rafmacalaba/data-use-mentions-extended huggingface dataset:
# Chapter 3. Infrastructure Prioritization Framework: An Alternative Approach

The Infrastructure Prioritization Framework is a quantitative multi-criteria approach to withinsector infrastructure project comparison. The IPF condenses project-level indicators (selection criteria) into two composite indices – a financial-economic index (FEI) and a social-environmental index (SEI) – and considers these alongside the budget constraint for a particular sector. Results are displayed graphically to map the projects’ relative expected performance along these two dimensions.

While the IPF is quantitative in nature, it is also policy-responsive, since the government specifies the set of project selection criteria that reflect the sectoral and developmental policy goals. These criteria may include social, strategic, and environmental considerations alongside traditional financial and economic factors. In fact, the IPF was developed by an infrastructure team within the World Bank in response to government demand for alternative decision support approaches that could directly consider key policy goals; be feasibly applied across large projects sets within the resource means of the government; and remain systematic and evidence-based (Marcelo et al., 2016).

IPF is designed to employ quantitative measures to the greatest extent possible to systematize project comparison and limit subjectivity in selection. While the IPF is a multi-criteria approach, it can utilize the results of CBA analysis as a key decision factor. While the approach was initially envisaged for low-capacity governments, its relevance to the Chilean context is demonstrated in the results of this report. In this section, we summarize the IPF prior to presenting the results of its application to the transport and water catchment sectors in Chile.

## The IPF Process

Implementing the IPF is relatively straightforward and follows five steps: (1) selecting decision criteria; (2) gathering related project indicator data; (3) calculating social-environmental and financial-economic indices; (4) plotting projects and budget limits; and (5) comparing projects (see Figure 2). In this section, we summarize IPF application in terms of these steps.<sup>8</sup>

> 8 An extensive technical description of the IPF methodology
the village) and the intensive margin (reported service quality, based on citizens’ ratings of service provision for each type of service on a 5-point Likert scale). Table 7, columns 2–3 report the results for citizens’ perceptions, while column 4 examines actions taken by the village government to improve service provision, as reported in our surveys of village heads. Figure 6 presents RD plots.

Overall, citizens in turnover villages report an increase in both service access and quality: averaging across all services, the reported access rate is 78% in control villages, and this increases by 8 p.p. at the RD threshold (column 2), while the perceived quality of these services increases by 0.21 s.d. (column 3). Both estimates are significant at the 5% level.<sup>12</sup> Consistent with these effects, village governments in turnover villages were more likely to have taken action or made investments in the past twelve months to improve local service quality. Across services, 76% of villages have taken such action to improve service quality, and this increases by 16 p.p. at the threshold (column 4).

Appendix Table A.18 reports effects on the individual components of the two indices of service access and service quality examined in Table 7, columns 2–3. In turnover villages, the most improved service according to citizens is garbage collection, in line with the findings in Appendix Table A.14. Citizens also report improvements in the condition of roads, whereas in the administrative data, we observe improvements in street lighting (Appendix Table A.14, column 5), paved roads (column 6), and public

> 12One concern could be that the sample of citizens, drawn from a snowball process with village officials, may be more favorably inclined towards the government. However, this bias would naturally arise on both sides of the RD cutoff. In Appendix Table A.3, we report balance
**Table 2: Individuals with mobile phone access, 2014**

||All<br>(percent)|Ratio<br>rural /<br>urban|Ratio<br>women /<br>men|Ratio<br>age 45+ /<br>15-24|Ratio<br>bottom40 /<br>upper60|
|---|---|---|---|---|---|
|South Africa|84.2|0.94|0.96|1.04|0.89|
|Botswana|80.0|0.80|1.09|0.86|0.76|
|Kenya|74.0|1.01|0.81|1.09|0.73|
|Nigeria|66.4|0.86|0.72|0.87|0.74|
|Ghana|59.5|0.61|0.95|1.09|0.60|
|Namibia|56.1|0.60|1.04|0.85|0.57|
|Uganda|46.7|0.78|0.61|1.40|0.65|
|Cameroon|44.5|0.31|1.02|0.92|0.53|
|Mozambique|42.5|0.44|0.99|0.77|0.49|
|Tanzania|35.8|0.46|0.74|1.31|0.49|
|Rwanda|24.4|0.38|0.77|0.52|0.36|
|Ethiopia|18.3|0.22|0.42|0.79|0.27|

