rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.7: Impact of Housing Finance on Economic Growth (Sensitivity and Robustness Check)**

|||**FGLS**|**GMM**|
|---|---|---|---|
||**(1)**|**(2)**|**(4)**|
|GDP L1|**---**|**---**|**0.94*****<br>**(0.075)**|
|Housing finance depth|**2.25****|**2.27****|0.133|
||**(1.11)**|**(1.11)**|(0.146)|
|Housing finance policy|**0.002*****|**0.002*****|**0.0005***|
||**(0.0003)**|**(0.0004)**|**(0.0002)**|
|Population 15-64|**-1.63****|**-1.62****|-1.126|
||**(0.777)**|**(0.777)**|(0.222)|
|Investment stock|-0.013|---|-0.012|
||(0.042)||(0.012)|
|Human capital|**---**|---|-0.0002<br>(0.0007)|
|Trade openness|**-0.007*****|**-0.01*****|**0.0004***|
||**(0.001)**|**(0.001)**|**(0.0002)**|
|Conflict dummy t-1|---|---|---|
|Infrastructure (electricity)|**0.002****|**0.003****|0.0006|
||**(0.002)**|**(0.002)**|(0.001)|
|Inflation|---|---|-0.0004|
||||(0.0003)|
|Interest rate|---|---|-0.0002|
||||(0.0003)|
|Transfers|---|---|**-1.2e-18****<br>**(5.9e-19)**|
|Domestic credit to private sector|---|---|-0.0009<br>(0.0005)|
|Constant|19.04***|18.61***|---|
||(3.34)|(3.037)||
|Auto|Na|Na|**-1.110**<br>**(0.266)**|
|Sargan OIR|Na|Na|**3.462**<br>**(0.483)**|
|Wald|Na|Na|**3.688***<br>**(0.054)**|
|Countries|45|45|45|
|Observations|500|500|178|

_Source_ : Estimation result observation by the authors. _Note_ : Kaotrop (Financial openness*Trade openness); Hfinwealth (Housing finance depth*Wealth- _dummy_ : wealth-based decomposition-); Hfinlegal (Housing finance depth*Legal- _dummy_ : Legal-based decomposition-); Hfinsahel (Housing finance depth*Sahel- _dummy_ : Sahel-based decomposition-). *,**,**: significance levels of 10%, 5% and 1% respectively. Values in bracket are standard errors. Values in bracket are standard errors. Auto: Autocorrelation test. OIR: Overidentifying Restrictions test. The significance of bold values is twofold. 1) The significance of estimated coefficients and the Wald statistics. 2) The failure to reject the null hypotheses of: a) no autocorrelation in the Auto tests and; b) the validity of the instruments in the Sargan OIR test. P-values in brackets. AR(1): First Order Autoregression. AR(2): Second Order Autoregression. Autoregression is on the Return variables.

24
# **Informality and Protection from Health Shocks:**

# **Lessons from Yemen**

# **Yoonyoung Cho**<sup>*****</sup> **The World Bank**

JEL Codes: I12, J42, H31 Key words: Informality, health insurance, health outcomes, access to health service, health spending

* Social Protection and Labor Team, World Bank, Washington DC. Email: <u>ycho1@worldbank.org. The findings,</u> interpretations, and conclusions expressed here are personal and should not be attributed to the World Bank, its management, its Executive Board of Directors, or any of its member countries. This paper was prepared for informality study in HD of MENA (Middle East and North Africa) region. Thanks go to Roberta Gatti and Joana Silva, for their support and insights, David Newhouse, David Robalino, Rita Almeida, and other workshop participants in 2009 at the World Bank for additional suggestions, and Afrah Ahmadi for valuable insights in understanding Yemeni insurance system. I also thank session participants of the 6<sup>th</sup> IZA/WB conference in Mexico city, Mexico for their valuable comments. All errors are mine.

