigeria, Thailand was the world’s largest cassava exporter (Figure 5), accounting for around 80 percent of global trade in both major product families ($910m) (COMTRADE, accessed 26 November 2009).
8 China’s poorest provinces. But it also probably reflects the limitations of the single-question approach to eliciting income in household surveys. We have trimmed outliers on both the out-ofpocket payments and income distributions: the top half of one percent of cases on the out-ofpocket payments distribution and the bottom half of one percent of cases on the income distribution were removed. We define health expenses as ‘high’ if they exceed 5% of the sample mean per capita household income, and as ‘catastrophic’ if they exceed 10% of the household’s _own_ per capita income (cf. e.g. Ranson 2002; Wagstaff and van Doorslaer 2003; Xu, Evans et al. 2003). As is apparent from Table 3, the rates reporting ‘high’ and ‘catastrophic’ out-of-pocket payments vary considerably across the surveys (the CHNS having far lower rates), and to a lesser extent over time within each of the two longitudinal surveys.<sup>6</sup> The far lower rates in the CHNS reflect in part the relatively low out-of-pocket payment amounts in our data. The shares are well below those in the Chinese Rural Household Survey, for example. In part, this probably reflects the relatively large number of cases in the CHNS with missing values for the various questions on medical expenses. We have adopted a conservative strategy in such cases, by replacing missing values by zero. Since missing values are somewhat more common in the data among those with insurance, we will end up _underestimating_ the effect of insurance on the probability of ‘high’ or ‘catastrophic’ medical expenses.

Health insurance coverage varies across the surveys and across waves in the case of the longitudinal surveys. There is no clear trend in the case of the CHNS in the fraction of the sample with some form of coverage, although a continued decline of the traditional schemes

> 6  It seems likely that the short (4-week) recall period used in the CHNS would result in a larger annualized out-of-pocket spending figure than the long (12-month) recall period used in the other two surveys. This is borne out by analysis of data from the 1998 Vietnam Living Standards Survey, which inquires about health spending over a 12-month period, and about use of services and medical expenses during the last 4 weeks. Responses to the spending questions covering the last 12 months produce a budget share (share of per capita household consumption) of 5.2%, while the questions covering the last 4 weeks produce an annualized budget share of 9.1%. The fractions of the sample with ‘high’ and ‘catastrophic’ expenses, defined along the lines of the present paper, are also higher when the 4-week questions are used.
**Table S7:** Program impacts on labor market outcomes and consumption (beneficiaries: **secondary** outcomes) **<u>Panel A:  Baseline Dropouts</u>**

||Effective<br>Daily Wage<br>(Past 7 Days)|Labor Income<br>(Past 5 Seasons)|=1 if Any<br>Wage Work in<br>Past 3 Months|Real T<br>C|otal Househo<br>onsumption (|ld Monthly<br>USD)|
|---|---|---|---|---|---|---|
||Tw|o Years After Pro|gram|During<br>Program|End of<br>Program|Two Years<br>After Program|
|=1 if Conditional Schoolgirl|-0.228|4.129|-0.020|-0.257|-1.941*|0.535|
||(0.148)|(8.620)|(0.037)|(1.029)|(1.113)|(1.130)|
|Mean in Control Group|0.753|52.840|0.366|17.502|20.860|17.977|
|Sample Size|263|744|744|712|719|737|
|**Panel B: Baseline Schoolgirls**|||||||
|=1 if Conditional Schoolgirl|0.121|7.476|-0.010|3.192**|3.223**|2.804*|
||(0.424)|(7.466)|(0.030)|(1.261)|(1.364)|(1.432)|
|=1 if Unconditional Schoolgirl|-0.549*|10.688|0.001|-0.586|-0.880|-0.817|
||(0.285)|(12.721)|(0.055)|(1.441)|(1.524)|(1.876)|
|p-value UCT vs. CCT|0.278|0.829|0.838|0.032|0.034|0.127|
|p-value Treatment|0.121|0.420|0.939|0.030|0.044|0.137|
|Mean in Control Group|0.902|33.302|0.250|18.638|23.342|20.774|
|Sample Size|465|2,049|2,049|2,006|2,021|2,040|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Effective daily wage is calculated using earnings and activities in the past seven days. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
In the home good production sector, the efficiency parameter <sup></sup> is normalized at unity and the curvature of the home production function initially at <sup></sup> = 07, to capture rapidly decreasing marginal returns in terms of women’s time and infrastructure services. In the experiments reported in the next section, a smaller value of <sup></sup> = 012, corresponding to the value used by Kimura and Yasui (2010, Table 4) for a production function with labor only, will also be used to capture smaller decreases in marginal returns initially.

To estimate women’s time allocation, we use the 2009 PNAD data and the calculations in Ribeiro and Marinho (2012). They estimate that the time spent by women in household chores is 20.2 hours per week, and that the time spent in market work is 41.4 hours per week. To calculate total time available in a week, we subtract from raw time (7 · 24 = 168 hours) time spent sleeping (8 hours a day, or a total of 56 hours), time spent on personal care (2 hours a day, or 14 hours). Weekly time available is thus 98 hours. The time spent by women in home production can consequently be estimated as <sup></sup> = 20298 = 0206, whereas the time spent in market work can be estimated as <sup></sup> = 41498 = 0421.