<mark>Source: Gallup World Poll (various years)
, infrastructure, health, and education) and programs to protect vulnerable groups; stabilizing short-term macroeconomic fluctuations by supporting activity; and serving as a safe asset. Potential costs of debt accumulation are servicing the cost of debt (the interest to be paid to creditors); rollover costs (the costs of refinancing when debt matures); limiting the magnitude and effectiveness of fiscal policy; and crowding out private investment.

As the recovery from the pandemic-induced recession gives way to a new normal, the balance of benefits and costs is likely to increasingly tilt toward costs. As the global economy strengthens, financial conditions are likely to tighten, whether because central banks begin to normalize monetary policy or because investors expect higher inflation. In EMDEs, this may be accompanied by depreciations that put pressure on debt sustainability in those countries with a large share of foreign currency-denominated debt. Even where foreign currencydenominated debt is limited, rising borrowing cost may erode debt sustainability, especially if growth fails to rebound strongly. Record-high EMDE debt makes countries vulnerable to financial market stress. Meanwhile, a recovery in domestic demand and closing output gaps may make additional fiscal stimulus unhelpful.

We now provide a brief discussion of these potential benefits and costs of debt and put them into the current context.

> 4 Total EMDE debt is defined as the sum of EMDE government debt (the GDP-weighted average for 152 EMDEs in 2020) and EMDE private debt (the GDP-weighted average for 128 EMDEs in 2020—private debt data are available on a quarterly basis, and in some countries, data as of Q3 are used).

6
& Cunningham, 2021). All in all, these three groups account for nearly 90 percent of informal employment.<sup>11</sup>

> 9 Both the IMK Survey and the Manufacturing Survey collect data on the number of paid and unpaid workers in an enterprise. The latter – i.e., unpaid workers – are likely informal workers (Marquez-Ramos, 2020). 10 BPS estimates of informal employment are much lower, ranging from roughly 48 to 56 percent of total employment, due to differences in definitions (Wihardja & Cunningham, 2021).

> 11 The remaining 10 percent are comprised of casual workers, wage employees, and unpaid family workers who are not in household enterprises (Wihardja & Cunningham, 2021).

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Policy Research Working Paper 10492

# **Abstract**

This paper aims to provide global evidence on whether and what attributes of laws governing the provision of childcare services affect women’s labor market outcomes. It merges country-year-level data from the World Bank’s Women, Business and the Law database, which documents childcare laws across countries, with data on women’s labor force participation from ILOSTAT. Using a difference-in-difference estimation framework, the analysis finds that the enactment of childcare laws increases women’s labor force participation by 2 percent, on average. Moreover, the effect increases over time, reaching up to 4 percent five years after an enactment. This effect is driven by women who are married, have completed less than primary education, and are between the ages of 35 and 44. Lastly, regulation of the availability and affordability of childcare has a similar impact on female labor force participation, whereas the effect of quality regulation is smaller.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at sanukriti@worldbank.org and ldinartediaz@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 conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced
authors find that the measures of the Doing Business indicators of getting credit and contracts enforcement are positively associated with poverty reduction.

Similarly, Ciccone and Papaioannou (2007) show that in economies where new business registration time is lower, more new firms enter industries undergoing technology shifts and hence benefit from increasing global demand for their products. Darnihamedani et al. (2018) add to the ‘start-up costs - taxes - entry rate’ equation the firms’ propensity to innovate. Using the data on entrepreneurs from 53 countries, the authors find that corporate taxes have a negative relationship with innovative capacity of firms. They also show that higher start-up costs have a significant positive relationship with innovative entrepreneurship, arguing that reduction of entry costs may increase the number of firms. Jerbashian and Kochanova (2016) use industry-level data and World Bank’s Doing Business indicators for 14 OECD countries to demonstrate that high costs of starting a business and registering property decrease investments in information and communication technologies, while the strength of legal rights and of minority investor protection tend to increase such investments.

