rafmacalaba/datause-encoder-data huggingface dataset:
The adjustments underscore again the need to ensure that any sampling biases are identified and corrected. In the particular case of the CILSS, the corrections for basic needs indicators are only quantitatively important for the poor, but this result must be considered unusual. In most cases, basic needs fulfillment, like welfare in general, is negatively correlated with household size, and thus sampling bias affecting household selection would be expected to give misleading estimates of the indicators also at the national level. At any rate, the magnitude of the corrections noted for the poor in Cote d'Ivoire are sufficiently important to affect the targeting of programs of basic needs provision to the poor, in the sense that the needs of the poor, and especially the very poor, are higher than previously estimated.

# **VII. Concluding Observations**

This paper has demonstrated the necessity of investigating closely the sampling design and properties of household survey data before embarking on empirical research. Regrettably, this does not appear to be a frequent practice among users of household survey data. The case study based on the CMte d'Ivoire Living Standards Survey 1985-88 revealed the occurrence of flawed sampling procedures which had not been corrected by previous users of the data. This has resulted in serious biases in estimates of household size, which in turn have yielded severely biased estimates of household expenditures and poverty measures. Basic needs indicators reported for the country as a whole were less affected, but the bias proved to be more pronounced at the lower end of the distribution.

In this paper an appropriate correction procedure was developed. The procedure consists of applying suitably constructed household size weights to the data. The quantitative adjustments to estimated variables proved to be non-trivial and served to underline the seriousness of problems arising from sampling biases. For example, the head count estimate of poverty in CMte d'Ivoire was over-estimated by 14 percent in 1986. The bias proved to differ widely across regions and socio-economic groups and was frequently in the order of 34
||(0.00467)|(0.00725)|(0.0166)|
|---|---|---|---|
|Real interest rate (%)|-0.00257**|-0.00289|0.0156*|
||(0.00130)|(0.00201)|(0.00805)|
|Net inflow of FDI (% GDP)|0.000743|-0.00128|-0.000616|
||(0.00564)|(0.00166)|(0.00329)|
|Government debt (% GDP)|0.000842|0.00534***|-0.00284**|
||(0.000795)|(0.00191)|(0.00141)|
|Domestic credit (% GDP)|-0.00190|-0.00220|-0.000799|
||(0.00163)|(0.00156)|(0.00109)|
|Urban population growth|0.0280|-0.0460|0.0790|
||(0.0296)|(0.0608)|(0.0786)|
|Agriculture value added|-0.0251***|0.0214*|0.0657**|
||(0.00484)|(0.0114)|(0.0323)|
|Dummy for legal strength|0.226|-0.735**|-0.248|
||(0.171)|(0.286)|(0.593)|
|Countries leading in housing investment|-2.019***|2.192***|-2.837***|
||(0.551)|(0.438)|(0.509)|
|Countries contemporaneous in housing investment|1.250***|0.293|-1.943***|
||(0.476)|(0.402)|(0.590)|
|Countries where no clear relation|-2.311***|2.372***|-0.371|
||(0.635)|(0.518)|(0.980)|
|Region Dummies: Europe and Central Asia|-6.222***|4.186***||
||(0.581)|(0.815)||
|Region dummy: Latin America and Caribbean|-5.054***|5.947***||
||(0.288)|(0.468)||
|Region dummy: Middle East and North Africa|-5.303***|7.588***||
||(0.362)|(0.876)||
|Region dummy: Sub-Saharan Africa|-4.109***|0.292||
||(0.439)|(0.503)||
|Constant|9.302***|4.215***|6.574***|
||(0.516)|(0.636)|(0.850)|
|Observations|965|736|925|
|Number of countrycode1|34|26|31|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||

**Table 5: Results for SSA and by income group**

|VARIABLES|(1)<br>Low and lower<br>middle income|(2)<br>Upper middle<br>income|(3)<br>All SSA|
|---|---|---|---|
|Domestic savings(% of GDP)|-0.0290***|-0.0411*|-0.0249***|
||(0.00741)|(0.0211)|(0.00590)|
|Current Account balance (% of GDP)|0.00616|-0.0166|0.00527|
||(0.00826)|(0.0192)|(0.00573)|
|Investment in bond and equity (% GDP)|-0.0148|0.00663|0.00424|

24
However, the indirect channels refer to societal changes that could take place when the state fails, composition of the community is affected by displacement, local market operations are altered, and the way individuals relate to each other changes. All of these effects are likely to change social norms that govern the role of women (Justino 2018), potentially impacting their empowerment. These more substantial and long-term effects on female empowerment are harder to measure and remain to be observed in the case of Yemen.

# IV. Data and methodology

- a. Survey data: HBS 2014 and YHDS 2021

The analysis in this paper draws on two household survey datasets: The Household Budget Survey (HBS) of 2014 and the Yemen Human Development Survey (YHDS) of 2021.

