rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> (A) Outcome trends (B) Predicted probability<br>60 6<br>5 40 i<br>In i 4<br>: i,<br>* 20<br>10 2<br>1 2 3 4 5 6 -20 -15 -10 5 0 5<br>‘Survey round Mobility change from the baseline (%)<br>—-=- <Skm —— §-10km -—@- In_dist=8 —@- = In_dist=9<br>— 10-15km —— = = 15kmor farther —@ = In_dist=10<br>(C) By sex (D) By skills<br>66<br>./ 5<br>ig iz<br>gg<br>E3E3<br>22<br>-20 -15 -10 5 0 5 -20 -15 -10 5 0 5<br>Mobility change from the baseline (2%) Mobility change from the baseline (%)<br>-—@- = In_dist=8, female=0 —4&- In_dist=8, female=1 -@- = In_dist=8, High-skilled —4- In_dist=8, Low-skilled<br>—@®— Indist=10, female=0 —4— Indist=10, female=1 —@— = In_dist=10, High-skilled —4— In_dist=10, Low-skilled<br><!-- End of picture text -->
Table (12) provides descriptive statistics from the administrative records digitized in the L2CU PSUs.

# **Table 12: Descriptive Statistics of Random Sample of 200 Surveyed Mahallas**

||Total|Urban|Not Urban|Min|Max|
|---|---|---|---|---|---|
|Mahalla Population|4381|5318|4106|653|17767|
|Mahalla Number of Families|1150|1628|1010|148|4749|
|Private Houses|684|586|713|0|3219|
|Multi-Story Family Houses|6|20|2|0|104|
|Apartments (in Buildings)|210|812|31|0|2965|
|Registered Citizens|4005|4509|3852|32|17767|
|Registered but Non-Resident|132|240|98|0|5791|
|Permanent Registration in Tashkent|31|114|5|0|932|
|Temporary Registration in Tashkent|26|82|9|0|1500|
|Resident without Registration|40|145|6|0|1238|
|Resident Foreign/Stateless Citizens|9|21|6|0|123|
|Residents Currently Traveling|122|145|115|0|638|
|Empty Dwelling with Owner|6|14|4|0|156|
|Empty Dwelling without Owner|0|1|0|0|23|
|Rental Homes|22|90|1|0|1250|
|Illegal Rental Homes|8|33|1|0|1000|
|Residents in Rental Housing|53|206|4|0|1810|
|Labor Migrants Abroad|120|137|115|1|554|

_Notes: The figures in the table provide average values weighted by population by mahalla, the smallest administrative unit in Uzbekistan. Author’s Calculations._ 58
- 22 -

TABLE 4: **Correlation of Alternative Definitions of Poverty vith the Adjusted Per Capita Consumption Definition: CUte** d'Ivoire

||Percent<br>Accura|age of Popu<br>tely Identi|lation<br>fied|_2_<br>x Statistic|
|---|---|---|---|---|
|Definition|Poor|Non-Poor|Total|(d.f.=1)|
|Urban Areas|||||
|Per capita income|16.80|56.85|73.65|105.2754*|
|Household consumption|17.67|57.81,|75.51|102.2547*|
|Per capita consumption|26.08|66.12|92.20|413.9545*|
|Per capita food consumption|22.64|62.67|85.31|262.0501*|
|Food ratio|14.23|54.24|68.47|30.8790'|
|Height for age|8.70|49.47|58.17|0.2791|
|Weight for height|8.95|49.01|57.96|0.4649|
|Per capita floor area|13.26|53.48|63.74|32.9857*|
|Adult school attainment|13.41|53.69|67.10|31.9716*|
|Rural Areas|||||
|Per capita income|17.70|57.60|75.30|166.7868*|
|Household consumption|19.93|59.90|79.83|209.0987*|
|Per capita consumption|26.43|66.38|92.81|595.2563*|
|Per capita food consumption|22.84|62.85|85.69|359.0950*|
|Food ratio|10.19|50.17|60.36|3.4448|
|Height-for-age|10.82|51.21|62.03|1.3537|
|Weight-for-height|9.56|50.07|59.63|0.0833|
|Per capita floor area|7.57|47.51|55.28|1.5571|
|Adult schooling|11.89|51.71|63.60|9.9035*|
|Agricultural land per capita|11.94|52.00|63.94|17.5915*|

