rafmacalaba/datause-encoder-data huggingface dataset:
Informal firms are financially excluded, use few and poor financial management practices and tools, and while LE-SE have the worst practices, all three types of informal firms are lagging. On average, only 10.6 percent of informal firms have a bank account (Figure 16). Among LE-SE, the share is only 5 percent, while it reaches 35.9 percent for HE-SE. The conditional correlations confirm that having a bank account is negatively associated with being LE-SE (Table 3).<sup>58</sup> An estimated 5.4 percent of the sample regularly uses banks or microfinance institutions, with employers having the highest use rate at 10.8 percent. Instead, nearly 40 percent of informal firms use mobile money in their regular operations with higher rates of use by employers and HE-SE. In terms of business practices, most firms mix business accounts with household expenses –almost 80 percent on average. The percentage is largest among LE-SE (83.6 percent) in comparison to HE-SE (75.4 percent) and employers (61.7 percent).

**Figure 16. Access to Financial Services and Financial Management**

<!-- Start of picture text --> Has bank account Account mixed with hh expenses<br>83.6<br>35.9 78.1 75.4<br>61.7<br>23.5<br>10.6<br>5.0<br>Total LE-SE HE-SE Emplo Total LE-SE HE-SE Emplo<br>yer yer<br>Uses bank or microfinance inst. Uses mobile money<br>for day-to-day operations<br>10.8 56.4<br>48.2<br>39.7<br>34.0<br>5.4<br>3.8<br>1.6<br>Total LE-SE HE-SE Emplo Total LE-SE HE-SE Emplo<br>yer yer<br>40<br>80<br>30 60<br>20 40<br>10 20<br>0 0<br>60<br>10<br>8 40<br>6<br>4 20<br>2<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average including Mozambique, Zambia and Zimbabwe except for mobile money which is not available for Zimbabwe.

All three informal firm types identify similar motivation for not being registered with authorities, though the top rationale differs by firm type. Employers mostly point to the lack of information about how to register as an impediment, while also citing low benefits from 58 For HE-SE we do not find a statistically significant association. This is due to the education regressor, which is likely positive correlated with having a bank account. When the education dummies are dropped, we find a positive relationship between having a bank account and HE-SE.

32
The average effect sizes we estimate for outcomes related to school participation and progression and children’s skills during the preprimary years and beyond suggest high returns to increasing investment in preprimary education and to targeting disadvantaged populations for investment. When experimentally offered access to preprimary education, children are on average 1.4 standard deviations (sd) more likely to participate in the preprimary program than their control group counterparts, suggesting strong demand for services. This is approximately equivalent to a 30 percentage point increase in preprimary school participation. During the preprimary period, children’s cognitive skills (in language, literacy, math, and other general skills) improve by an average of 0.15 sd, with larger improvements in literacy (0.22 sd) and math (0.22 sd) compared with language (0.10 sd) and general skills/indices capturing multiple domains (0.08 sd). During this period, children’s non-cognitive skills (their executive functions, socialemotional skills, and behavior) improve by an average of 0.12 sd. Effects are similar (and statistically indistinguishable) across high- and low- & middle-income countries.

The average effects pool effects from studies examining impacts relative to different counterfactual scenarios. Sometimes the intervention under evaluation introduced preprimary education where the only other option was care in the home; sometimes the intervention expanded the set of facility-based options or attempted to improve the quality of existing services. We find larger average effects among evaluations assessing interventions that aimed to improve the quality of existing preprimary education services than among evaluations of expansions in services. The average effect on cognitive skills, for example, was approximately 0.2 sd among evaluations of quality improvements compared to 0.1 sd among evaluations of expansions.

Our estimates of average effect sizes also reflect average effects across small-scale pilots implemented by non-governmental organizations and nationally scaled government programs; they also average effects across contexts where teachers with formal qualifications teach small class sizes for a full day and contexts where teachers with only secondary education and a couple of weeks of training manage large classes for half a day. Insufficient reporting of program implementation features and contexts, as well as the sample size of studies, preclude testing for heterogeneous average effects along these dimensions.

