rafmacalaba/datause-encoder-data huggingface dataset:
|**_Random assignment pay_**||||
|---|---|---|---|
|Pay Group 2|-0.489***|-0.489***|-0.485***|
||(0.018)|(0.018)|(0.024)|
|Pay Group 3|-0.868***|-0.868***|-0.871***|
||(0.018)|(0.018)|(0.024)|
|Pay Group 4|-1.071***|-1.071***|-1.090***|
||(0.018)|(0.018)|(0.024)|
|Pay Group 5|-1.237***|-1.237***|-1.262***|
||(0.019)|(0.019)|(0.026)|
|Female Respondent||-0.006||
|||(0.018)||
|Constant|4.044***|4.047***|4.027***|
||(0.022)|(0.024)|(0.029)|
|Sample|All|All|Female|
|Observations|23,880|23,880|14,008|
|R2|0.302|0.302|0.312|
|Adjusted R2|0.302|0.302|0.311|

notes: Standard error in parenthesis; .01 - ***; .05 - **; .1 - *; Columns 1-3 report results of ordinary least squares regression; Dependent variable: 1=Severely overpaid, 2= Somewhat overpaid, 3= Fair, 4=Somewhat underpaid, 5=Severely underpaid; Controls for survey round not shown. Base category for gender is female, Base category for age is 25, Base category for pay is the lowest pay.

Table 6 reports interaction terms for a female subject of the vignette with respect to the perceived appropriateness of pay by random level of pay described. The results reported in column 1 show that within both male and female groups, a higher randomly assigned wage rate led to a lower share of respondents describing the subject as underpaid. The systematic difference for the effect of the wage rate between male and female subjects was again dominated by the randomly assigned gender of the vignette subject. Figure 4 reports the key results from tables 5 and 6 graphically.

**Table 6: Randomized characteristics interacted: level of pay**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**_Interaction sex#level of pay_**||||
|Female#Pay Group 2|-0.505***|-0.505***|-0.512***|
||(0.025)|(0.025)|(0.033)|
|Female#Pay Group 3|-0.849***|-0.849***|-0.847***|
||(0.024)|(0.024)|(0.032)|
|Female#Pay Group 4|-1.019***|-1.019***|-1.013***|
||(0.024)|(0.024)|(0.032)|
|Female#Pay Group 5|-1.178***|-1.178***|-1.167***|
||(0.024)|(0.024)|(0.033)|
|Male#Pay Group 1|0.209***|0.210***|0.248***|
||(0.025)|(0.025)|(0.033)|
|Male#Pay Group 2|-0.263***|-0.263***|-0.208***|
||(0.026)|(0.026)|(0.035)|
|Male#Pay Group 3|-0.678***|-0.678***|-0.644***|
||(0.025)|(0.025)|(0.034)|
|Male#Pay Group 4|-0.914***|-0.914***|-0.918***|
||(0.025)|(0.025)|(0.032)|

11
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 2. Kenya’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-income<br>countries|**Kenya**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1000 km2 of arable land|86.6|**152**|507.4|
|Unpaved road density|km/1000 km2 of arable land|504.7|**930**|1,038.3|
|GIS rural accessibility|% of rural population within 2 km of all-<br>season road|21.7|**32**|59.9|
|Paved road traffic|Average annual daily traffic|1,049.6|**1,108**|2,786.0|
|Unpaved road traffic|Average annual daily traffic|62.6|**38**|12.0|
|Paved network condition|% in good or fair condition|80.0|**84**|79.0|
|Unpaved network condition|% in good or fair condition|57.6|**63**|58.3|
|Perceived transport quality|% firms identifying roads as major<br>business constraint|23.0|**37**|10.7|

Source: Gwillliam and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data.

# **Challenges**

Nevertheless, the country faces a huge rehabilitation backlog that must be addressed before the trunk network can be considered to be in a maintainable condition (figure 4). As of 2006, levels of capital spending for the roads sector—at around 1 percent of GDP—were low by regional standards and fell substantially short of what would be needed to clear the rehabilitation backlog in a reasonable period of time. There is a need for a one-time push on road sector investment to remedy this situation.