The HBS 2014 is the main welfare survey for Yemen used to construct household consumption and official poverty statistics. It is a multiuse survey, with different modules on welfare, women’s role in the household, health, and income. The YHDS 2021 was collected from the areas under the control of the IRG in Yemen. In the first stage of sampling, 105 accessible enumeration areas (EAs) were drawn from the 1,200 EAs of the HBS 2014, providing a panel of EAs over time. The YHDS 2021 then visited a sample of 1,681 households, representative of four regions under IRG control, urban and rural locations, and displacement status. The YHDS provides key indicators on welfare, living conditions, human development outcomes, and women’s role in household decision-making.

We construct a combined dataset of 2014 and 2021, limiting the data to the common EAs. This limits the representativity of the analysis, since the YHDS 2021 was not collected across the entire country for security reasons, but it does allow for the analysis of relationships over time. The resulting dataset is 2,469 observations. The pattern of conflict and violence observed in the sample is likely to be different for Yemenis living in the Northern areas and under the control of the Houthis, where we might see different impacts on household decision making and social control. In fact, evidence points to stricter mobility for women in areas under Houthi control (ACAPS 2023), but the nuances of this impact on gender roles are not yet clear. Unfortunately, this remains a limitation of the analysis presented in this paper.<sup>3</sup> The main outcome of interest is women’s participation in household decision making. We consider the involvement of any woman in the household in decisions related to household purchases, health expenditures, marriage of children, education, and other decisions. In Figure IV.1 we see that women’s involvement in decision making reduced from 2014 to 2021 in terms of the marriage of children, but we see an improvement in 2021 for all other decisions. The

> 3 Figure 0.1 in the appendix maps the variation in conflict intensity across all districts in Yemen, while highlighting the common 2014-2021 sampled districts in the IRG areas.

7
in this table (i.e. child characteristics, education of adults, characteristics of the head, urban residence).

The coefficients on the dummy variable for being male confirm those reported earlier on the bivariate relationships. The effect is significant for all of the Western and Central African countries (except for Ghana), for the North African countries (Egypt in 1995-96 and Morocco), and for the South Asian countries (other than Bangladesh). In these countries, the effect of being male increases the probability of being enrolled by between 14 percentage points (India) and 29 percentage points (Central African Republic) except for Egypt where it is 7.7 percentage points. In the other regions, the only countries with a significant female disadvantage are Comoros, Malawi (although the effect is significant in 1992 but not so in 1996), and Uganda.

Virtually all countries have a significant (both in the statistical sense as well as in magnitude) wealth gap in the percentage enrolled, especially comparing the poorest to the richest group. The sole exceptions to this pattern are Ghana, Kenya, Malawi in 1996, Namibia, Colombia in 1990 (although the gaps are significant in the 1995 sample), Kazakhstan, and Uzbekistan. The five largest rich-poor gaps occur in Mdorocco (44 percentage points), Pakistan (36), Benin (34), Mali (28), and Burkina Faso (27). In two of these (Morocco and Pakistan) being male significantly mitigates (but not completely) the wealth gap, although this is not the case in the other countries. For example, in Pakistan, the marginal effect of being in the richest group is 43 percentage points, but if the child is male this is reduced by 14 percentage points. The other countries where the effect of being male significantly reduces the wealth gap are Egypt, India, Morocco, Mozambique and Pakistan. Typically though, the wealth gap is not mitigated by being male.

The results from Table 8 are derived from a pooled sample of urban and rural households, and include a dummy variable equal to one for urban residence. As discussed in which to take this average, are included. In those cases, the average is set to zero.

32
**necessary** to **replace this assumptLon. The** introduction of convex adjustment costs for **the** capital stock provides such a smoothing incentive.<sup>6</sup> **'he** following analysis follows closely that first presented in Auerbach (1989). For the interested reader, the full derivation is provided in the appendix. An empirical application for the United States may be found in Auerbach and Hines (1988).

We begin again with a firm seeking to maximize its value as in (11), but introduce two changes, First, the tax parameters may vary over time, so that, in particular, r, k and hence r require time subscripts. For the moment, we continue to assume perfect certainty about these tax changes and the absence of any risk. Second, we replace the exogenous price of capital goods g with a total cost g(l - _60K_ + i ), chosen to give rise to a simple expression for the marginal cost of capital goods: The term the 0 is an adjustment cost parameter, equal to the percent increase in effective capital goods prices to the firm per unit of additional investment.

Replacing g in (11) with q as defined in (20), and adding subscripts to the tax parameters, we obtain the following Euler equation for the firm, replacing (12): where the after-tax present value of investment incentives is: 18
however, better-capitalized banks could better sustain lending and even lend more (Gambacorta and Shin 2018).

