rafmacalaba/datause-encoder-data huggingface dataset:
- (i) some formal legal requirements both (a) to underpin the regulatory agency and (b) to set out the powers and duties of the regulatory agency;  and

- (ii) regulatory processes that promote consistent and reliable decisionmaking.<sup>9</sup>

In the empirical work that we discuss below, our measures of regulatory governance will, perforce, be limited to four indicators of the formal legal aspects of the regulatory agencies<sup>10</sup> .

# **2.2 Output Measures for Utility Regulatory Agencies**

For utility service industries, there is a major issue in defining appropriate output measures for utility regulation. For all countries, rich and poor, a relevant output is higher levels of (and faster growing) technical and efficiency as well as quality of service. However, whereas virtually all developing countries need significant _increases_ in capacity to meet demand – at least in electricity supply, the same is by no means always true for rich countries. One of the main drivers of the liberalization plus privatization plus independent regulation OECD electricity reform model has been the desire to _reduce_ unnecessarily high capacity reserve margins as well as to reduce investment costs.

This issue is important since _significantly higher investment (and private investment)_ was the single most important reason cited over the last 15 years by the World Bank and similar policy institutions for the promotion of independent regulatory agencies in electricity and similar utility service industries<sup>11</sup> . This view goes back to the underlying time-inconsistency problem and the question of how, given limited tax resources, developing countries can increase capacity and reduced unserved demand – particularly for countries with poor reputations as regards their treatment of private investment. Hence, an independent regulatory agency has been advocated as the way in which private investors can be assured that they will be able to earn a reasonable rate of return.

**_In consequence, on this hypothesis, it is to be expected that sizeable increases in private investment flows (domestic and foreign) will arise in developing country electricity and similar industries following the establishment of an independent regulatory agency._** It is, however, worth noting that the speed at which the regulatory credibility is established is very unclear.  It is likely to take some time, so that one might well expect lags of some years between the establishment of the new regulatory agencies and any significant increase in investment.

> 9 See among others Levy and Spiller (1994), Smith (1997), Stern and Holder (1999), Noll (2001).  For a full discussion, see Stern and Cubbin (2003).

> 10

> See Cubbin and Stern (2004) for a discussion of the implications of having only these as measures of governance.

> 11 The World Bank’s 1994 World Development Report “Infrastructure for Development” is a good example.  See Chapter 3.

6
Table 9. Indonesian Exports, ICT use, and Log of Skills Measures. Low-educated workers. 2005-2010

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000186||0.000158|0.000113|0.000128||0.0000995|-0.0000229|-0.000122||-0.00009|0.000111|
||(0.000205)||(0.000209)|(0.000292)|(0.000203)||(0.000207)|(0.000284)|(0.000191)||(0.000196)|(0.000274)|
|Share of hhlds with computer||0.0205|0.0456|0.0668||0.00874|0.0358|0.0565||-0.00310|-0.0333|-0.0548|
|||(0.0535)|(0.0437)|(0.0496)||(0.0497)|(0.0362)|(0.0432)||(0.0502)|(0.0338)|(0.0433)|
|IDN exports per worker *||||0.0000318||||0.000509||||-0.000981|
|Share of hhlds with computer||||(0.000783)||||(0.000705)||||(0.000691)|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.903|0.902|0.903|0.903|0.530|0.528|0.530|0.530|0.830|0.829|0.831|0.830|
|||M|anual|Routine ski|ll measures|Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.000675||-0.000641|-0.00035|-0.0000586||-0.0000353|0.000409|||||
||(0.000236)***||(0.000236)***|(0.000351)|(0.000194)||(0.000197)|(0.000312)|||||
|Share of hhlds with computer||-0.0771|-0.104|-0.124||0.0100|-0.0126|-0.0226|||||
|||(0.0794)|(0.0763)|(0.0837)||(0.0528)|(0.0392)|(0.0398)|||||
|IDN exports per worker *||||-0.00156||||-0.0026|||||
|Share of hhlds with computer||||(0.00123)||||(0.00101)***|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.974|0.974|0.974|0.974|0.843|0.843|0.843|0.843|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Low-educated workers: workers with uncomplete secondary education or less. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