1
Dhaka division, and Chittagong district in the Chittagong division. We began with a list of the number of households in each of 1,971 _mouzas_ (the lowest administrative level at which national surveys are conducted in Bangladesh and for which such data were available) in the four targeted districts. We used the _mouza_ as our primary sampling unit (PSU) and selected 80 _mouzas_ with probability proportional to size (PPS) where size was the number of households.<sup>vi</sup> Within each _mouza_ , we selected 30 households for first-stage sampling, using a random walk method, as follows. Three enumerators, plus a supervisor, would arrive at the selected _mouza_ . The supervisor first reviewed the general layout of households in the _mouza_ , and then directed each enumerator to start at a different point, typically along the outside border of the _mouza_ . The enumerator was instructed to visit each 10<sup>th</sup> household.<sup>vii</sup> At each selected household, we used a screener questionnaire to collect basic information about all household members, including age, gender, employment status and type of employment. We collected screener information for the whole household from any adult who was available at the time.

Among the households included in the screener, there were a total of 10,690 household members, including 7,239 adults. Among these adults, 3,787 were reported to be working. We focused on working adults for several reasons. First, our aim was to examine the tradeoffs that individuals would make between different job attributes, conditional on having decided to participate in the labor force. In practice, this means that we exclude many women from the sample, since the Bangladeshi labor force participation rate (LFPR) for men was 81.7 percent in 2013, compared with an LFPR of 33.5 percent for women.<sup>viii</sup> In our results, we examine how preferences differ among currently working men 9
is low, with over 70% of households having no women in the workforce. Before 2017, only around 25% of children aged 3-6 were enrolled in preschool, well below averages in other Central Asian countries. Following the childcare expansion reform, the number of preschools has increased by over 3.5 times, and by 2022, preschool capacity reached 63% (Sankar, 2021). Despite this growth, preschool remains costly, with fees ranging from 25% to 200% of an average woman’s monthly earnings.<sup>1</sup> We use the Listening to Citizens of Uzbekistan (L2CU) survey conducted by the World Bank and Development Strategy Center, which includes a 2018 nationally representative baseline of 4,000 households and a monthly panel of 1,500 households through 2023. This data captures quarterly female labor supply and household outcomes. We measure preschool availability annually by district using data from the Uzbekistan Statistics Agency. We focus on households with children aged 1-6 who are most likely affected by the policy expansion.

In Section 4, we outline our empirical methodology. We adopt a quasi-experimental approach similar to Müller and Wrohlich (2020) and Nollenberger and Rodríguez-Planas (2015) to assess the impact of childcare availability on female labor supply. The gradual expansion of childcare facilities varied significantly across districts, providing both temporal and spatial variation that allows us to estimate the policy’s causal effect. Households in districts with above-median growth in childcare coverage since 2019 serve as our treatment group, while households in districts with later or minimal expansion form the control group. The key underlying assumption of our approach is that, in the absence of the policy, female labor supply would have followed a similar trend in both treated and control districts. To address potential confounding factors arising from unobserved regional differences, we include time and district-fixed effects in our analysis.

We present our findings in Section 5. Our primary empirical analysis shows that the childcare expansion policy increased women’s labor supply by 2.4 percentage points on average. This effect is particularly significant given the low baseline labor force participation rate for women, which averages just 20 percent. In relative terms, the policy led to a 12 percent increase in female labor force participation. Moreover, our heterogeneity analysis reveals that the impact was stronger for low- and middle-income households, as well as for those households prioritizing their children’s education. Notably, households with multiple adult women co-residing showed a stronger response to the policy compared to households where women live alone. Unlike previous studies where time availability was seen as the primary mechanism, our findings indicate that

> 1Source: https://cabar.asia/ru/uzbekistan-gosudarstvennyh-detskih-sadov-ne-hvataet-chastnye-dorogi

3
by the World Bank, in collaboration with WHO (Pandey, et al., 2004; Wang, et al., 2003).

For natural resource degradation, we base our indicator on recent research that identifies the vulnerability of people on fragile lands (i.e., land that is steeply-sloped, arid, or covered by natural forest) as a major determinant of rural poverty and natural resource degradation in developing countries (World Bank, 2003). Our indicator, the total rural population living on fragile lands, has been constructed from a GIS (Geographic Information System) - based spatial overlay of demographic, topographical, climatic and natural resource information.