Note: One asterisk denotes that the hypothesis of no correlation is rejected by the chi-square test at the 1 percent level.
The paper is structured as follows. Section 1 presents a brief description of the data, and Section 2 presents the model. Section 3 discusses the national-level results from the model using regression analysis to test the relationship between different household characteristics and welfare. Section 4 carries out the decompositions at the national level to analyze the relative importance of characteristics and returns to characteristics on welfare. Section 5 discusses the extension of the model to allow for comparisons within and across regions. Section 6 concludes.

# 1. Data Source

This analysis uses data from the Living Conditions Survey ( _Encuesta de Condiciones de Vida_ —ECV) for 2005-2006. The ECV is a micro-level dataset collected by Ecuador’s National Statistics and Census Institute ( _Instituto Nacional de Estadística y Censos_ —INEC). The ECV collects information about individual characteristics like gender, age, education, main activity, income, household’s attributes, and detailed information on the expenses of the household. The survey is representative at the national level and by urban and rural areas. Other representative areas include Quito, Guayaquil, Cuenca and Machala. The survey design was stratified, multi-staged and clustered. At the first and second stages the units were selected with probability proportional to the number of households on them, while at the third stage households were selected with equal probability. Weights are then necessary to get suitable estimations. The final sample size was 13,581 households, 8,065 in urban area and 5,516 in rural areas (Table 1).

**<u>Table 1. Households by Region</u>**

|Region|Urban|Rural|Total|
|---|---|---|---|
|Sierra|3,815|3,451|7,266|
|Costa|3,770|1,537|5,307|
|Oriente|480|528|1,008|
|Total|8,065|5,516|13,581|

Source: Own calculations based on ECV.

Nation-wide, the ECV indicates that urban households have an average consumption of one and a half times the poverty line, while the mean consumption of rural households is almost equal to the poverty line.

# 2. Model

A linear model is used to estimate the contribution of location differences in living standards after controlling for non-geographic attributes. The dependent variable is the logarithm of the welfare ratio, which is a proxy for living standards, and the independent variables are household attributes. These are two different regression equations, one for each area (urban and rural). The model is as follows:

> where _Wi_ denotes the welfare ratio, defined as the per capita consumption deflated or divided by a national poverty line at the household _i_ , **X** _i_ is a vector of non-geographic variables for the household _i_ , **G** _i_ is a vector of geographical dummy variables (provinces), and _U_ and _R_ denote the urban and rural

3
# **1. Introduction**

I spent most of my childhood in the small town of Huntly, New Zealand. I remember clearly that there used to be a sign at the entrance of the town that said something like “Welcome to Huntly, population 6,238 and growing,”<sup>1</sup> and that after a while, they removed the “and growing” from the sign. The town was surrounded by rural farmland (there was a farm over our back fence). The main industry in town was a coal-mine and a power station that used the coal to generate thermal energy, with the two big smoking chimneys from the power plant the largest landmarks in town. Fast forward to today, and the population is still only 8,030 (June 2020 estimate), the median personal income of NZ$24,500 (US$17,253) is 40 percent less than the NZ$34,400 (US$24,225)<sup>2</sup> in the Auckland region to which I later moved, and the local high school (where my father taught) had its enrollment fall from over 600 students in the 1980s to only 204 students in 2020. In 2019, Huntly was voted New Zealand’s worst town in one online poll.<sup>3</sup> From this description it would seem obvious that most people would want to move from such a place. Yet when the time came to move to Auckland, I cried. There were many things I liked about living there – playing soccer on a foggy field by the lake, running about on the farm, my friends, and the familiarity of our house and backyard. Leaving all these behind to start afresh in a new place seemed scary and uncertain. But move we did, and after going to high school and doing my undergraduate degree in Auckland, I was instead firmly attached to this new home. But then a new opportunity was open to me – the chance to move to America for graduate school. Not only would moving to a new country offer educational opportunities far exceeding those I could get in Auckland, but research I later did with John Gibson found that New Zealanders who were at the top of their academic classes at the end of high school gain an average of US$1,000 a week, or around $50,000 a year, by migrating (Gibson and McKenzie, 2011). Again, deciding whether or not to move would seem an easy decision by economic cost-benefit analysis. Yet as I hopped on that plane, moving once again involved substantial fears and tears, including this time moving