A focus solely on impacts observed during the preprimary period could underestimate the full benefits of improving skills during the early childhood. Investments in preprimary education could lead to improvements that extend beyond the pre-primary period (Kline & Walters, 2016) 4
# Figure 13. Human capital and infrastructure

# A. Years of schooling

<!-- Start of picture text --> Distance to frontier<br>Inter-quartile range 2017 2007<br>100<br>Advanced economy average<br>80<br>60<br>40<br>20<br>0<br>LAC ECA MNA EAP SAR SSA<br><!-- End of picture text -->

# C. Infrastructure gaps

<!-- Start of picture text --> Percent of GDP per year<br>20 Range Preferred LMICs<br>15<br>10<br>5<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# B. Life expectancy

<!-- Start of picture text --> Distance to frontier<br>Inter-quartile range 2017 2007<br>100 Advanced economy average<br>90<br>80<br>70<br>60<br>50<br>40<br>LAC ECA MNA EAP SAR SSA<br><!-- End of picture text -->

# D. Climate risk

<!-- Start of picture text --> Share of total Share of total<br>population population<br>4.5 Less-favored agricultural areas 10<br>Living at sea level (RHS)<br>8<br>3.0<br>6<br>4<br>1.5<br>2<br>0.0 0<br>EAP SSA LAC SAR MNA ECA<br><!-- End of picture text --> Source: Barbier and Hochard (2018), Rozenberg and Fay (2019), United Nations Development Program. Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa, and LMICs = Low- and middle-income countries.

A. Education index from the Human Development Index. 2007 and 2017 are unweighted means in EMDEs. Inter-quartile range is for 2017 observations.

B. Life expectancy index from the Human Development Index. 2007 and 2017 are unweighted means in EMDEs. Interquartile range is for 2017 observations.

C. Investment needs based on goals as set out in Rozenberg and Fay (2019), including both new investment and maintenance of existing capital stock. Infrastructure investment includes investment in electricity, transport, water supply and sanitation, flood protection, and irrigation. “Preferred” is defined as the infrastructure “pathway [that] limits stranded assets, has a relatively high per capita consumption due to electric mobility, and invests mostly in renewable energy and storage.” D. Less-favored agricultural areas are agricultural lands constrained by difficult terrain, poor soil quality, limited rainfall, or with limited access to markets. “Sea level" identifies areas where elevation is below 5 meters. Data are from 2010.
the households across PSUs. For L2Arm, 400 PSUs are allocated across strata proportional to the population<sup>19</sup> (i.e., implicit allocation) with some adjustments. Ten households are targeted within each PSU.<sup>20</sup> **Table 5** presents the proposed baseline sample allocation.

In the first stage of the two-stage stratified cluster sampling design, PSUs in each stratum are randomly selected using systematic random sampling with probability proportional to size (PPS), size being the estimated population of the pre-EA. This method assigns each PSU’s likelihood of selection based on the PSU’s size within the stratum. Population size, rather than the number of households, is used due to a lack of data on the number of households at the PSU level in the sampling frame. Thus, each PSU’s likelihood of selection corresponds to the percentage of the stratum population residing in the PSU. In the second stage, a set number of households are randomly selected from each chosen PSU.<sup>21</sup>

**Table 5: Baseline sample design based on proportional sample allocation (2022)**

||||Baseline<br>||
|---|---|---|---|---|
|Stratum|Estimated<br>Population Size|Allocated<br>Number of PSUs|Target N of HHs<br>PSU size|Number of HHs|
|Yerevan|1,098,866|147|10|1470|
|Aragatsotn - Urban|26,738|4|10|40|
|Aragatsotn - Rural|98,949|13|10|130|
|Ararat - Urban|72,294|10|10|100|
|Ararat - Rural|186,983|25|10|250|
|Armavir - Urban|82,953|11|10|110|
|Armavir - Rural|183,703|25|10|250|
|Gegharkunik - Urban|65,902|9|10|90|
|Gegharkunik - Rural|162,809|22|10|220|
|Kotayk - Urban|137,493|18|10|180|
|Kotayk - Rural|116,364|16|10|160|
|Lori - Urban|124,050|17|10|170|
|Lori - Rural|87,532|12|10|120|
|Shirak - Urban|133,620|18|10|180|
|Shirak - Rural|96,856|13|10|130|
|Syunik - Urban|90,205|12|10|120|
|Syunik - Rural|44,350|6|10|60|
|Tavush - Urban|49,859|7|10|70|
|Tavush - Rural|69,943|9|10|90|
|Vayots Dzor - Urban|16,160|2|10|20|
|Vayots Dzor - Rural|31,501|4|10|40|
|**Total**|**2,977,130**|**400**||**4000**|

_Notes_ : The population estimates at the stratum level are by the end of 2022 and match with the population statistics from the Statistical Committee of the Republic of Armenia. Refer to https://armstat.am/file/doc/99538403.xlsx for the urban population and https://armstat.am/file/doc/99538413.xlsx for the rural population.