We have developed our biodiversity threat indicator from a variety of sources. For terrestrial biodiversity, we use a GIS-based spatial overlay of human population with critical areas identified by Conservation International (CI), the World Wildlife Fund (WWF), and Birdlife International (BI). We also include freshwater lake areas, to capture the role of inland aquatic ecosystems. The World Bank’s Environment Strategy focuses on both the threat to biodiversity from human encroachment, and the value of biodiversity resources for human populations. Our indicator for this two-way relationship in each country is its total human population in critical biodiversity areas. For marine biodiversity, we draw on estimates of reef ecosystems at risk by Bryant, et al. (1998). Summing across all endangered reefs, we use each country's share of the total as our index of marine biodiversity threat. While terrestrial and marine threats are quite distinct geographically, we create a composite indicator to match the Bank's thematic category (biodiversity conservation). Since the two indices are weakly correlated (ρ = .27), assignment of relative weights has a significant impact on the result. We assign equal weights, because we have no scientific basis for a differentiated weighting scheme.

5
|Any earner type with no dependents|7.5%|5.4%|2.0%|1.5%|2.5%|
|---|---|---|---|---|---|
|Any earner type with senior dependent|6.7%|4.6%|2.4%|2.5%|1.0%|
|Total|100%|100%|100%|100%|100%|

Note. Some groups have been collapsed for presentation purposes. Sample of 71 countries Source: WB Staff’s calculations based on GMD.

In which types of households do poor women live?

Results at the individual level, show that by age 20 to age 34, women are two percentage points poorer than men, which is a significant difference. At the household level, adult couples with children and single male earner with children and a non‐earner dependent are the most numerous among the poor. At the individual level, marriage is the most frequent status for women in the 20‐34 age group.

All these findings suggest household formation and income generation are possible factors associated with individuals living in a poor household. In fact, both poor and non‐poor women concentrate in the _adult couple household with children (with or without other adults)_ group. However, the share of women living in households with children is higher among poor women (more than 90 percent) than among non‐poor counterparts (75 percent of total non‐poor women, and 77 percent among the 20‐34 age group). Similarly, the share of non‐poor women living in households without children is higher than among poor women. Looking through the lenses of the economic composition, more than half of poor and non‐poor women live in households with children and with children and earner dependents, and where the earner is a single male or a head couple. This is also the case among women 20‐34. As in the demographic composition case, women are concentrated in the most frequent household type found among the poor. Yet, the shares of poor women (both all women and women ages 20‐34) living in households with both children and non‐earner adults (i.e. more members depend on the income earner) is higher than among non‐poor women.

These patterns also hold true across regions, where the highest share of poor women living in households with children is found in Sub‐Saharan Africa (97 percent, compared to 88 percent among non‐ poor women), and the lowest in East Asia and the Pacific (86 percent, compared to 67 percent among non‐ poor women).

_Table 8. Distribution of poor and non‐poor women by demographic and economic household compositions, global._

|Demographic household typology|All wom|en|Women|20‐34|
|---|---|---|---|---|
||Poor|Non‐|Poor|Non‐|
|||poor||poor|
|Adult couple ‐ with children (30.6%)|38.3%|33.6%|43.0%|37.6%|
|Adult couple ‐ with children and other adults (17.1%)|37.6%|28.1%|36.2%|27.8%|
|Adult combinations (other than a couple) ‐ without children (13.6%)|2.6%|12.0%|3.6%|13.3%|
|One adult only ‐ no children (11.2%)|0.7%|3.3%|0.3%|2.8%|
|Total (72.5%)|79.2%|77%|83.1%|81.5%|

30
optimizing behavior and limited information, information asymmetry problems could have greater effect among female-owned firms due to lack of credit history or limited access to collateral.

The magnitude of such effects will depend on how the key theoretical channels between mobile money and investment may be affected by gender. Mobile money use may improve supplier and customer relationships and increase demand for credit. Beck et al. (2018), using a dynamic general equilibrium model with heterogeneous entrepreneurs, imperfect credit markets, and the risk of theft, find a positive relationship between supplier credit and mobile money use. Empirically the literature has documented the role of trade credit, via reputation effects, in increasing access to external sources of financing such as bank financing (Alphonse et al., 2006; Buckart and Ellingsen, 2004). Women entrepreneurs may have difficulty accessing suppliers and customers or breaking into networks dominated by men due to women-specific barriers in mobility or access to resources. If mobile money provides women greater access to supplier and customer networks, then this could lead to more opportunities to expand and invest. In other words, mobile money would provide women access to networks that men already have access to. Thus, the use of mobile money may lead to increased investment for female-owned businesses but not necessarily for male-owned businesses.