# **3. Conceptual Framework: Basel III Implementation and Transmission Mechanism**

This section provides a conceptual framework on how the Basel III implementation can affect SME access to financing. First, we describe the progress with Basel III implementation by country and how we classify whether a country is considered a Basel III implementer or not. Second, we focus on the potential transmission mechanisms from Basel III implementation to SME access to financing.

# <u>3.1 Basel III Implementation</u>

We use the BIS Financial Stability Institute’s survey on Basel II, 2.5, and III Implementation (FSI 2015) to determine whether a country implemented Basel III. The value of the variable _Def cap_ in the survey needs to be equal to the value of 3 or 4 for the country to be classified as a Basel III implementer for the purpose of our study.<sup>5</sup> _Def cap_ stands for a definition of capital. Table A.1, in the appendix, provides country-specific information on the implementation status—along with the list of countries and the availability of World Bank Enterprise Surveys (which we discuss in detail in the following data section).

The value for most countries classified as Basel III implementors is 4, that is, the final rule on capital adequacy is already in force (FSI 2015). Basel III implementers that show the value of 3 published the final rule but the rule is not yet in force. The values of 1 and 2 in FSI (2015) denote that the regulation is published in draft form or is not published at all. Table A.1 shows that, according to our classification, Basel III has been implemented (for our sample of countries) mostly in 2013 (Argentina, Bolivia, Colombia, India, Indonesia, Kenya, Malaysia, the Philippines, Thailand, and Turkey) and 2012 (North Macedonia and Peru).<sup>6</sup> We label these countries as treated countries or Basel III implementors, interchangeably. Other EMDEs did not implement Basel III

> 5 However, G20 countries are not included in the FSI Survey. Consequently, for the G20 countries in our sample (Argentina, India, Indonesia, and Turkey), we use the data from the progress reports on adoption of the Basel regulatory framework, which are regularly prepared by the Basel Committee on Banking Supervision (BIS 2015).

> 6 We include Peru in the treated group of countries—although it did not yet implement Basel III definition of capital, Peru started with the introduction of capital buffers in line with Basel III (as well as a few other aspects of the Basel III framework; see table A.2).

6
5 53 -

- Desai, B.M., 1983, 'Group-Based Savings and Credit Programme in Rural India," in Grouo Based Savings and Credit for the Rural Poor; Papers and Proceedings or a Workshop in Bagra, Bangladesh, November. ILO, Geneva.

- Desai, B.M., 1983 "Group Lending Experiences in Reaching Small Farmers", Occasional paper #761, Dept. of Agricultural Economics and Rural Sociology, Ohio State, Columbus.

- Desai, B.M., 1983, 'Group Lending in Rural Areas', in Von Pischke, J.K., D. Adams and G. Donald (eds), Rural Financial Markets in Develovint Countries, The World Bank, Johns Hopkins University Press, pp. 284-288.

- Gadway, John, 1980, 'La Inflacion y la Politica de Prestamos a Bajo Interes: Informe para la Cooperativa de Ahorro y Credito Tingo Maria,' USAID/Peru (January).

- Gadway, John, 1988, 'The FENACRE System of Credit Unions: A Survey," USAID/Bolivia (January).

- Government of Kenya. 1987. 'Renewed Growth through the Cooperative Movement,' Sessional Paper _#4,_ Ministry of Cooperative Development, Nairobi.

- Hossain, Mahabub, 1988, 'Credit for Alleviation of Rural Poverty: The Grameen Bank in Bangladesh', International Food Policy Research Institute, Research Report No. 65.

- Kislev, Y., Z. Lermaru and P. Zusman, 1988, 'Experience with Credit Cooperatives in Israeli Agr'iculture', Report prepared for Agriculture and Rural Development Department, The World Bank.

- Lee, Tae Young. Kim, Dong Hi and Dale W. Adams, 1983, 'Savings Deposits and Agricultural Cooperatives in Korea', in Von Pischke, J.D., D. Adams and G. Donald (eds.) Rural Financial Markets in Developing Countries, The World Bank, Johns Hopkins Uniersity Press, pp. 288-295.

- Marion Peter, J., 1987, 'Building Successful Financial Systems: The Role of Credit Unions in Financial Sector Development'. World Council of Credit Unions, Madison.

- Mosley, Paul, and Rudra Prasad Dahal, 1987, *Credit for the Rural Poor: A Comparison of Policy Experiments in Nepal and Bangladesh," Manchester Paners on Development (July), pp. 45-59.

- Owusu, Kwame Opoku, and William Tetteh, 1982, 'An Experiment in Agricultural Credit: The Small Farmer Group Lending Programme in Ghana (1969-1980),' Savints and Development, (1), pp. 67 13.