> 19 It is preferred to use total number of households as the size. However, there is no available information on the number of households in each pre-EA.

> 20 Although 400 PSUs and 10 households in each PSU, with a total target sample of 4,000 households, are targeted, the sample size can be smaller than the target if the total number of households in the selected PSU is less than the target or due to nonresponsiveness.

> 21 Appendix C describes the calculation of survey weights.

23
banks with lower spreads and margins. Competition, however, is not necessarily the same as market structure (Claessens and Laeven, 2004). Demirguc-Kunt, Laeven and Levine (2004) find no robust association of bank concentration with interest rate margins. The ownership structure of the banking system might also be associated with differences in efficiency. Claessens, Demirguc-Kunt and Huizinga (2001) find that countries with higher share of foreign banks experience lower average margins, consistent with the hypothesis that foreign bank entry imposes competitive pressure with resulting efficiency gains.<sup>2</sup> All these studies, however, are based on cross-country panels. This paper studies the effect of market concentration and foreign bank entry for a low-income country’s banking system that has undergone profound changes in its ownership and market structure.

Our results support the strong role that bank-specific characteristics play in variation of interest spreads and margins. First, we find more cross-bank than cross-time variation in spreads and margins. Second, and consistent with the first finding, bank-level variables are the statistically and economically most significant group of variables in explaining variation in spreads and margins. Specifically, banks with larger overhead costs, more exposure to agriculture and less exposure to mining as well as domestically owned banks are associated with higher spreads. Higher overhead costs are also associated with higher ex-post margins, while banks with higher share of agricultural lending report lower margins. Larger banks charge lower spreads, but earn higher margins. Although the Ugandan banking market has undergone dramatic changes in its market structure, there does not seem a robust relationship between these changes and variation of spreads or margins over time. Further, we find little evidence that structural changes such as the privatization of UCB and the subsequent merger with Stanbic

> 2 Martinez Peria and Mody (2004) find an indirect effect of foreign bank entry on interest margins through lower overhead costs in Latin America, while Barajas, Steiner and Salazar (2000) find a positive effect of foreign bank entry on operational efficiency in Colombia.

4
where a government might be a real or perceived contributor to displacement or is harboring an overt or covert policy to reduce the number of refugees in the country.

With limited results in including FDPs in national sampling frames, alternative approaches remain necessary. The UNHCR, the World Bank and numerous scholars and practitioners worldwide are experimenting with satellite images and phone surveys to try detecting refugee and IDP populations that may escape the UNHCR and national registers with some initial encouraging results. In Lebanon, Jordan and the Kurdistan region of Iraq, Aguilera et al. (2020) designed sampling strategies for Syrian refugees with known ex-ante selection probabilities. They used a variety of data sources, including data collected by humanitarian agencies, and also employed geospatial segmenting to create enumeration areas where they did not exist. Systematic field experiments are also underway to test different sampling approaches for IDPs living in camps. For example, Himelein, Pape and Wild (forthcoming) compare the performance of five alternative sampling approaches (satellite mapping, segmentation, grid squares, “Qibla method,” and random walk). Different indicators are assessed including household size, consumption, poverty and ownership of assets. Using empirical evidence from a field experiment in an IDP camp in South Sudan, the total survey error of each sampling approach is compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. One of the main findings is that the implementation of all approaches suffers from over-estimation of household sizes caused by a systematic tendency of enumerators to select larger households because they are more likely to find an adult respondent. Such studies with a focus on ground truthing remain important to validate sampling approaches for displaced communities.

Sampling FDPs outside camps faces a different challenge, especially when a list-based approach is not possible. FDPs can be spread in low density across a larger population requiring an area-based adaptive sampling approach. The approach continues to list additional enumeration areas systematically selected, e.g., by selecting neighboring enumeration areas, until a sufficient number of FDPs are covered. Adaptive sampling creates analytical complexities for the calculation of sampling weights. It also adds to the cost as more enumeration areas must be listed – often with only very few or possibly no FDPs present – and it is more difficult to implement given the uncertainty about the number of required enumeration areas that need to be visited to satisfy a given sample size.

# Unit of observation

The definition of the unit of observation in a survey (household, family, case, individual) is a crucial choice that has direct impacts on the measurement of poverty because it determines how household income, 9
recent papers by Cooke (1998a, 1998b) on Nepal show that scarcity motivates increased time on collection but has no significant effect on labor in agriculture. Adding to this thin literature is a paper by Fisher _et al_ . (2005) using data from southern Malawi, which supports some of Cooke’s findings.