There is considerable debate whether female-owned businesses face more barriers in accessing finance than male-owned business. Asiedu et al. (2013) find that female-owned firms in SubSaharan Africa have difficulty accessing finance, while Hansen and Rand (2014a) do not find gender-specific differences in access to finance. The empirical link between access to finance and investment has been well established (Rajan and Zingales, 1998; Levine et al., 2000; DemirgucKunt and Maksimovic, 1998; Cull and Xu, 2005). If we assume that access to finance is harder for women entrepreneurs than male entrepreneurs, then mobile money may provide female-owned businesses access to financing that was already available to male entrepreneurs. Therefore, through the financing channel we can expect mobile money to have a positive effect on investment by female-owned businesses but not male-owned businesses. Similarly, the collection of transaction, savings, and financial operations data from digital financial service platforms may generate credit scores and evaluate and price credit risk. This can help to overcome the so-called collateral technology hurdle, which has hindered the development of credit markets in Africa (Ndung’u, Morales, and Ndirangu. 2016). Mobile money can provide female-owned firms a way to develop 7
|**Table 3: Cross‐Sectional Associations with Index Subco**|**mponents**<br>Log Sales|Log Sales|Log Sales|Log Profit|s Log Profit|s Log Profits|
|---|---|---|---|---|---|---|
|Proportion of marketing practices used|0.310***|0.229***|0.195***|0.302***|0.246***|0.209***|
||(0.036)|(0.035)|(0.035)|(0.034)|(0.034)|(0.034)|
|Proportion of buying and stock control practices used|0.112***|0.086***|0.072**|0.025|0.005|‐0.006|
||(0.034)|(0.033)|(0.032)|(0.032)|(0.032)|(0.032)|
|Proportion of record‐keeping practices used|0.574***|0.459***|0.409***|0.479***|0.394***|0.344***|
||(0.035)|(0.034)|(0.034)|(0.033)|(0.033)|(0.033)|
|Proportion of financial planning practices used|0.152***|0.136***|0.109***|0.117***|0.104***|0.078**|
||(0.039)|(0.038)|(0.038)|(0.037)|(0.036)|(0.036)|
|Log(Paid Workers)|0.654***|0.476***|0.466***|0.497***|0.367***|0.359***|
||(0.026)|(0.026)|(0.027)|(0.026)|(0.026)|(0.026)|
|Log(Owners Hours)|0.351***|0.313***|0.302***|0.260***|0.232***|0.221***|
||(0.020)|(0.019)|(0.019)|(0.019)|(0.018)|(0.018)|
|Log(Capital Stock)||0.092***|0.084***||0.071***|0.064***|
|||(0.007)|(0.007)||(0.006)|(0.006)|
|Log(Inventories)||0.200***|0.198***||0.149***|0.147***|
|||(0.010)|(0.010)||(0.009)|(0.009)|
|Owner is male|||0.100**|||0.134***|
||||(0.045)|||(0.045)|
|Age of owner|||‐0.005***|||‐0.006***|
||||(0.001)|||(0.001)|
|Years of education of owner|||0.012***|||0.011***|
||||(0.003)|||(0.003)|
|Digitspan recall|||0.035***|||0.033***|
||||(0.007)|||(0.007)|
|Firm age (years)|||0.008***|||0.007***|
||||(0.001)|||(0.001)|
|Raven test score|||0.019***<br>(0.004)|||0.013***<br>(0.004)|
|Sample Size<br>Notes:<br>Robust standard errors in parentheses, *, **, *** indic<br>respectively<br>Regressions also include dummies for zero or missing<br>All regressions include country dummies and industry|18102<br>a ** p<0.05<br>values of e<br>sector dum|18102<br>*** p<0.0<br>ach contro<br>mies.|18102<br>1"<br>l.|18089|18089|18089|

