rafmacalaba/data-use-mentions-extended huggingface dataset:
needed for a business, they see government support as inaccessible and unsupportive for business, and most importantly, household and domestic responsibilities limit their participation in business.

**The major impediment to the emergence of women entrepreneurs is the cultural, gender-based traditions regarding the role of women in society.**<sup>12</sup> These include the burden of household and family responsibilities, general lack of respect for women entrepreneurs, and an unwelcoming environment for women entrepreneurs in maledominated sectors. While not only specific to women-owned businesses, SMEs in Myanmar face many challenges related to access and use of financial instruments. Lack of financial service sophistication, short loan tenors, and onerous collateral requirements, are all developmental challenges faced by firms in the country.<sup>13</sup> Moreover, research suggests that women entrepreneurs face specific barriers when dealing with bureaucracy, this is quite relevant in the context of Myanmar. These challenges may arise from lower educational attainment, literacy levels, and underrepresentation of women in senior positions.<sup>14</sup>

**To improve women’s participation in the labor force, especially their access to quality employment, progressive firms have adopted formal family-friendly policies aimed at reducing the gender gap.** When talking about HRM policies, the literature refers to maternity and paternity leave, subsidized childcare, paid sick and annual leave, housing, and food and transport subsidies. The adoption of such policies, especially maternity leave and childcare, is conducive to a higher participation of women in the workforce (Ferragina, 2017), and they are generally associated with positive employment outcomes for mothers (Misra, Budig and Boeckmann, 2010). The She Works report conducted across several different countries, including LICs, highlights how policies such as flexible workplace and leave, child and elderly care, and equal pay for equal work, are all important for the recruitment and retention of female talent.<sup>15</sup> Moreover, the report calls attention to
# **3. The South Sudan Experimental Phone Survey**

In 2010 the World Bank financed a mobile phone-based survey to monitor conditions in South Sudan on a monthly basis and to contribute to knowledge regarding the feasibility and implementation of mobile phone surveys in low-income settings. The rationale for selecting a mobile data collection approach was rooted in context. In early 2010, South Sudan was a year away from the referendum for independence. Given a dynamic situation, setting up a platform to collect high frequency panel data on perceptions, living conditions, and access to services would help shed light on the evolving situation. Additionally, given the poor state of roads and the high costs of travel, mobile phones would offer the potential of a cost-effective solution to collecting high-frequency data by avoiding costly repeat visits to households.

While ownership of modern consumer durables (such as televisions, refrigerators and computers) is uncommon in South Sudan, reflecting low incomes and the lack of reliable access to electricity in homes, ownership of mobile phones has been steadily increasing in both urban and rural areas (Table 2). In 2006 the percentage of households that reported owning mobile phones in South Sudan was 2 percent (5 percent in urban areas and less than 1 percent in rural areas). By 2010 reported household ownership was up to 18 percent, with almost two in three households in urban areas owning at least one phone. (In urban areas, those households that have at least one phone own on average two phones.) The growth in household ownership of mobile phones in the span of four years is quite significant, especially in urban areas, suggesting a growing familiarity with the technology and the likelihood that a good proportion of respondents in urban areas would have used a mobile phone previously.

6
Therefore in the analysis we will allow the parameter β to vary across different types of firms and inputs. Also we will allow for a nonlinear effect of a demand shock on input smoothing by including a quadratic term (ΔYit)<sup>2</sup> in the above equation, as a firm which faces fixed adjustment costs will not adjust inputs initially for small negative shocks, unlike a firm facing convex adjustment costs.

# _Smoothing of labor inputs_

Different versions of the empirical model will be estimated for different indicators of input use. We start with the use of labor inputs. As already presented in Table 1, the CRBS 2009 survey asked the firm by which percentage the number of permanent workers had increased relative to the same period one year before.<sup>16</sup> On average, firms reduced the number of workers. However, most notable is that the firms reduced the input use of unskilled production and non-production workers much more than that of managers, professionals and skilled production workers. This may reflect differences in adjustment costs, as the hiring and firing of skilled workers is typically more costly than that of unskilled workers (Hamermesh and Pfann 1996).