Cooke (1998a,b) and Fisher _et al._ (2005), while their empirical models differ, derive their labor allocation models from a neo-classical household production function framework. Similar models are used in Illahi and Grimard (2000) and Khandker (1988) who study labor allocation in different contexts in Pakistan and Bangladesh. We do not develop a formal model of household behavior for this paper; however, our partial equilibrium personal labor allocation model is rooted in the same household production and utility function framework.

The idea here is that of a rural representative household that functions in a subsistence setting and produces the goods that it consumes. Households choose the optimal levels of consumption goods and leisure. Some home produced goods (for example maize) may be generated with the use of market inputs, labor and land. Others, such as fuelwood are generated by the use of biomass and labor. Household behavior and utility is conditioned on several fixed household characteristics such as household size, health stock and education and so on. Households maximize utility by allocating optimal amounts of labor to different home production tasks and by purchasing market inputs subject to a full income constraint. At the margin, households equate the marginal value product of leisure to the net returns to labor in different tasks. This yields a set of labor supply equations that can be empirically estimated. Men and women’s labor supply is a function of output prices, wages, and household and community characteristics that may affect productivity. Scarcity of biomass is expected to impact labor supply decisions.

Following Illahi and Grimard (2000) we model labor allocation as an individual decision influenced by household characteristics. This level of disaggregation allows us to control for individual characteristics and their relationship with the rest of the household in the labor allocation decision. For example, we can control for age, sex, health, and marital status of the individual.<sup>14</sup>

> 14 Alternate models (Cooke 1998a,b, for example) estimate labor demand and supply based on average household behavior versus individual allocative decisions.  In these models, the wage rate is for an ‘average’ male or female member of the household.  However, wages differ based on individual age, sex, education, and experience rather than because of household characteristics.  This is the reason why we

13
## EUROPE AND CENTRAL ASIA

||||Δ NRCA|||
|---|---|---|---|---|---|
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Georgia|‐0.403|‐0.263|0.140|yes|yes|
|Kyrgyzstan|0.004|0.082|0.078|yes|no|
|Armenia|0.048|0.067|0.020|yes|no|
||SE|Temporary|diff<br>Δ NRCI|Equal sign trend|Reject H0|
|Georgia|0.033|0.010|‐0.023|yes|no|
|Kyrgyzstan|0.054|0.117|0.063|yes|no|
|Armenia|‐0.346|‐0.337|0.009|yes|no|
||||Δ RC|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Georgia|‐0.047|‐1.073|‐1.026|yes|yes|
|Kyrguiztan|‐0.216|‐0.519|‐0.303|yes|yes|
|Armenia|0.819|0.193|‐0.627|yes|yes|
||||Δ NRM|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Georgia|0.323|0.409|0.086|yes|yes|
|Kyrguiztan|0.126|0.019|‐0.107|yes|yes|
|Armenia|0.216|0.486|0.270|yes|yes|
||||Δ RM|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Georgia|0.097|0.153|0.056|yes|no|
|Kyrguiztan|‐0.121|‐0.198|‐0.077|yes|yes|
|Armenia|0.340|0.615|0.275|yes|yes|

# Annex II. Descriptive statistics for NSE and SE

# 1. Age and Gender

Average age of employment. Latin American and the Caribbean. (Mid 1990/Mid 2010)

|Mean AGE|Argentina|Uruguay|Brazil|Chile|Peru|Bolivia|Dominican<br>Republic|Mexico|El Salvador|
|---|---|---|---|---|---|---|---|---|---|
|Starting point|38.2|39.3|34.7|37.7|36.4|35.9|35.0|35.1|35.1|
|Ending point|39.9|40.7|38.0|42.1|40.7|37.3|39.0|37.5|37.5|

Page 49 of 51
**Table 4A: Rosenbaum Sensitivity analysis for the Impact of Family Background on Children's Years of schooling** **<u>(primary schooling as the threshold for parental education)</u>**