Time spent by women to accumulate human capital in adulthood is estimated by using an estimate on the number of years devoted to human capital accumulation, divided by the length of the period in adulthood, 25; thus, <sup></sup> = 4625 = 0185. As discussed earlier, this estimate accounts not only for time spent in formal, tertiary education during adult life (an average of four years for women with higher education in Brazil) but also for the time spent during adulthood accumulating human capital through other means–seminars, on-the-job training, personal reading, etc. To estimate rearing time, we assume that women have one hour a day for leisure (or 7 hours a week), or 0071 in percentage of total time available.<sup>24</sup> Thus, total rearing time can be estimated as <sup></sup> = 1 − 0206 − 0421 − 0185 − 0071 = 0117, implying that time spent on each child is <sup></sup> = 0056. Using the data on <sup></sup> as well as  below, and the definitions of Λ1 and Λ2 provided earlier, formula (32) can be solved backward for the

> 24Even though leisure is not explicitly accounted for in the model’s description of women’s time allocation, we introduce it (exogenously) in the calibration in order to obtain more reasonable estimates of women’s time allocated to child rearing.

22
Table <u>3: In-sample Estimation Results of Mark-up Models</u><sup>a</sup>

|Regressor|Mark-upModel I|Mark-upModel II|
|---|---|---|
|Constant|-1.21 (5.74)|-0.57(3.98)|
|ECt-1|-0.09 (2.99)|-0.05(2.55)|
|∆pt-1|0.32 (4.44)|0.37(5.76)|
|∆pt-2<br>|-0.11 (1.43)<br>|-0.04(0.57)<br>|
|∆pt-3|-0.12 (1.43)|-0.09(1.71)|
|∆pt-4|-0.13 (1.73)|-0.14(2.69)|
|∆pt-5|-0.08 (1.27)||
|∆pt-6|-0.08 (1.30)||
|∆pt-7|-0.13 (2.25)||
|∆apt<br><sup>*</sup>||0.21(5.74)|
|∆(et+p<br>t<sup>)</sup><br><sup>*</sup>|0.13 (3.75)|0.06(1.88)|
|∆(et-1+p<br>t-1<sup>)</sup><br><sup>*</sup>|-0.09 (2.41)|-0.06(2.81)|
|∆(et-2+p<br>t-2<sup>)</sup><br><sup>*</sup>|-0.07 (2.12)|-0.07(2.28)|
|∆(et-3+p<br>t-3<sup>)</sup><br><sup>*</sup>|-0.01 (0.27)||
|∆(et-4+p<br>t-4<sup>)</sup>|-0.07 (2.20)||
|∆wt|0.06 (2.37)|0.08(3.69)|
|∆wt-1|0.06 (2.45)|0.07(3.51)|
|1994.4 dummy|0.11 (5.47)|0.04(1.91)|
|2001.4 dummy|0.03 (2.04)|0.02(1.01)|
|**_Other Statistics_**<br>|||
|R<sup>2</sup><br>|0.716|0.752|
|Adj R<sup>2</sup>|0.671|0.719|
|S.E.E|0.013|0.012|
|Jarque-Bera|6.660|3.841|
|LM AR (12)|23.807|31.233|
|LM ARCH (12)|11.415|10.427|
|White-Heteroskedasticity|42.269|31.663|
|Reset|16.662|3.798|

a: Numbers inside the parentheses are absolute values of the t-ratios.

In the case of mark-up model I, the lagged error-correction term (ECt-1) has a size of 0.09, which is negative and statistically significant, suggesting a relatively fast convergence to the long-run equilibrium in the face of shocks. The error-correction term in the mark-up model II also carries its correct negative sign and is statistically significant, supporting the existence of a long-run relationship among the variables involved. The ARDL error-correction term in the mark-up model II, however, has a 23
# **Figure 2: % of Beneficiaries who Achieved the Goals within each dimension**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Identification Employment Education Health Housing Family<br>dynamics<br>Baseline Treated<br>Average Indicators Achievement<br>Chile Solidario Unidos Chile Solidario Unidos Chile Solidario Unidos Chile Solidario Unidos Chile Solidario Unidos Chile Solidario Unidos<br><!-- End of picture text --> Source: Administrative data provided by program staff.

29
**How Do Cities in Ethiopia Create Jobs?**<sup>*</sup> Megha Mukim<sup>±</sup>

_Keywords:_ Cities, Jobs, Gazelles, Agglomeration, Industrialization _JEL Classification:_ O12, R11, R12

> * The author is grateful to Bob Rijkers, Tom Farole, Somik Lall, Roland White, Ruth Hill and Onur Ozlu for providing feedback, comments and suggestions. Lars Christian Moller, Johanne Buba, Eyasu Tsehaye and Karan Singh helped facilitate data access and readiness. This research was carried out as part of the Ethiopia Urbanization Review project at the World Bank Group.