**Figure 4. Kenya is not spending enough to catch-up with its road rehabilitation backlog**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>-150<br>Ethiopia Tanzania Uganda Kenya<br>maintenance rehabilitation<br>Source: Gwillliam and others 2009.<br>spending as % of requirements<br><!-- End of picture text --> However, systemic issues affect the country’s public investment system. These will need to be addressed to ensure that any major scale-up in capital expenditure is cost-effective. Road investments have been characterized by low rates of budget execution (only about 60 percent of the 2006 budget was spent), cost overruns of as much as 80 percent over engineering estimates, and lengthy delays that tend to 7
In Jordan, Rodriguez, Wai-Poi, and Woodham (2022) proposed five “care” subcategories to represent the important differences in the amount of home-based care and social reproduction necessary as well as in the share of these activities accomplished by different household members (Figure 3). Three of the five categories represent a quarter to a third of each of the following Jordanian population groups: married with dependents, married with dependents and other adults, and no dependents.<sup>34</sup> The _single adult with dependents_ category is smaller (at 5 percent); it was included in the analysis as the implications for care responsibilities are significant. A single adult must bear all of the burden, perhaps with some help from older children or elderly members.

Figure 3. Care Categories in Jordan <!-- Start of picture text --> 2+ unmarried<br>adults with<br>dependents<br>(seniors or<br>seniors and<br>children)<br><!-- End of picture text --> <!-- Start of picture text --> Married couple<br>and 1+ other adults:<br>1+ children (at<br>least one is 0-5),<br>no seniors<br>1+ children (all<br>6-14), no seniors<br>1+ seniors, no<br>children<br>1+ seniors, 1+<br>children<br><!-- End of picture text --> <!-- Start of picture text --> Married couple 2+ unmarried<br>and 1+ other adults: adults with<br>dependents<br>1+ children (at<br>Married, Other<br>least one is 0-5),<br>no seniors (seniors or<br>other adult seniors and<br>1+ children (all<br>6-14), no seniors 31 children)<br>11<br>1+ seniors, no<br>children<br>1+ seniors, 1+<br>children<br>23<br> man<br>Married couple 31 woman<br>with: 5<br>dependents<br>1+ children (at<br>Married, Married adults, other<br>least one is 0-5),<br>no seniors<br>2+ unmarried adults,<br>1+ children (all<br>no dependents<br>6-14), no seniors<br>1+ seniors, no<br>Single man or woman with:<br>children<br>1+ children (at least one is 0-5), no seniors<br>1+ seniors, 1+<br>1+ children (all 6-14), no seniors<br>children<br>1+ seniors, no children<br>1+ seniors, 1+ children<br><!-- End of picture text --> <!-- Start of picture text --> man<br> woman<br>dependents<br>Married adults, other<br>2+ unmarried adults,<br>no dependents<br><!-- End of picture text --> <!-- Start of picture text --> Married couple<br>with:<br>1+ children (at<br>least one is 0-5),<br>no seniors<br>1+ children (all<br>6-14), no seniors<br>1+ seniors, no<br>children<br>1+ seniors, 1+<br>children<br><!-- End of picture text --> <!-- Start of picture text --> Single man or woman with:<br>1+ children (at least one is 0-5), no seniors<br>1+ children (all 6-14), no seniors<br>1+ seniors, no children<br>1+ seniors, 1+ children<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

> 33. The _others_ category makes up 11 percent of the population.