30
**Table** **<u>9b</u>** <u>:  Estimation of income diversification (D) and household welfare, urban areas</u>

|**Dependent Variables:**||**1990/91**|||**1995/96**||
|---|---|---|---|---|---|---|
|Income Diversification (D) and log of per<br>capita consumption(Cons)|1<sup>st</sup>stage<br>(1) D|IV<br>(2) Cons|OLS<br>(3) Cons|1<sup>st</sup>stage<br>(4) D|IV<br>(5) Cons|OLS<br>(6) Cons|
|D||-0.63|-0.46||0.76|0.27|
|||(1.2)|(7.8)***||(3.5)***|(13.5)***|
|Head sex (Male)|-0.04|<br>0.08|<br>0.14|-0.08|<br>0.23|<br>0.11|
||(2.0)**|(1.3)|(4.9)***|(5.2)***|(4.7)***|(5.3)***|
|Head age|-0.02|0.24|0.27|0.02|0.14|0.16|
||(0.5)|(3.3)***|(5.7)***|(0.7)|(3.1)***|(5.4)***|
|Age Squared|<br>0.00|<br>-0.02|<br>-0.03|<br>0.00|<br>-0.02|<br>-0.02|
||(0.8)|(2.9)***|(5.6)***|(0.1)|(3.5)***|(5.2)***|
|Head education primary|<br>-0.02|<br>0.24|<br>0.27|<br>-0.10|<br>0.26|<br>0.10|
||(0.6)|(3.2)***|(5.6)***|(3.5)***|(3.5)***|(2.8)***|
|Head education secondary or higher|-0.12|0.34|0.52|-0.20|0.54|0.22|
||(3.2)***|(2.5)**|(10.4)***|(6.7)***|(4.8)***|(5.8)***|
|Household size|<br>-0.17|<br>-0.40|<br>-0.14|<br>-0.17|<br>0.09|<br>-0.17|
||(21.7)***|(2.5)**|(13.0)***|(26.5)***|(1.1)|(19.8)***|
|# Boys|0.06|0.15|0.06|0.05|0.00|0.08|
||(4.6)***|(2.5)**|(3.6)***|(5.3)***|(0.1)|(6.5)***|
|# Girls|<br>0.05|<br>0.14|<br>0.07|<br>0.05|<br>0.02|<br>0.09|
||(4.0)***|(2.7)***|(4.1)***|(5.3)***|(0.5)|(7.5)***|
|# Male adult with primary education|<br>0.01|<br>0.00|<br>-0.02|<br>0.03|<br>-0.02|<br>0.02|
||(0.7)|(0.0)|(0.8)|(1.9)*|(0.6)|(1.0)|
|# Male adult with sec./ higher education|0.03|0.11|0.06|0.06|0.02|0.11|
||(3.0)***|(2.8)***|(4.0)***|(6.4)***|-0.7|(9.6)***|
|# Female adult with primary education|<br>-0.01|<br>0.04|<br>0.05|<br>0.01|0.05|<br>0.07|
||(0.5)|(1.0)|(2.1)**|(0.6)|(1.9)*|(3.8)***|
|# Female adult with sec./ higher education|0.00|0.20|0.20|0.01|0.18|0.20|
||(0.1)|(7.6)***|(11.2)***|(1.2)|(8.2)***|(14.6)***|
|Per capita asset holding|0.01|0.30|0.28|0.04|0.26|0.32|
||(2.0)**|(17.9)***|(34.6)***|(7.0)***|(11.8)***|(42.9)***|
|Access to credit (yes)|<br>0.01|<br>0.03|<br>-0.04|<br>0.14|<br>0.19|<br>0.39|
||(0.1)|(0.3)|(0.6)|(4.1)***|(2.1)**|(9.1)***|
|Secondary city (yes)|0.08|-0.12|-0.32|0.12|-0.55|-0.37|
||(3.0)***|(0.9)|(11.7)***|(9.6)***|(9.2)***|(23.1)***|
|Bulawayo city (yes)|<br>-0.02|<br>-0.13|<br>-0.25|<br>0.09|<br>-0.25|<br>-0.15|
||(0.6)|(1.5)|(7.7)***|(5.8)***|(6.1)***|(8.3)***|
|Rainfall standard Deviations (planting)|<br>0.47<br>|||<br>-0.21<br>|||
||(2.9)***|||(1.4)|||
|Rainfall standard deviations (harvesting)|0.14|||0.17|||
||(3.6)***|||(2.2)**|||
|Constant|<br>1.23|6.26|4.27|<br>1.26|2.12|4.04|
||(13.5)***|(5.0)***|(36.2)***|(17.0)***|(3.6)***|(52.3)***|
|R Squared (adjusted)|0.46|--|0.50|0.42|--|0.51|
|Observations (N)|4561|4561|4561|7177|7177|7177|