> 1 The 1981 New Zealand census records a population estimate of 6,100 for Huntly:

> [https://www3.stats.govt.nz/New_Zealand_Official_Yearbooks/1981/NZOYB_1981.html#idchapter_1_9605]. 2 Data on incomes from the 2018 Census https://www.stats.govt.nz/tools/2018-census-place-summaries/huntly- <u>east#work-income-and-unpaid-activities. Exchange rate of US$1 = NZ$1.42 used.</u>

> 3 https://www.nzherald.co.nz/travel/huntly-named-worst-town-in-new-zealand-by-publicvote/A4ROMUR4LDUJXHCMGW4S7YARRE/

2
# **Table 4: Financial Attitudes**

||(1)|(2)|(3)|
|---|---|---|---|
||Financial Attitudes<br>Composite Score|Q1: Saving for<br>Child's Education|Q2: Take Out<br>Smaller Loan|
|Autonomous Trainer Selection (T1)|0.018**|0.022<br>|0.014|
||(0.009)|(0.014)<br>|(0.009)|
|Fixed Trainer Selection (T2)|0.011|0.012<br>|0.010|
||(0.009)|(0.014)<br>|(0.010)|
|R-squared|0.749|0.469<br>|0.754|
|N|7885|7885<br>|7885|
|Dependent Variable Mean in Control Group|<br>0.443|0.446<br>|0.440|
|F-test(p-value): T1 = T2|0.421|0.489<br>|0.598|

This table presents analysis for financial attitudes using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
<!-- Start of picture text --> Figure 4.2: Income Source for Households per Capita, 2001 (percent)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>Self-emp Salary<br>20% Transfer Property<br>Self-cons Barter<br>10%<br>Other<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Source: Own calculations based on HLCS 2001.<br>Proportion of Income<br><!-- End of picture text --> Self-employment income, wages, and transfers are crucial to reducing poverty in Haiti. Self-employment is the most important income source for all income levels, although it accounts for more of the total income of the poorest 10 percent of the population (46.7 percent of their total income) than of the richest 10 percent (30.9 percent of their income). Private transfers, mostly remittances, are generally the second most important income source, accounting for 26 and 30 percent of the total income of the poorest 10 percent and the richest 10 percent, respectively. Salaries are relatively unimportant for deciles 1-3: less than 6 percent of this group’s total income is from wage labor. For the upper deciles, however, salaries are a significant part of total income: 19.3 and 30.7 percent for the top two deciles, respectively.

Table 4.4: Income Source for Households per Capita 2001 (percent)

|||Rural<br>|Population<br>|||Whole<br>|Population<br>||
|---|---|---|---|---|---|---|---|---|
|Quintile|Farm|Off-farm|Remittances|Other|Farm|Off-farm|Remittances|Other|
|1(poorest)|39.0|33.6|14.4|13.1|37.2|34.2|15.4|13.2|
|2|49.2|26.6|14.4|9.9|41.2|30.5|17.7|10.5|
|3|51.6|25.8|14.7|7.9|43.2|31.1|17.6|8.1|
|4|51.4|27.0|15.5|6.1|36.6|37.1|18.7|7.7|
|5(richest)|37.3|34.4|20.4|7.5|10.9|52.1|27.8|9.3|

_Note:_ ‘Farm’ includes own consumption of crops and meat and barter. ‘Other’ contains other transfers than remittances, <u>property income and 'other'. ‘Off-farm’ contains all kinds of labor income and sales of products.</u> _Source:_ Own calculations based on HLCS 2001.