In order to understand to which extent the changes in labor input use are the result of demand shocks, we first estimate model (5) for reported changes of each type of labor input controlling for the demand shock as experienced by the firm. The demand shock is measured as the percentage change in the firm’s total sales (domestic and foreign) in the period since one year ago. In terms of the vector Zit, we include dummies for the type of labor, sector, firm size and location. These dummies control for factors that affect the (relative) adjustments of each type of labor, such as differences in labor-output elasticity across type of labor, changes in relative prices of types of labor, and changes in raw materials and output prices. In the subsequent analysis we will also present estimates where we allow
|**Data Appendix**|**92**|
|---|---|
|Uppsala Confict Data Program Battle-Related Dataset . . . . . . . . . .|. .<br>92|
|Geographical Research On War, Unifed Platform (_Grow_<sup>_up_</sup>) Dataset . . .|. .<br>93|
|Polity IV Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>95|
|FDI data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>96|
|Refugees data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>98|

# **List of Figures**

|1|Violence per Capita and Growth . . . . . . . . . . . . . . . . . . . . . .|8|
|---|---|---|
|2|Locally Afected Population and Growth (African Countries) . . . . . .|9|
|3|Estimated output loss in the period 1946-2010 (cross-country analysis)|12|
|4|Estimated Output Loss in the period 1992-2012 (ethnic group level) . .|16|
|5|Length of Recovery Period on the Ethnic Group Level . . . . . . . . . .|21|
|6|GDP per capita in 2013 and Peace Stability at the Country Level . . .|24|
|7|Confict and Economic Performance . . . .
Policy Research Working Paper 5612

# **Abstract**

Many countries in the MENA region have established partial credit guarantee schemes to facilitate SME access to finance. These schemes can play an important role, especially in a period where MENA governments are making efforts to improve the effectiveness of credit registries and bureaus and strengthen creditor rights. This paper reviews the design of partial credit guarantee schemes in MENA, and assesses their preliminary outcomes. The paper is based on a survey conducted in 10 MENA countries in early 2010. The authors find that the average size of guarantee schemes in MENA (measured by the total value of outstanding guarantees) is in line with the international average, although there are wide differences across countries, and some schemes seem too small to make any significant impact. Most importantly, the number of guarantees looks generally small while their average value looks large. This suggests that guarantee schemes are not yet reaching the smaller firms. Guarantee schemes in MENA look financially sound and most schemes have room to grow. However, this growth should be accompanied by an improvement of some key design and management features, as well as the introduction of systematic impact evaluation reviews.

This paper is a product of the Financial and Private Sector Development Unit, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at rrocha@worldbank.org, zarvai@worldbank.org, and ysaadanihassani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and
.31**|**2.50**|**2.57**|

_Source: Authors estimates based on HIECPS 2005-2008 and regional CPI indice). Note: Table uses fixed household size to construct constant panel weights._ This 7 percent growth in real average household welfare translated in poverty reduction, but not at the same rate for everyone. Table 3 presents assessment of poverty changes based on poverty line defined in World Bank (2007). The “lower poverty line” used here represents the cost of the minimum subsistence basket comprising food and non-food goods and services. The poverty headcount moved from about 23 percent in 2005 to about 19 percent in 2008. Poverty reduction was especially rapid in urban areas. However, large standard errors mean that only for Egypt as a whole change in poverty was significant at high confidence level (95 percent).

> 15 Notably, allowing for panel design does not change the magnitude of standard error significantly (3rd and 4th columns).  Hence, we can rely on simple survey methods to calculate standard errors (available as standard command in Stata).

10
region. Future research will explore the implications of economic diversification for gender equality and women’s economic empowerment.

> 32 See World Bank (2011).

> 33 Most African countries are slated to miss the first Millennium Development Goal of halving poverty by 2015, despite the impressive boom in commodity prices over the last decade. 34 See World Population Prospects, the 2010 Revision, United Nations, Population Division, Population Estimates and Projections, United Nations (2011).