Absolute value of t statistics in parentheses

* significant at 10%; **significant at 5%; *** significant at 1%

24
households in the bottom of the income distribution disproportionally benefit from an increase in the number of workers in their families, as they move towards fertility transition.

The realization of the first demographic dividend, led by reductions in child dependency ratios, could facilitate the eradication of global poverty (figure 4). In 1990, East Asia had a higher average poverty headcount than South Asia. However, poverty headcount rates in East Asia decreased from 61 percent to only 7.2 percent between 1990 and 2012. This poverty reduction was paralleled by sharp reductions in child dependency ratios in the region. For a country perspective, evidence from Bangladesh suggests that demographic factors, including age-structure, gender, and regional distributions of populations, accounted for a quarter of the rapid reductions in poverty between 2000 and 2010 (World Bank 2013). Bangladesh halved its fertility rate between 1971 and 2004, going from more than 6 children per woman to about 3, and is on track to reach replacement rates in the coming decades.

**Figure 4 Lower child dependency ratios are associated with lower poverty rates**

Poverty rate and child dependency ratio, percent <!-- Start of picture text --> 100<br>1990 2012<br>80<br>60<br>40<br>20<br>0<br>EAP SAR SSA EAP SAR SSA<br>Child dependency Poverty headcount<br><!-- End of picture text --> _Source:_ Authors’ estimates _Note_ : Data are from United Nations (2015) and PovcalNet. The poverty headcount rate is based on the $1.90 a day poverty line.

Because of the association between fertility and education, income, and life expectancy, households in the top 60 percent of the income distribution tend to have lower child dependency ratios and to pass through the demographic transition before households in the bottom 40 percent in almost all countries for which data are available (figure 5). As fertility rates fall, the demographic structures of the households change and directly affect poverty and shared prosperity, particularly in poor households. Households in the top of the income distribution tend to have lower child dependency ratios and to pass through the demographic transition before households 19
SPLIT DECISIONS

|Matching|Mahala-<br>nobis|0_._435|(0_._130)|_−_0_._313<br>(0_._0318)|0_._0202|(0_._0227)|0_._616<br>(0_._213)|
|---|---|---|---|---|---|---|---|
|eighbors|10|0_._298|(0_._0922)|_−_0_._317<br>(0_._0195)|0_._0360|(0_._0123)|0_._370<br>(0_._129)|
|g, nearest n|5|0_._355|(0_._0985)|_−_0_._323<br>(0_._0212)|0_._0348|(0_._0138)|0_._298<br>(0_._144)|
|Matchin|2|0_._387|(0_._112)|_−_0_._320<br>(0_._0250)|0_._0164|(0_._0175)|0_._278<br>(0_._175)|
||OLS|0_._406|(0_._0779)|_−_0_._297<br>(0_._0155)|0_._0372|(0_._01000)|0_._212<br>(0_._102)|
||RDD_∗_|0_._684|(0_._435)|_−_0_._229<br>(0_._180)|_−_0_._00862|(0_._0402)|0_._00221<br>(0_._269)|
||Outcome|_Households_<br>ln Expenditures: Educ. & med.||Business (non-agr.)?|_Children (6_⩽_age < 18)_<br>In school?||_Adults (age_⩾_18)_<br>Years of educ.|

45
The main findings of this paper of a high share of voluntary informal workers needs further exploration to better understand the drivers behind these results. Previous studies for developing countries with a high proportion of informality show that high voluntary informality is linked to low valuation of benefits of being formal and workers who prefer higher wages instead of lower wages with certain benefits (Anton 2012; Levy 2008; Maloney 2004; Maloney 1998). From the firm side, literature shows that frail governance, driven by rigid regulations, corruption and inadequate public services (Mbaye and Benjamin 2014; Dabla-Norris et al. 2008; Steel and Snodgrass 2008; Verick 2006; Schneider 2004; Djankov et al. 2002) and lagging economic growth (World Bank 2019; Loayza 2016; Calvés and Schoumaker 2004) are the key drivers of informality. The interlinkages between these factors result in informal firms having little incentives to formalize, thus preventing their employees from accessing the benefits of formalization.