23
# **Tables**

Table 1: Unadjusted sample means by child gender, firstborn sex and pre-post ultrasound

||Firstb|orn boy f|amilies|Firstb|orn girl f|amilies|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Male|Female|(2)-(1)|Male|Female|(5)-(4)|
|**A. Under-5 mortality (%)**|||||||
|Pre-ultrasound: 1973-1984|12.90|12.82|-0.08|10.49|13.36|2.87|
|N|17,769|16,520||17,252|15,908||
|Early difusion period: 1985-1994|10.04|10.26|0.22|9.04|10.63|1.58|
|N|30,066|28,333||31,382|28,412||
|Late difusion period: 1995-2005|7.71|7.80|0.09|7.14|8.22|1.09|
|N|10,314|9,709||11,543|9,921||
|**B. Received at least one vaccine**|||||||
|Pre-ultrasound: 1973-1984|0.707|0.708|0.001|0.720|0.679|-0.041|
|N|1,861|1,624||1,044|916||
|Early difusion period: 1985-1994|0.859|0.858|-0.001|0.898|0.886|-0.012|
|N|1,882|1,723||1,438|1,298||
|Late difusion period: 1995-1999|0.924|0.910|-0.014|0.938|0.942|0.004|
|N|792|754||696|568||
|**C. Months breastfed**|||||||
|Pre-ultrasound: 1973-1984|19.05|18.63|-0.42|19.15|18.90|-0.25|
|N|1,537|1,124||619|401||
|Early difusion period: 1985-1994|18.32|18.35|0.03|18.50|18.10|-0.40|
|N|2,197|1,758||1,105|734||
|Late difusion period: 1995-1999|12.78|12.52|-0.26|13.71|12.95|-0.76|
|N|1,453|1,302||673|532||
|**D. Medicines/special food during illness last year (Rs.)**|||||||
|Pre-ultrasound: 1973-1984|153.44|174.34|20.90|265.96|165.22|-100.74|
|N|645|399||396|245||
|Early difusion period: 1985-1994|162.15|149.74|-12.41|159.56|143.26|-16.30|
|N|1,816|1,651||1,407|1,250||
|Late difusion period: 1995-1999|191.71|162.80|-28.91|214.36|148.81|-65.55|
|N|769|733||683|553||
|**E. Total number of children**|||||||
|Pre-ultrasound: 1973-1984||2.91|||3.12||
|N||14,095|||10,690||
|Early difusion period: 1985-1994||2.13|||2.22||
|N||25,857|||22,169||
|Late difusion period: 1995-2005||1.85|||1.96||
|N||23,480|||22,372||

NOTES: This table shows pre- versus post-ultrasound trends in unadjusted (raw) outcomes among secondand higher-order children, by firstborn sex and child gender. Panel E shows the total number of children per mother for the sample of mothers whose first and last birth took place within each of the three periods. Data: NFHS in Panels A and E. REDS in Panels B, C, D.

39
Mongolia is a small, transitional country with a population of slightly above three million living in a landlocked area located between Russia and China. Ethnic Mongols account for about 95 percent of the population. Mongolia is one of the least densely populated countries in the world, with almost half its population living in the city of Ulaanbaatar, the country’s capital. Following the dismantling of the USSR in the early 1990s, Mongolia’s economy changed dramatically from a centrally planned system to a market-based one, and Soviet assistance, which had accounted for almost one-third of Mongolia’s GDP, disappeared almost overnight. Mongolia is resource-rich, with large deposits of copper, gold, coal, and uranium. Its mining sector accounts for almost one-fifth of GDP and 40 percent of exports, although it accounts for only 4.2 percent of employment. Mining has transformed Mongolia in a few decades from a traditional agricultural- and herding-based economy to a resource-based economy (World Bank Enterprise Survey, 2013). Today, Mongolia has a per capita GDP of USD $13,000 (PPP), which makes its economy comparable to that of South Africa or Sri Lanka. Agriculture nonetheless remains the largest employer in the country and still absorbs 30 percent of the labor force (down from 46 percent in 2003), followed by services (17 percent), public administration (15 percent), and manufacturing (10 percent) (World Bank Enterprise Survey, 2013).