26
# **6.6 Effect of Information on Self-Reported Spending Plans**

There are several possible interpretations of this result. One possibility is that individuals update their _intended_ behavior but cannot follow through on their consumption plans, for example due to self-control problems or liquidity constraints. Since our survey collected data on spending plans, we can test this hypothesis directly. We estimate equation (6) using selfreported spending plans measured post-treatment as the dependent variable, rather than actual spending observed in the credit card data. The results show that individuals do not change their stated spending plans in response to information about inflation or the exchange rate, as shown in Table 7. For reference, columns (1) and (2) reproduce the treatment effects of information shocks on the inflation and exchange rate expectations. In columns (3) through (6), we report the results of estimating equation (6) using self-reported spending plans for the four specific sub-categories of spending. Each of these outcomes is measured on a subjective 3-point scale that takes the values -1 (if the respondent anticipates spending less in the future), 0 (if they anticipate spending about the same), or +1 (if they anticipate spending more). Each outcome in columns (3) to (6) of Table 7 corresponds to the survey equivalents of the consumption behavior measured with administrative data in columns (3) to (6) of Table 4. In column (3), the dependent variable is the stated intention to increase or decrease spending on durable goods. The prediction from the macroeconomic model is that higher inflation expectations should increase intended spending on durables. We find no evidence of such an effect. The impact of increased inflation expectations on total consumption is close to zero (-0.006), statistically insignificant and small in magnitude: a 1 pp inflation shock is associated with a reduction in expected durable spending of only 0.001 standard deviations. In column (4), the dependent variable is intended future
used for the adjustment and the application of the adjusted poverty line. The second component depends on the difference in the share captured by those items between the two surveys. Both errors become zero if the approach is implemented for the same population at the same time. In the usual case though, neither the population nor the time point is the same. In these cases, especially the second component of the error leads to large variation of the performance. Even though the consumption shares of the items can change, the changes might cancel out leading to a good performance of the approach. However, adding one more item to the consumption module can void the cancellation, leading to a worse performance. Without knowing the share of the items from total consumption (which is not measured), it is impossible to predict how many items should be selected for a good performance of the approach.

# **Application to Kenya: Rapid vs. Cross-Survey Estimation**

In 2015/16, a CAPI pilot was implemented alongside KIHBS 2015/16 using the rapid approach. While the configuration is conservative with only 30 percent time savings, the results show impressive performance for all FGT measures (Figure 3) in comparison with the full consumption as estimated for KIHBS 2015/16. Across all potential poverty lines, the rapid approach has a bias of below 1.3 percentage points for FGT0, 0.7 percentage point for FGT1 and 0.6 percentage point for FGT2. The Gini has a bias of only 0.012.

We compare the performance with a cross-survey imputation of consumption using a structural model built based on the KIHBS 2005/6 data set and applied to KIHBS 2015/16, ignoring the collected consumption data in 2015/16. The performance of the structural model is then assessed against the KIHBS 14
|House has improved floor|8.2|3.5|4.7|
|---|---|---|---|
||(2.0)|(3.0)|(4.1)|
|House has radio|39.8|42.3|-2.5|
||(2.4)|(4.6)|(5.2)|
|House has television|5.0|2.7|2.4|
||(2.2)|(2.4)|(4.6)|
|**Household composition and ethnicity indicators**||||
|Two+ children under 5 yrs|13.1|15.5|-2.4|
||(1.3)|(6.1)|(3.8)|
|Religion is Hindu|86.3|97.2|-10.9|
||(3.5)|(2.5)|(7.0)|
|Ethnic group||||
|Brahmin|14.2|11.0|3.2|
||(1.8)|(2.6)|(3.8)|
|Chhetri|20.1|43.1|-23.0<sup>**</sup>|
||(4.4)|(13.3)|(10.6)|
|Occupational|17.8|15.6|2.2|
||(4.1)|(5.6)|(8.5)|
|All other|47.9|30.3|17.6|
||(7.9)|(20.8)|(18.2)|