The rest of the paper is structured in the following form. Section 2 describes the data. Section 3 presents stylized facts benchmarking informality in Senegal with respect to regional and global peers, as well as a brief discussion on a recent profile of labor informality in Senegal. Section 4 presents the methodology and results of the analysis on voluntary informality in Senegal, explores the presence of heterogeneous effects and provides some robustness tests. Section 5 concludes.

# **2. Data**

The analysis uses the 2018-19 Harmonized Survey of Household Living Conditions for Senegal (Enquête Harmonisée sur les Conditions de Vie des Ménages, EHCVM) which is nationally representative. The EHCVM was carried out by the West African Economic and Monetary Union<sup>1</sup> (WAEMU) Commission with the support of the World Bank in two collection waves of 3 months each to take seasonality into account. The first wave took place from mid-September to mid-December 2018. The second wave was implemented from April 11 to July 10, 2019. The sample design guarantees

> 1 The WAEMU region is composed of Burkina Faso, Côte d’Ivoire, Benin, Guinea-Bissau, Mali, Niger, Senegal and Togo. The survey program is a cooperative effort between WAEMU, the World Bank, and the national statistical institutes across the WAEMU member countries. See Regional Program to Harmonize and Modernize Living Conditions Surveys (dashboard), World Bank, Washington, DC, https://projects.worldbank.org/en/projects-operations/project-detail/P153702.

3
2 There may be several purposes for measuring growth rates. One pu-Dose may be to see how the structure of an economy has been changing over time; whether the economy has moved from a lower (higher) to a higher (lower) c'rowth path. Although we deal with this issue in the paper, our main focus is on the welfare aspect of growth rates. The main contention of this paper is that if there is an improvement (deterioration) in the growth rate during a period (for instance, 1970 to 1979), then it should imply a higher (lower) welfare level for that period.

Thus, there must exist a one-to-one relationship between growth rates and changes in welfare levels. This paper explores this relationship using alternative growth procedures.

Growth rates of several economic indicators routinely appear in World Bank documents and are given wide circulation in publications such as the World Development Report and the World Bank Atlas. These growth rates are computed by fitting a least-square linear trend line to the logarithmic values of the indicators in the relevant period. This method is used widely not only by the World Bank but also by other international organizations and individual countries.

The issue to be discussea in this paper is whether the widely used least-squares procedure is the appropriate one if our objective is to see how th, economic welfare of people has changed over a period of time. The paper develops a conceptual framework to derive the aggregate growth rate from a welfare function defined in terms of levels of per capita incomes in different years. Using this framework, welfare implications of alternative growth estimation procedures are derived. A new procedure is suggested which captures all the desirable properties of a welfare function.
health-related decision making. Thus, educating men about the reproductive process, disease prevention and the benefits of reproductive health care for both men and women seems to be urgent to reduce, for instance, the growing AIDS epidemic in India.

Men’s knowledge about contraception is important, but its use and effectiveness depend directly on men’s involvement. Several studies have examined the ways in which culture and social organizations influence contraceptive patterns. Research from Ghana (Ezeh 1993) and Nigeria (Bankole 1995) suggests high levels of influence by men over women’s contraceptive decisions. However, the reverse may not be true. Bankole’s analysis (1995) of the Nigerian Yoruba shows that the number of offspring has important consequences for apparent equality in spousal desire for more children. Additionally, there is evidence that men’s preferences have a major direct impact on the first decade of a marriage and the first four children. It seems that men are likely to want more children in families with few members and that women’s wishes prevail with larger numbers of surviving children in the family.