|**Gamma**|**Non farm, low**|**edu**|**Farm, high e**|**du**|**Non farm, hig**|**h edu**|
|---|---|---|---|---|---|---|
||**Mantel-Haenszel**<br>**statistics**|**P value**|**Mantel-Haenszel**<br>**statistics**|**P value**|**Mantel-Haenszel**<br>**statistics**|**P value**|
|**Abadie and I**|**mbens matching**||||||
|_Daughters_|||||||
|1.0|0.50|0.00|1.00|0.00|1.63|0.00|
|1.3|0.25|0.05|0.75|0.00|1.38|0.00|
|1.5|0.13|0.26|0.63|0.00|1.13|0.00|
|1.8|-0.13|0.72|0.38|0.00|1.00|0.00|
|2.0|-0.25|0.90|0.25|0.00|0.88|0.00|
|_Sons_|||||||
|1.0|0.75|0.00|1.00|0.00|1.63|0.00|
|1.3|0.38|0.00|0.75|0.00|1.38|0.00|
|1.5|0.25|0.03|0.63|0.00|1.25|0.00|
|1.8|0.13|0.31|0.38|0.00|1.00|0.00|
|2.0|0.00|0.62|0.25|0.00|0.88|0.00|

|**Biased corrected**<br>_Daughters_|**radius matching**||||||
|---|---|---|---|---|---|---|
|1.0|0.50|0.03|1.00|0.00|1.50|0.00|
|1.3|0.00|0.44|0.50|0.00|1.00|0.00|
|1.5|0.00|0.79|0.50|0.00|1.00|0.00|
|1.8|-0.50|0.98|0.00|0.08|0.50|0.00|
|2.0|-0.50|1.00|0.00|0.36|0.50|0.00|
|_Sons_|||||||
|1.0|0.50|0.00|1.00|0.00|1.50|0.00|
|1.3|0.50|0.03|0.50|0.00|1.50|0.00|
|1.5|0.00|0.24|0.50|0.00|1.00|0.00|
|1.8|0.00|0.76|0.00|0.25|1.00|0.00|
|2.0|-0.50|0.94|0.00|0.74|0.50|0.00|

# **_Notes:_**

1. Mantel-Haenszel test statistics gives the upper bound under the assumption of overestimation of treatment effects; 2. Gamma=1 implies the absence of unobserved selection bias and both matched individuals have the same probability of participating; 3. Gamma=2 implies the two individuals are identical on matched covariates but one might be twice as likely as the other to receive the treatment because they differ in terms of unobserved covariates.
13- Rashad, H. (1989), 'Oral rehydration therapy and its effect on child mortality in Egypt', Journal of Biosocial science; 10 (suppl): 105-113 and Hoda Rashad (1988), 'Measuring the demographic impact of oral rehydration therapy: Learning from Egyptian experience', The Population Council West Asia and North Africa, Regional papers, Egypt.

14- United Nations Children's Funds and National Control of Diairheal Diseases Project (1989), 'Control of diarrheal disease and its effect on mortality in Menoufia governorate, Egypt', draft report.

15- Black, R.E. (1984), 'Diarrheal diseases and child morbidity and mortality', Child Survival: Strategies for Research edited by Henry Mosley and Lincoln Chen, Population and Development Review, A supplement to vol. 10: 141-162.

16- Fauveau V., Yunus M., Zaman K. (1991), 'Diarrhoea mortality in rural Bangladesh children', Journal of Tropical Medicine, vol.37: 31-36.

17- Chowdhury, J.R., V. Fauveau, M. Yunus, K. Zaman and A. Briend (1991), 'Is acute watery diarrhoea an important cause of morbidity and mortality among rumral Bangladeshi children?', Transactions of the Royal Society of Tropical Medicine and Hygiene, vol.85: 128130.

18- Mosley, W. H. and Becker, S. (1988), 'Demographic models for child survival: implications for program strategy', paper prepared for conference on Child Survival Programs: Issues for the 1990's, Institute for International Programs, School of Hygiene and Public Health, John's Hopkins University, November 21-22.

19- Larson, A. and S.N. Mitra, (1990), 'Rates measuring the use of Oral Rehydration Solutions (ORS): A critical assessment', Research Note on Child Survival, no. 32cs International Population Dynamics Program, the Australian National University.

20- _Some of these evidence have not been published yet, they were supplied in kindness by both Peter Miller and Ray Langsten. The author particularly wishes to thank Mr. Miller for his continuou_ **_s_** _upply of needed references and more importantly for lively debates that enriched the presentation._ 21- Ray Langsten and Kenneth Hill (1991), 'Child Survival in Rural Egypt, Third Six Month Report', Draft report on the base line round of a Menoufla child survival study underway under Johns Hopkins/American University in Cairo (AUC) auspices.

22- Social planning, Analysis and Administration Consultants (SPAAC), (1988), Final Report: Evaluation of NCDDP National Campaign, KAP of Mothers", Cairo.

23- Social planning, Analysis and Administration Consultants (SPAAC), (1988), 'Menoufli ORT Impact Study', Mimeo.

25