Farm labor is still the most important income source for Haiti’s rural population. Both the poor and nonpoor in rural areas receive the largest share of their total income from activities such as farming and agricultural labor (Table 4.4). Rural dwellers also 20
**<u>Appendix table 8: Lease price regression for agricultural land for personal use purchased by individuals</u>**

||<br>**Alltran**|<br>**sactions**|<br>**With contr**|<br>**act duration **|
|---|---|---|---|---|
|Area (ha)|-0.033**<br>(0.015)|-0.039**<br>(0.015)|-0.029*<br>(0.016)|-0.033**<br>(0.016)|
|Area^2|<br>-0.040***|<br>-0.038***|<br>-0.045***|<br>-0.043***|
||(0.012)|(0.012)|(0.013)|(0.013)|
|Moratorium lifted|<br>0.069**|<br>0.068**|<br>0.019|<br>0.018|
||(0.028)<br>|(0.028)<br>|(0.033)<br>|(0.033)<br>|
|No of transactions in village|0.028***|0.027***|0.022**|0.023**|
||(0.009)|(0.009)|(0.009)|(0.009)|
|Parcel part of a field||0.051**<br>||0.058**<br>|
|Field area (ha)||(0.025)<br>0.004<br>||(0.026)<br>-0.004<br>|
|^||(0.008)<br>||(0.008)<br>|
|Field area2||-0.001||-0.000|
|||(0.002)<br>||(0.002)<br>|
|Unmapped area share in field||-0.079||-0.057|
|||(0.074)||(0.078)|
|Lease term 14-20 years|||-0.055*|<br>-0.054*|
||||(0.032)|(0.032)|
|Lease term > 20 years|||-0.268***|-0.268***|
||||(0.029)|(0.029)|
|Dist. to tertiary road (km)|-0.012|-0.013|<br>-0.013|<br>-0.012|
||(0.008)|(0.008)|(0.008)|(0.008)|
|Dist. to secondary road (km)|0.002|0.002|0.007|0.007|
||(0.009)|(0.009)|(0.009)|(0.009)|
|Dist. to primary road (km)|-0.008|-0.008|-0.015|-0.015|
||(0.010)|(0.010)|(0.011)|(0.011)|
|Dit t t k|<br>0031**|<br>0031**|<br>0031**|<br>0031**|
|s. o moorway (m)|-.<br>(0.015)|-.<br>(0.015)|-.<br>(0.015)|-.<br>(0.015)|
|Dist. to railway (km)|<br>-0.010|<br>-0.010|<br>-0.005|<br>-0.005|
||(0.010)|(0.010)|(0.011)|(0.011)|
|Dist. to port (km)|<br>-0.546***|<br>-0.548***|<br>-0.544***|<br>-0.545***|
||(0.167)|(0.167)|(0.176)|(0.176)|
|Dist. to elevator (km)|-0.066***|-0.065***|-0.066***|-0.065***|
||(0.017)|(0.017)|(0.018)|(0.018)|
|Dist. to nearest city (km)|-0.074*<br>|-0.073*<br>|-0.065<br>|-0.065<br>|
||(0.041)|(0.041)|(0.042)|(0.042)|
|Share built-up area|<br>1.608***|<br>1.626***|<br>1.218***|<br>1.233***|
||(0228)|(0228)|(0244)|(0244)|
|Share cultivated area|.<br>0.657***|.<br>0.640***|.<br>0.636***|.<br>0.623***|
||(0.142)|(0.143)|(0.143)|(0.143)|
|Share forested area|0.684***|0.698***|0.652***|0.667***|
||(0.172)|(0.172)|(0.174)|(0.175)|
|Share grassland|0.526***<br>|0.517***<br>|0.492***<br>|0.482***<br>|
||(0.143)|(0.143)|(0.144)|(0.144)|
|Soil bulk density (g/cm^3)|<br>-11.583***|<br>-11.259***|<br>-12.867***|<br>-12.641***|
||(4.273)<br>|(4.275)<br>|(4.469)<br>|(4.471)<br>|
|Density^2|18.976**<br>(8243)|18.453**<br>(8246)|20.749**<br>(8615)|20.382**<br>(8619)|
|Total nitrogen (g/kg)|.<br>0.707|.<br>0.679|.<br>0.834*|.<br>0.809*|
||(0.455)|(0.455)|(0.471)|(0.471)|
|Nitroen^2|-0268*|-0257*|-0310**|-0300*|
|g|.<br>(0.151)|.<br>(0.151)|.<br>(0.157)|.<br>(0.157)|
|Soil pH (pH)|<br>25.064*|<br>24.902*|<br>36.596***|<br>35.850***|
||(13.273)|(13.281)|(13.707)|(13.718)|
|pH^2|<br>-6.232*|<br>-6.195*|<br>-9.300**|<br>-9.104**|
||(3518)|(3520)|(3634)|(3637)|
|Soil organic carbon stock (g/kg)|.<br>-0.927|.<br>-0.958|.<br>0.523|.<br>0.490|
||(1.511)|(1.511)|(1.593)|(1.594)|
|Organic carbon^2|0264|0272|-0134|-0124|
||.<br>(0.428)|.<br>(0.428)|.<br>(0.452)|.<br>(0.452)|