33
The prevalence of stunting is more strongly associated with geographic (regional) disparities than household characteristics. In 2008, a child in Lower Egypt is about 12 percentage points (in Frontier Governorates about 7 percentage points) more likely to be stunted), compared to a child with the same household characteristics in the Urban governorates. There is almost no difference in stunting and severe stunting prevalence by household wealth: stunting prevalence among the poorest quintile was 25 percent in 2008, compared to 24 percent for the top quintile. The situation was quite different back in 2000 when wealth quintiles as well as father’s education were significant correlates of stunting, in addition to regional variables.

On the other hand, with a successful fortification program, Egypt has achieved a significant reduction in iodine deficiency over the 2000s. For instance, in 2000, three in four children under the age of five lived in households where salt was not adequately iodized, compared to only one in four by 2008.<sup>9</sup> The rapid expansion in availability of iodized salt benefited households in all regions, and across all income groups. In rural areas, the share of children living in households with inadequately iodized salt declined from about 83 percent in 2000 to 28 percent in 2008, and from 87 to 44 percent among the poorest quintile (Figure 5). However, disparities in access to iodine based on wealth still persist. While less than 11 percent of children in the top quintile are reported to have insufficient iodine, over 44 percent of children in the bottom quintile live in households with inadequate (or no) iodized salt.

Similarly, the use of iron supplements by pregnant mothers improved significantly between 2000 and 2008. In about 43 percent of pregnancies that ended in birth in 2008 mothers did not receive iron supplementation tablets, which represents a significant improvement from the 72 percent registered in 2000 (Figure 6). Similar to the antenatal care section, mother’s education and wealth are strong correlates of whether blood samples are taken from the mother during pregnancy. In 2000, a child born to a mother with no formal education and in the poorest wealth quintile was 36 percentage

> 9 Inadequate iodization is defined as <=.15 ppm iodine in salt consumed by the household. (EDHS 2008 report)

19
the capacity of aid to treat equal countries equally. And transparency regards the capacity to assess the equity and effectiveness of aid. In a recent note, Anderson and Waddington (2006) group contributions to the normative debate into three main allocation principles respectively based on: poverty-efficiency, equal opportunity or structural indicators, roughly reflecting in this way the broader norms described.

The poverty-efficiency literature emphasizes the importance of considering the poverty level in conjunction with the policy capacity of a country so as to maximize the poverty reduction capacity of aid at the global level given a budget constraint. Collier and Dollar (2001) and Collier and Dollar (2002), for example, promote a poverty-efficiency aid allocation criteria. Poverty being equal, aid should increase with the policy capacity of a country, because aid can have larger growth effect in a better policy environment. Vice-versa, the policy capacity being equal, aid should increase with the level of poverty because the growthpromoting effect of aid has more poverty reduction capacity in a very poor country. The poverty-efficiency position is supported by influential institutions such as the World Bank (Wolfensohn, Stern, Goldin, Rogers, and Karlsson, 2002) or the OECD (OECD, 2003; OECD, 2006) although empirical evidence (among others, Nunnenkamp and Thiele, 2006) suggests that the broader donors’ community rarely follows this criterion.

This position is also questioned by scholars arguing for transparency, who point out the necessity of predictable, or less volatile, flows of aid to individual countries (among others Eifert and Gelb, 2008, Fielding and Mavrotas, 2008, Bulir and Hamann, 2006, Bulir and Hamann, 2003). As the policy capacity of a country is hard to assess and also volatile, the poverty efficiency criteria would be difficult to implement and also subject to frequent changes. This would speak in favor of using standard statistics such as GDP per capita or poverty rates to rank countries and allocate aid so as to capture basic but well understood indicators. Guillaumont, 2008 argues that the poverty-efficiency position is not backed by evidence and that a structural vulnerability approach whereby countries are considered based on their structural and exogenous constraints would be preferable, which is an argument for allocating more aid to the LDCs.

A different stream of the literature (Llavador and Roemer, 2001; Cogneau and Naudet, 2007) focuses instead on equal opportunities. These authors believe that the distribution 5