Notes: Weighted
Note that _θ_ controls the thickness of the top end of the income distribution, which is a key determinant of income inequality; the smaller the value of the tail index _θ_ , the larger the proportion of high incomes, the higher the value of inequality. Under the assumption that top incomes are Pareto distributed, the mean top income level takes on the following form: This mean top income level features in the computation of the top income shares as well as the computation of the between-inequality components.<sup>8</sup>

# **2.2.2 Population underlying top income database is unclear**

Let us next address the challenge that emerges when the data underlying DB-2 are not necessarily representative of the whole population (i.e. households with incomes exceeding _τ_ ). Consider the possibility that DB-2 has “over-sampled” some and “under-sampled” other households among the top earners, such that DB-2 no longer yields a consistent estimator for _F_ 2( _y_ ) unless some corrective efforts are made. This is a rather realistic scenario as the data may constitute a series of transactions or listing prices rather than a proper sample drawn from the target population. For ease of exposition we will assume that DB-2 observes actual household incomes and not predictors of income, so that we may focus exclusively on the challenges presented in this section.

We will assume that the data are representative for selected sub-populations and that a representative “sample” can be obtained by anchoring DB-2 to some known population totals. Suppose that the target population can be sub-divided into _D_ districts with _d_ = 1 _, . . . , D_ indicating the district. The top income distribution for district _d_ will be denoted by _F_ 2 _,d_ ( _y_ ) = _Pr_ [ _Y ≤ y|Y > τ, district d_ ]. By extension, let _F_ 1 _,d_ ( _y_ ) = _Pr_ [ _Y ≤ y|Y ≤ τ, district d_ ]. Using this notation the complete income distribution for district _d_
. Since its launch in 2008, the LSMS-ISA project has worked with statistical offices in eight partner countries in Sub-Saharan Africa, providing technical assistance to design and implement multi-purpose household panel surveys with a strong focus on agriculture, while promoting innovation and efficiency in data collection methods. Table 5.1 summarizes the partner countries, the panel surveys supported, and the years of survey implementation.

Table <u>5.1 LSMS-ISA Surveys</u>

|**Country**|**Survey**|**Year**|
|---|---|---|
|**Burkina Faso**|Burkina Faso Enquête Harmonisée sur le<br>Conditions de Vie des Ménages(EHCVM)|2018/19, 2022|
|**Ethiopia**|Ethiopia Socioeconomic Survey (ERSS)|2011/2012, 2013/2014, 2015/2016,<br>2018/2019,2021/2022|
|**Malawi**|Third Integrated Household Survey (IHS3)|2010|
||Integrated Household Panel Survey (IHPS)|2013, 2016/2017, 2019/2020|
|**Mali**|Enquête Agricole de Conjoncture Intégrée<br>(EAC-I)|2014/2017|
|**Niger**|National Survey on Household Living<br>Conditions and Agriculture(ECVM/A)|2011/2014|
|**Nigeria**|General Household Survey (GHS)|2010/2011, 2012/2013, 2015/2016,<br>2018/2019|
|**Tanzania**|Tanzania National Panel Survey (TZNPS)|2008/2009, 2010/2011, 2012/2013,<br>2014/2015,2020/2021|
|**Uganda**|Uganda National Panel Survey|2009/2010, 2010/2011, 2011/2012,<br>2013/2014, 2015/2016, 2018/2019,
the degree of progressivity in the existing system _without having had the analyst specify in advance how large this should be._ Such an approach is provided by the decomposition framework proposed by Aronson et al. [8] in the early 1990s, and used in the health finance literature in the late 1990s [6, 7]. In this framework fairness is assessed explicitly in terms of the impact of health care financing on the distribution of income, since this is, after all, the ultimate concern amongst policymakers when they think about financial protection. In the approach, the change in the Gini coefficient for income caused by health care payments is decomposed into terms corresponding to changes attributable to overall share of income absorbed by health care payments, changes brought about by the progressivity (or regressiveness) of the payment structure, changes brought about by horizontal inequities in the system, and changes brought about by households changing their position in the income distribution as a result of health care payments.