Structural economic changes have been accompanied by two important demographic shifts that have imposed additional pressure on labor markets. Mongolia’s population is relatively young: the median age is 26 years, while the youth share of the working-age population is close to 55 percent. Likewise, the clearly declining trend of employment in herding and agricultural is mirrored by strong internal migration from provinces to the city of Ulaanbaatar. Indeed, since the late 1990s, population growth in Ulaanbaatar has averaged 5.8 percent, twice the country’s population growth rate. In 2013, 20 percent of the youth in Ulaanbaatar were new migrants who moved to the city in the past five years (Shatz et al., 6
**Table 2**

|**M**<br>|**arket Concentr**<br>|**ation in th**<br>|**e SPP (12/96)**<br>**Fund**<br>||
|---|---|---|---|---|
|**AFP**|**Affiliates**|**%**|**(million soles)**|**%**|
|**Profuturo**|417,634|26.9|509.7|20.7|
|**Horizonte**|390,925|25.2|585.9|23.7|
|**Integra**|377,118|24.3|735.1|29.8|
|**Union**|284,084|18.3|479.9|19.4|
|**Nueva Vida**|80,878|5.3|157.2|6.4|
|**Total**|1,550,639|100.0|2,467.8|100.0|

Source: SAFP, December 1996 The evolution of the sales force in the SPP matches the evolution of affiliation discussed earlier. At the outset of the new system, the AFPs had a salesforce of more than 3,000 agents which rapidly increased to almost 6,000 in December 1993. In the initial phase, AFPs competed intensely for affiliates paying less attention to actual contributions and income levels of their affiliates. Since the number of workers switching over to the new system was considerably lower than the AFPs had originally projected, marketing efforts were increased to attract more affiliates. The advertisements, however, were often confusing and unsuccessful in convincing workers of the benefits of the SPP.

As it became evident that affiliation rates were declining and the AFPs were running large operational deficits, the number of agents was reduced. As shown in Graph 2, the reduction of sales personnel was gradual at first, as marketing strategies were revised, and then more rapid as the AFPs started closing branches and drastically cutting their operational costs. In December 1994, there were less than 800 agents employed by the whole system. The number of AFP branch offices was reduced from 139 in December 1993 to 71 in December 1994.

<!-- Start of picture text --> Evolution of Sales Personnel<br>7,000<br>6,500<br>@. 2 03,500 /<br>1,500<br>1,000<br>500<br>J A 0 D F A J A 0 D F A J A 0 D F A J<br>1993 1994 1995 1996<br><!-- End of picture text --> Source: SAFP 13
**Table 2-3: Development of total EU trade with Morocco (1995-2007, in million €)**

||Imports by the<br>EU|Exports to<br>Morocco|Total trade|Annual change|Balance|
|---|---|---|---|---|---|
|1995|4017|4728|8745||711|
|1996|4233|4699|8932|2.1%|467|
|1997|4750|5329|10079|12.8%|579|
|1998|5334|6603|11937|18.4%|1269|
|1999|5553|6627|12180|2.0%|1075|
|2000|6015|7736|13751|12.9%|1720|
|2001|6241|7476|13717|-0.2%|1236|
|2002|6295|7697|13993|2.0%|1402|
|2003|6228|8072|14300|2.2%|1843|
|2004|6472|8754|15226|6.5%|2282|
|2005|8971|11578|20549|35.0%|2608|
|2006|7031|10160|17191|-16.3%|3130|
|2007|7614|11837|19451|13.1%|4222|

_Source: Comext Database_

**Table 2-4: Development of trade in food and live animals (SITC 1) of the EU-27 with Morocco (2000-2007, in million €)**

||Imports by the<br>EU|Exports from<br>Morocco|Total trade|Annual change|Balance|
|---|---|---|---|---|---|
|1995|876|482|1358||-394|
|1996|994|338|1332|-1.9%|-655|
|1997|975|303|1278|-4.0%|-672|
|1998|957|395|1352|5.8%|-562|
|1999|1079|416|1495|10.6%|-663|
|2000|1152|621|1773|18.6%|-531|
|2001|1236|519|1755|-1.1%|-717|
|2002|1400|494|1894|7.9%|-907|
|2003|1393|492|1885|-0.5%|-901|
|2004|1418|490|1909|1.3%|-928|
|2005|1628|562|2190|14.7%|-1067|
|2006|1718|520|2238|2.2%|-1197|
|2007|1772|821|2593|15.9%|-951|

_Source: Comext Database_ The experience with the EU-EUROMED agreement in agriculture, as exemplified by Morocco, illustrates that quantitative import restrictions (i.e. granting limited tariff rate quotas) are the EU’s preferred instrument for integrating agriculture in an (otherwise free) trade arrangement. The effect on trade can be positive, provided the partner country is able to 15
2011. Likewise, the HCES surveys sample consists of 17,336 in the 2000 survey, 21,724 in the 2005 survey, and 28,032 in the 2011 survey.