39
Table 3: Foreign Direct Investment by Sector

||**isic 1-digit**|**2006**|**2011**|
|---|---|---|---|
|**1**|Manufacturing,<br>Mining,<br>Quarrying|4.49|1.62|
|**2**|Manufacturing food paper<br>chemicals metal|1.05|0.60|
|**3**|Manufacturing ofce electri-<br>cal machinery transport fur-<br>niture|2.31|0.42|
|**4**|Electricity, gas and water<br>supply Construction|0.68|0.34|
|**5**|Wholesale and retail trade; re-<br>pair of motor vehicles; motorcy-<br>cles and personal and household<br>goods; Hotels and Restaurants|0.38|0.16|
|**6**|Transport,<br>storage<br>and<br>communications, Financial<br>intermediation|3.43|2.01|
|**7**|Real<br>estate,<br>renting<br>and<br>business<br>activities<br>Public<br>administration and defense;<br>compulsory social security|0.82|0.17|
|**8**|Education Health and social<br>work|0.27|0.09|
|**9**|Other<br>community,<br>social<br>and<br>personal service activities, Ac-<br>tivities of private households as<br>employers and undiferentiated<br>production activities of private<br>households, Extraterritorial or-<br>ganizations and bodies|0.37|0.26|
||Average|0.78|0.32|

11
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 18. Funding gaps by sector**

<u>$ millions</u>

||**ICT**|**Irrigation**|**Power**|**Transport**|**WSS**|**Total**|
|---|---|---|---|---|---|---|
|Spending needs|(173)|(91)|(454)|(328)|(434)|(1,480)|
|Existing spending|173|3|157|220|127|681|
|Reallocation potential within sectors|0|0|100|53|0|153|
|Efficiency gains|0|0|487|43|57|586|
|Funding gap|**0**|**(88)**|**0**|**(12)**|**(250)**|**(350)**|
|Reallocation potential across sectors|96|0|0|0|0|96|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Reported totals assume complete fungibility across sectors. Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that savings from elimination of overspending would be applied in other infrastructure sectors. WSS = water supply and sanitation; ICT = information and communication technology.

# What else can be done?

The funding gap can be addressed only by raising additional finance or, alternatively, by adopting lower-cost technologies or less-ambitious targets for infrastructure development.

In the case of Cameroon, there may be realistic prospects for increasing the flow of resources to all infrastructure sectors form private players and public sources.

Cameroon has already attracted a significant amount of private finance into infrastructure. In the early 2000s, Cameroon captured private investment commitments worth about 1 percent of its GDP, not only in the ICT sector but also in power. Many other African countries have done far worse in this area (figure 35). On the other hand, some—for example Nigeria, Uganda, Kenya, and Senegal—have done much better, attracting more that 2 percent of GDP in private finance for infrastructure.