In the last part of the paper, this method was illustrated using data on out-of-pocket payments in Vietnam for the years 1993 and 1998. The WHO index simply indicated a move towards greater fairness over the period in question, but this could have been because payments became more progressive, or became less regressive, or became progressive having been regressive, or became regressive having been progressive, or because of greater differences in payments at each income level. With the Aronson et al. approach, a much clearer picture emerged. Over the period in question, health care payments impacted adversely on the income distribution in both years, but the degree of impact was smaller in 1998. This was due in part to
estimated using micro data from household surveys are smaller than those produced by macro studies.

This is especially true for elasticities of electricity access, previously computed in the 0.18 to 0.43 range, while our estimate is at most 0.06 on average and 0.16 for the highest country value (Peru). Similarly, macro estimates for cell phones ownership and internet access are lower than when estimated with household survey data. The closest values are for water and sewerage, although the macro estimates tend to match the highest household data country estimates.

It is probable that this disparity is partly due to the ability of disaggregated household data to control adequately for the prices of infrastructure through country-time fixed effects, while macro data can only include time invariant country fixed effects. Consequently, the difference in estimates appears bigger when dealing with services for which the price matters more and has experienced large shifts over the last two or three decades, such as cell phones, internet access, and to a lesser extent electricity.

Several factors are likely to be key in understanding the differences in how countries have been able to accommodate the demand pressure on infrastructure stemming from growing incomes. First, demand side issues are likely to matter, for example the extent that credit constraints have limited the ability of households to access infrastructure and related equipment.

In addition, on the supply side, it is likely that constraints related to limited investment and institutional capacity are playing a role. Given these likely binding supply constraints, it is enlightening to simply ask what these elasticities mean in terms of the ability of policy makers throughout the region to meet very simple universal access goals, akin to the early MDGs criteria, such as for water “access to a source of water in the house or lot.” Making simple assumptions on future income growth, we can forecast for each country the number of years it would take to offer universal access to a given infrastructure service under a “business as usual” scenario.

Consider very simple mechanical projections,
# **Weather and Child Health in Rural Nigeria**

Mariano Rabassa, Pontificia Universidad Catolica Argentina Emmanuel Skoufias, The World Bank Hanan G. Jacoby, The World Bank _Corresponding author:_ Emmanuel Skoufias, The World Bank (Mail Stop: MC4-415), 1818 H Street NW, Washington DC 20433-USA. tel: (202)458-7539. fax: (202) 522-3134. e-mail: <u>eskoufias@worldbank.org.</u> _Acknowledgements_ : The authors are grateful to Andrew Dabalen and Patrick Hoang-Vu Eozenou for useful comments and to NIMET for making available the historic rainfall station data used in this study. The findings, interpretations, and conclusions in this paper are entirely those of the authors and they do not necessarily reflect the view of the World Bank.
Table A.1: A Summary of the Literature on Effective Tax Rates and Firm Size

|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|---|---|---|---|---|---|---|
|**Reference**|**Data**|**Country**|**Numerator**|**Denominator**|**Firm Size Measure**|**ETR & Firm Size**|
|Yuzhu et al.(2022)|US|Financial data|Cash taxes paid|Pre-tax income|Total asset|Negative|
|Gaertner et al.(2021)|US|Financial data|Cash taxes paid|Pre-tax income|Total asset|Negative|
|Bach et al.(2019)|France|Tax return data|Tax expenses|Net proft|Turnover<br>Number of employees|Negative|
|Laz ̆ar(2014)|Financial data|Romania|Income tax expense|Net proft<br>Turnover|Total assets|No relationship|
|Wu et al.(2012)|Financial data|China|Tax expenses|Net proft|Total assets|Positive|
|Guha(2007)|Financial data|India|Income tax expense|Net income|Total assets|Negative|
|Richardson and Lanis(2007)|Financial data|Australia|Income tax expense|Net proft<br>Turnover|Total assets|Negative|
|Adhikari et al.(2006)|Financial data|Malaysia|Tax expenses|Operating income<br>Turnover|Total assets|Negative|
|Janssen(2005)|Financial data|The Netherlands|Tax expenses