> ± Megha Mukim is an Economist and team leader of the Competitive Cities Project, a joint-collaboration between the Trade & Competitiveness and the Social, Urban, Rural and Resilience Global Practices at the World Bank Group (1818 H Street NW, Washington DC 20433).
Annuities form only a small component of life insurance business (Table 14). Annuity business attracts about one-sixth of total premiums. The business has shown some volatility in recent years, usually in response to changes in government incentives under its retirement income policy. However, as shown in Table 15, allocated annuities, mostly related to superannuation, dominate total annuity business. Since 2005 lifetime annuity business has almost completely disappeared.<sup>33</sup>

**Table 14:  Annuity Business of Life Companies (year to 30 June)**

||2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|
||||percent|of total|||
|**Individual**|||||||
|Allocated: non-investment linked|4.4|3.2|0.8|0.8|1.2|2.2|
|Allocated: investment-linked|44.1|36.4|32.7|57.9|71.7|74.1|
|Market-linked (a)|0.0|0.0|0.0|0.6|1.0|1.1|
|Lifetime|2.8|5.1|6.3|0.6|0.5|0.4|
|Term|45.9|49.6|53.4|29.8|19.8|20.8|
|**Group**|||||||
|Allocated: non-investment linked|1.4|3.4|4.9|8.6|0.9|0.8|
|Allocated: investment-linked|1.4|2.4|1.9|1.8|2.0|0.7|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|
|Total(AUD billion)|6.10|5.10|6.70|4.30|6.30|6.8|
|Total(% GDP)|0.80%|0.63%|0.80%|0.48%|0.65%|0.66%|

**Source:  APRA Prudential Rules 26 and 32 collections**

**Table 15:  Superannuation Assets of Life Companies (year to 30 June)**

||2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|
|Total Assets (AUD billion)|188.4|186.5|196.9|211.4|231.7|256.6|
|Total Assets (% GDP)|24.8%|23.1%|23.4%|23.6%|24.0%|24.8%|
|Superannuation Assets (% total)|86%|86%<br>percent|87%<br>of total supe|88%<br>rannuation a|89%<br>ssets|90%|
|Lifetime Annuities|2.4|2.8|2.6|2.5|2.1|1.7|
|Term Annuities|3.3|4.3|3.6|3.9|3.2|2.8|
|Allocated Annuities|13.5|12.7|12.3|12.2|12.4|13.7|
|Investment Account|14.1|13.8|12.6|11.0|9.6|8.4|
|Investment Linked|66.7|66.4|69.0|70.4|72.7|73.4|
|TotalSuperannuation Assets|100.0|100.0|100.0|100.0|100.0|100.0|

**Source:  APRA Life Office Market Reports**

One factor, which has a bearing on the popularity of lifetime annuities, is the value that they provide to their purchaser as measured by the money’s worth ratios. Knox (2000) found that the majority of money’s worth ratios for Australian annuities are in the range of 85-95 percent of the purchase price. He also observed that the results in Australia, even though the market is poorly developed, are consistent with major international markets and that the MWRs compare favorably with recent research for the UK and US markets. However, in more recent research Ganegoda (2007) reports that when compared with previous calculations for Australian annuities, it appears that the money’s worth of Australian annuities have dropped significantly reflecting high loadings, such as

> 33 Only four companies are currently providing regular quotes on life annuities compared with 11 in 1998 (Ganegoda 2007)

40
specific services. The structural and process indicators are based on the Rwandan clinical practice guidelines.<sup>13, 14</sup> The quality indicators in Box 2 are assessed through the regular monitoring system, in which district hospitals monitor and supervise the quality of health centers in their districts. Every quarter, supervisors from the district hospital visit each facility on an unannounced randomly chosen day, and assess quality indicators through direct observation and review of patient records. At the end of the visit, they discuss their findings with the facility’s personnel, and provide recommendations to improve quality of services. In P4P districts, information from the visit is used to compute each facility’s overall quality score.

P4P payments go directly to facilities and are used at each facility’s discretion. In the sample of 80 treatment facilities in the study, the P4P payments increased average overall expenditures by 22 percent. On average, facilities allocated 77 percent of the P4P funds to increase personnel compensation, amounting to a 38 percent increase in staff salaries.

# **_Experimental Design_**

The evaluation design took advantage of the phased implementation of the program at the district level over a 24-month period. Administrative districts with pre-existing NGO P4P schemes were excluded from the sampling frame. The remaining districts were then grouped into 8 pairs based on similar characteristics for rainfall, population density, and predominant livelihoods using data from the 2002 Census. One side of each pair was then randomly assigned into the treatment group and the other into the comparison group.

Just before implementation of the baseline survey, the administrative district boundaries were redrawn in the context of a decentralization effort.<sup>17</sup> As a result, some of the experimental 4