## **Figure 35. Private sector participation**

Average per year between 2002 and 2007 <!-- Start of picture text --> 3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Water Transport Power ICT<br>Percentage of GDP<br>Guinea-Bissau Senegal Kenya Uganda Nigeria Liberia Sierra Leone Mozambique Tanzania Rwanda Ghana Zambia Burkina Faso Mali Benin Seychelles South Africa Cameroon Niger Lesotho Gabon Madagascar Guinea Malawi Mauritania Cote d'Ivoire Zimbabwe Angola Burundi Congo, Rep. Mauritius Chad Botswana Togo Swaziland Central African ... Namibia<br>DRC Congo, Dem. ... Equatorial Guinea<br><!-- End of picture text --> _Source:_ AICD calculations. _Note:_ GDP = gross domestic product; ICT = information and communications technology.

46
The success of NGOs in health development in rural Bangladesh is attributed to decades of experience, characterized by continuous learning and adaptation of interventions. Engagement by NGOs in the provision of urban primary health services is much more recent, but a similar process would be needed. Notwithstanding, the health development record in rural Bangladesh is mixed. The government and NGOs have been especially successful in promoting family planning through contraceptives, infectious disease prevention through vaccinations, and diarrheal treatment through oral rehydration, whereas they have been less successful in other areas of maternal and child health, and in addressing maternal and child undernutrition (Arifeen et al. 2013). Hence, there is an even greater need for iterative learning from testing interventions for improving the quality and increasing the use of health services—irrespective of the provider—by slum residents and the urban poor.

While policymakers should continue to expand access to, and improve the quality of, preventive health technologies and services, international evidence points to insufficient demand for preventive health among poor households. Indeed, household demand that is sustained over a long period may be needed for investments in preventive health to translate into significant health benefits (Brown and Clasen 2012). Reviewing experimental evaluations on the effects of interventions on health in developing countries, Kremer and Glennerster (2012) indicate that the patterns in the evidence suggest that household investment in preventive health is highly sensitive to price, convenience, and salience, and that these patterns are consistent with presentbiased preferences and limited attention among households. The explanations point to the need for behavioral interventions to influence household choices in relation to factors that we find to be associated with child nutrition status. Dupas and Miguel (2016) also review experimental evaluations on the effects of interventions on health in developing countries. They argue that other patterns in the collective evidence suggest that households may find it difficult to ascertain the benefits of investing in preventive health in settings where illness is highly prevalent and where the sources of illness are many and diverse, or that households may perceive low benefits to investing in preventive health.

Findings in several studies of health and nutrition in urban slum settlements in Bangladesh and elsewhere are consistent with one or both of the above sets of explanations. For example, based on focus group discussions in selected slum settlements in Dhaka, Goudet et al. (2011) find that information does not appear to be a major constraint: community health workers 35
<!-- Start of picture text --> Figure 3b. Poverty headcount ratio<br>Elderly living alone vs. elderly in artificially split households<br>Elderly in EHH1<br>Elderly in split EHH2<br>Poverty line = $2.5 a day, OECD equivalency scale adjusted household size<br>The poverty line in both cases is the same as that in the original population without the artificial splits<br>Argentina Bolivia Brazil ChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHonduras MexicoNicaraguaPanamaParaguay PeruUruguayVenezuela, RB<br>0.42<br>0.40 0.40<br>.4 0.39 0.38 0.38<br>0.35<br>0.33 0.33<br>0.32<br>.3 0.28<br>0.27<br>0.26<br>0.23<br>0.23 0.22<br>0.21<br>0.20 0.19<br>.2 0.18 0.18<br>0.16<br>0.14<br>0.13 0.13<br>0.11<br>0.10 0.10<br>.1<br>0.06<br>0.05 0.04<br>0.03<br>0.02 0.02<br>0.01 0.00<br>0<br><!-- End of picture text --> 31
<!-- Start of picture text --> o- telephone lines :-roads .2mobile phones<br>:.<br>§ o. -<br>s ETH 3 Pa 5 e :<br>3s PR s TH Ss :<br>BoSK EBas2Z §sSA"<br>cS=w43 SS]= Fi 3]se”<br>bd wes<br>Ss? + a o<br>o 2 4 6 8 1 o 2 4 6 8 1 o 2 4 6 8 1<br>Rank of country on (0-1) scale Rank of country on (0-1) scale Rank of country on (0-1) scale<br>- all countries and decades @ Ethiopia 2001-2010<br>@ SSA5 2001-2010<br><!-- End of picture text -->