**Table 1.  Deprivation indicators, definitions and their use for urban and rural analysis**

|Deprivation<br>Indicator|Definition: A household is deprived when...|Urban|Rural|
|---|---|---|---|
|1_. Education, Health,_<br>1.1 Education of<br>school-aged children<br>1.2 Health facility<br>quality|_Water and Sanitation_<br>...at least one child, age 7-15, in the household is not currently<br>attending school.<br>...the household reported dissatisfaction with at least one health<br>facility visit, or did not use a health facility due to cost, distance,<br>quality, or other reasons.|√<br>√|√<br>√|
|1.3 Health facility<br>access<br>1.4 Drinking water<br>1.5 Sanitation|...the household is located more than 5 km away from the nearest<br>health facility (clinic, health station, hospital, or health post).<br>...a safe drinking water source—piped water, protected water source,<br>or rainwater—is not used by the household.<br>...an improved toilet—private flush toilet or private pit latrine—is<br>not used by the household. (e.g. A household that uses an improved<br>toilet facility, but it is shared, is deprived.)|√|√<br>√<br>√|
|_2. Aspirations_<br>2.1 Source of<br>information|...the household does not own a TV, radio, or phone.|√|√|
|_3. Wellbeing of Girls a_<br>3.1 Education of<br>female school-aged<br>children<br>3.2 Institutional birth<br>3.3 Female<br>circumcision|_nd Women_<br>...at least one girl child, age 7-15, in the household is not currently<br>attending school.<br>...at least one child, age 0-4, in the household was not born in a<br>health facility.<br>...at least one girl child, age 0-14, in the household has been (or will<br>be)circumcised.|√<br>√<br>√|√<br>√|
|_4. Monetary_<br>_Dimensions_<br>4.1 Assets<br>4.2 Below poverty<br>line|...none of these assets are owned by the household: fridge, phone,<br>radio, TV, bicycle, jewelry, or vehicle.<br>...the household’s real total consumption expenditure per adult is<br>lower than thepovertyline (3781 Birr).|√<br>√|√<br>√|

Note: The columns Urban and Rural specify which indicators are used in the overlap analysis for urban areas and rural areas. Access to a health facility and access to a safe drinking water source are present for nearly all urban households, so they are not considered in the overlap analysis. Institutional birth is not considered in the overlap analysis for rural households because almost all children in rural areas aged 0-4 years were not born in a health facility.

5
Ortiz-Juarez (2014) and Ferreira et al. (2013) have done for Latin America and the Caribbean. López-Calva and Ortiz-Juarez (2014) set the lower bound of middle-class income in Latin America as the maximum income that ensures economic stability. Households were defined as economically stable and invulnerable only if the probability of their falling below the national poverty line within five years was 10 percent or less. The income level associated to the 10 percent probability was defined as the lower bound of the middle class.

Using longitudinal data in their study of Latin America and the Caribbean, Ferreira et al. (2013) established as the lower threshold of the middle class the value of US$10 a day in 2005 PPP terms. This estimation was corroborated by a subjective approach in which respondents were asked to report their social class; the majority of respondents with that income identified themselves as middle class rather than poor. The upper bound was established as $50 (2005 PPP international dollars) as a reasonable level that excludes the richest 2% of Latin American households.<sup>5</sup> Though the US$50 threshold is fairly arbitrary, it has since gained ‘authority’ by being widely adopted.