- Poyo, Jeffrey, 1983, 'La Movilizacion de Ahorros por las Cooperativas de Ahorro y Credito Rurales en Honduras,' (April).
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic growth, 2003-07<br><!-- End of picture text --> <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>telecom electricity roads<br>b. Potential contributions of infrastructure to annual per capita economic growth<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>Niger Uganda Ethiopia Tanzania Kenya South Mauritius<br>Africa<br>telecom electricity roads<br><!-- End of picture text --> _Source:_ Calderón 2009.

4
# **Section I: Introduction**

Evidence from developed and developing countries indicates that there is significant gender segregation within the labor market, with women more likely to work in low‐productivity sectors, less profitable businesses, or be relegated to unpaid family employment or the informal wage sector (World Bank, 2011; ILO, 2012; Dolado et al; 2003). In entrepreneurship, women‐owned<sup>2</sup> businesses typically underperform men’s (see Klapper and Parker, 2011, for an overview of this literature). While female participation in entrepreneurial activities varies by region, the gaps in productivity and growth between male and female‐owned enterprises are stark and remain large even in rich countries (World Bank, 2011; Klapper and Parker, 2011). Occupational segregation also comes with a growth cost. If we make the not unreasonable assumption that skills are equally distributed across men and women at start, the growth of an economy will be reduced by gender segregation of employment as a given sector does not have access to the full set of skills that exist. Focusing on enterprises in developing countries, Bruhn (2009) finds that female‐owned firms throughout Latin America tend to be smaller than male‐owned firms in terms of sales, number of employees and profits. Similarly, Bardasi, Sabarwal, and Terrell (2011) reveal large gaps in firm size in Europe and Central Asia, Latin America, and Sub‐Saharan Africa. And research in Latin America and Asia found that only one in ten firms growing to at least 15 employees is women‐owned (Kantis, Angelelli and Koenig, 2005).

In Africa, female participation in entrepreneurial activities is higher than in any other region, with women representing half of non‐farm business ownership (Hallward‐Driemeier, 2011). However, these entrepreneurship opportunities have yet to lead to equal earnings between women and men. A range of studies show female‐owned firms lagging behind their male counterparts in a number of key measures of enterprise performance. In terms of revenue, Bardasi, Sabarwal and Terrell (2011) indicate that female‐ owned enterprises in Sub‐Saharan Africa have sales that are 31 percent lower than male‐owned firms. For productivity, Hallward‐Driemeier (2011) estimates the gender gaps in labor productivity in Africa to be 6 to 8 percent, and Costa and Rijkers (2012) analyze rural non‐farm entrepreneurship and find that male‐ owned firms in Ethiopia are three times more productive than female‐owned ones. Finally, our examination of summary statistics from the Uganda National Household Survey (2005/2006) indicates

> 2 See Hallward‐Driemeyer and Aterido (2011) for a discussion on the importance of the definition of female ownership between partial ownership and decision‐making. In the setting of this study 90 percent of the enterprises only have one owner. For those in partnership, female ownership is defined in this study as the main business owner being a woman.

2
support intervention (0.05 SD) (Banerji et al., 2015) or for children in Ecuador who were randomly assigned to kindergarten teachers (0.10 SD) (Araujo et al., 2016).<sup>24</sup> At the 36-month follow-up (when the children were 6-8 years old), using a complex battery of child assessments, we found no sustained treatment effects on child development outcomes. Thus, the program did not result in higher levels of school readiness for primary school, despite the early indications of improvements in child development indicators, parental involvement, and improvements to the classroom environment. The reasons for the fade-out of effects may have been that once the children graduated from the CBCCs, they were then absorbed into a primary school system with low quality that could not provide an appropriate or beneficial learning environment for the children to build sustainably on what they had learned in the CBCC.

The primary weakness of our approach has to do with the quality of implementation of the program. Although our approach was grounded in the real-life conditions of rural Malawi, the success of the intervention relied on a volunteer workforce with low levels of education and formal training, which may have had consequences in terms of intervention effectiveness. A second weakness is that we are not able to identify the effects or cost-effectiveness of parenting support alone, since the group-based parenting support sessions were delivered by the newly trained preschool teachers, relying on the teacher training intervention. While this approach made the intervention easier to administer and more likely to be scalable in this resource-constrained environment, it ruled out isolating the direct effect of parenting support alone. A third weakness is that we had a short intervention period that lacked intensity, and outcomes may have been better if regular refreshers supplemented the interventions across early childhood in addition to the short intervention period. Fourth, other than the SDQ, we have no child 24 Our assessments are comparable with other studies, such as the study of the Ecuadorian children (Araujo et al., 2016), which also used the TVIP, in addition to tests of vocabulary and comprehension from the Woodcock-Johnson battery of child assessment tests; our tests are also comparable to the Chilean study, which used subtests of the Woodcock-Munoz, and other letter-word recognition tasks (Yoshikawa et al., 2015).

32