The importance of including men in policy design and research related to reproductive health has been highlighted by other researchers. Bankole (1995) and Dodoo (1993) suggest high probabilities of invalid estimates for unmet contraceptive need in sub-Saharan Africa when these estimates are derived from data collected only on women. In Zimbabwe, although men report having “the final say” in contraceptive use, women are responsible for obtaining contraceptives (Mbizvo and Adamchack 1991). These and other studies demonstrate discordance within couples in terms of contraceptive use. The main conclusion in previous research is that men agree on using contraception for birth spacing purposes but not to limit family size. The fact that men prefer more children than women suggests that reproductive health programs or policies in developing countries should involve both sexes.

Note that most of the conclusions in previous studies are derived from surveys in which individuals stated their preferences about contraception use and knowledge. Stated preferences may substantially diverge from revealed preferences or actual behavior. Thus, additional fieldwork is needed to accurately measure women’s “unmet need,” the difference between women’s fertility preferences and their use of contraception.

10
9 satisfactory” to “satisfactory”. The regions with the greatest rate of failure – viz. “unsatisfactory” to “moderately unsatisfactory” – are Middle East and Northern Africa (32 percent) and Sub-Saharan Africa (25 percent). The region with the highest rate of success – viz. “moderately satisfactory”, “satisfactory” or “highly satisfactory” – is Eastern Europe and Central Asia, at 92 percent.

In addition, we run the regressions using the ratings of the Implementation and Completion Results Report (ICR). The latter cannot be considered independent of the Bank’s management.

# **_3.3 Variables of interest_**

We hypothesize that the success of policy-based loans post-2004 depends heavily on key elements of reform program design. Below we present a short explanation of each; precise definitions are given in Annex 2. Illustrative examples drawn from actual operations are listed in the _Correlates of Success Codebook_ which can be shared upon request.

_Weaker prior actions._ We hypothesize that prior actions or “conditions” agreed upon with the country authorities that are “substantive”, viz. policy- and action-oriented, are more likely to attain their expected goals; and that prior actions that lack policy substance and are less actionoriented are less likely to attain their goals. For example, focusing on process-oriented steps, such as setting up “task forces”, doing studies, and issuing “plans” for the future, may be less likely to attain tangible results, particularly if supported in later stages of programmatic series of loans without actionable policy as follow up. Based on desk reviews of Program Documents, the fraction of weaker prior actions is calculated for each operation.

_Matrix/results consistency_ . The Bank requires development policy loans to be underpinned by a results framework, which provides the basis for monitoring and evaluation. A desk review of Program Documents was performed to construct a variable for each loan and programmatic series of loans that examines the extent to which there is consistency, or a clear “line of sight”, between the prior actions or conditions for the program, the stated objectives of the program, and the intended results/outcome indicators. In the variable constructed, consistency occurs when the program objectives are all measured by meaningful results/outcome indicators, and where there is a clear line of sight from prior actions or conditions for the loan to the intended results/outcomes that are evaluated at the latter stage. If there are no result indicators to assess
# Cities, Slums, and Early Child Growth: Empirical Evidence from Bangladesh

Dhushyanth Raju Kyoung Yang Kim Quynh Thu Nguyen Ramesh Govindaraj _JEL codes_ : I12, I14, I15, O15 _Keywords_ : Bangladesh, urban, slum, undernutrition, stunting, height, linear growth, children

> Raju: <u>draju2@worldbank.org; Kim: kkim7@worldbank.org; Nguyen: qnguyen4@worldbank.org; Govindaraj: rgovindaraj@worldbank.org. We thank Alayne Adams, Karar Zunaid Ahsan, Bushra Binte Alam, Judy Baker, Tekabe</u> Belay, Sadia Chowdhury, Timothy Evans, Jean-Jacques Frere, Nedim Jaganjac, Zahed H. Khan, Iffat Mahmud, Nkosinathi Mbuya, Patrick Mullen, Hossain Zillur Rahman, E. Gail Richardson, Federica Secci, Diana Silimperi, and Owen Smith for helpful suggestions and feedback during various phases of the research. We also thank Naihan Yang for research assistance. Funding from the World Bank’s Bangladesh Health Sector Development Program Multi Donor Trust Fund (TF011554) is gratefully acknowledged.
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