Although these fixed thresholds are in PPP terms, only to a limited extent can they be used to define the middle class in Kazakhstan because they have been attributed to countries at different stages of social and economic development. Country-specific thresholds are therefore preferable. However, in Kazakhstan the method proposed by López-Calva and Ortiz-Juarez (2014) cannot be applied directly for two reasons: (1) it would yield an extremely low probability of falling into poverty because of the very low poverty rate in Kazakhstan, and (2) household longitudinal data for at least four years are not available. In the next section, therefore, we explore a new methodology for estimating country-specific thresholds for identifying the middle class in Kazakhstan.

# **3.2 Estimation of the thresholds**

Our approach to estimating absolute thresholds for identifying the Kazakhstan middle class exploits individual perception of social status and relates self-declared social class to income. We follow a vulnerability-poverty approach by identifying a

> 5 The upper threshold lies in the 92nd percentile for Chile, the 97th for Mexico and the 98th for Peru.

7
The details of the original sources of the variables can be found in the _Basic Information Document_ (NBS and World Bank 2016).

**Table A.1. Summary statistics by zone, geographical controls (three-wave panel)**

||Pooled<br>Mean|sd|North<br>East<br>Mean|sd|North<br>Central<br>Mean|sd|South<br>South<br>Mean|sd|
|---|---|---|---|---|---|---|---|---|
|Annual mean temperature|262.3|11.5|262.5|8.98|259.8|17.4|264.3|3.26|
|(°C * 10)|||||||||
|Annual precipitation (mm)|1657.0|751.3|834.4|265.2|1324.2|130.7|2464.4|409.9|
|Slope (percent)|2.78|2.87|2.82|3.40|3.66|3.40|1.99|1.40|
|Elevation (m)|264.1|294.5|376.1|154.1|425.5|379.1|55.1|66.6|
|Potential wetness index|15.5|5.62|14.7|3.49|13.4|1.44|17.7|7.76|
|Terrain roughness|2.87|2.56|3.01|2.03|4.44|3.11|1.45|1.16|
|Average 12-month total|1375.9|428.8|929.4|336.5|1246.4|154.5|1770.4|261.7|
|rainfall (mm) for Jan.–Dec.|||||||||
|Observations|2125||516||825||784||

_Note:_ Description of the variables is given in table A.2. sd = standard deviation.

iii
recently. Motivated by such concerns, a common type of intervention provides financial literacy or financial education training to migrant workers and their families remaining behind in the home country. Financial literacy programs are commonly conducted as part of pre-departure orientation training programs (discussed in the previous section), such as the Pre-Departure Orientation Seminar (PDOS) mandated by the Philippine government for labor migrants, or the migration training programs run by the International Organization for Migration.

Important evidence on the impact of providing financial education to migrant workers and their families is provided by Doi et al. (2014), who implemented a randomized controlled trial among Indonesian women about to depart for overseas work as household servants (maids), and these women’s families. Study participants were randomly assigned to either a control group (that received no training) or to one of three treatment groups. In the three treatment groups, financial literacy training was provided prior to the migrant’s departure for overseas, for either: 1) the migrant alone, 2) a left-behind family member alone, or 3) both the migrant and the family member. The training was conducted over sessions lasting either 1 day (for the migrants) or 2 days (for family members), and covered financial planning and management, savings, debt management, sending and receiving remittances, and migrant insurance. The study examined impacts on families remaining behind in Indonesia, finding that each type of treatment did increase financial knowledge (for example, on knowledge of financial terminology). In addition, training both the migrant and family led to increases in savings in the origin household, but the other two treatments (migrant only and family only) did not have a similar savings impact. None of the treatments had substantial impacts on remittances received. The study highlights the complementarities from training both migrants and family members.

Financial literacy training also occurs frequently at destination, targeting the migrant. Seshan and Yang (2014) examine such a program for married male migrant workers in Doha, Qatar who were from Kerala, India and whose wives remained behind in India. Participants were randomly assigned to either a control group or a treatment group. The treatment group was invited to attend a one-time motivational session on personal financial management that stressed the importance of savings and of making joint financial decisions with spouses remaining behind in India. The session lasted 3 hours and was led by a popular motivational speaker. Impacts were measured via follow-up surveys of both migrants in Kerala and their wives in India. The treatment led both 9