Bertrand and Kramarz (2002) draw a similar conclusion, finding that cumbersome business entry regulation for the retail sector in France hampered employment in the industry. The authors demonstrate that a burdensome requirement of an approval from a regional zoning board, when either creating or extending any large retail store in the country, decreased retail employment by more than 10%. Bailey and Thomas (2017) run fixed effects regressions using the U.S. data on firm employment and the extent of federal regulations, by industry and from 1998 to 2011. Their research demonstrates that fewer new firms were created and employment growth was slower in industries with heavier regulation. Also, Cacciatore and Fiori (2016) show that business regulation affects firm entry costs, workers’ unemployment benefits and firing restrictions. They use panel VAR estimates for OECD countries to demonstrate the recessionary short-run and expansionary long-run effects of reforms. The authors, thus, prove a strong effect of market deregulation on business cycle fluctuations
local goods, and hence prices and wages, without any wealth eects on labor supply (I assume government purchases are not valued by households). Again, this is very dierent from the cross-region transfer multiplier, which is negative in NC models.

# 5. Quantitative Cross-Region Transfer Multipliers

In this section, I investigate the extent to which standard NK or NC models can rationalize the multipliers estimated in the data. The full models used here are similar to the analytical models presented at the start of Section 4, but the full NK model has Calvo sticky prices/wages rather than rigid prices/wages. The population share of the home region is calibrated to represent a typical US state ( _n_ = 1 _/_ 50 rather than _n →_ 0 as in the analytical model). Other parameters are taken from NS, who present a very similar model except for parameters relating to two features not in their model: the fraction of handto-mouth household ( _ω_ = 1 _/_ 3 ; based on evidence from Kaplan, Violante, and Weidner 2014) and sticky wages that adjust once a year, on average ( _θw_ = 0 _._ 75 ). See Appendix Table 11 for a full list of parameters.

Before comparing model-based and empirical multipliers, it is important to ensure that these multipliers are calculated in the same way. As there are no ocial state-level price data, empirical estimates in Section 3 are produced by deating nominal labor income or nominal GDP by national ination.<sup>40</sup> I call these measured multipliers to distinguish them from the actual multipliers in Proposition 1 (actual multipliers are quantities, which are nominal values deated by local (state-level) producer prices). Measured and actual impact multipliers are very similar in NK models (due to sticky prices), but in NC models with exible prices and persistent shocks, demand shocks increase

> 40Using my own constructed proxy of quarterly state-level prices, I nd little robust
- Jacobs, CIM & Antea Paviotti. 2017. “Social integration of internally displaced people in urban settings.”.

- Jenson, Jane. 1998. _Mapping social cohesion: The state of Canadian research_ . Canadian policy research networks Ottawa.

- JIPS. 2012. “Profile at a glance: Uganda.” https://www.jips.org/uploads/2019/03/ JIPS-ProfileataGlance-Uganda-2011.pdf.

- Landau, Loren B. 2014. “Urban refugees and IDPs.” _The Oxford Handbook of refugee and forced migration studies_ 1.

- Marbach, Moritz & Guido Ropers. 2018. “Not in My Backyard: Do Increases in Immigration Cause Political Violence?”.

- Mels, Cindy, Ilse Derluyn, Eric Broekaert & Yves Rosseel. 2010. “The psychological impact of forced displacement and related risk factors on Eastern Congolese adolescents affected by war.” _Journal of Child Psychology and Psychiatry_ 51(10):1096–1104.

- Miller, Joy. 2007. Uganda’s IDP policy. Technical report Brookings-Bern Project on Internal Displacement.

- Milner, James & Gil Loescher. 2011. “Responding to protracted refugee situations: Lessons from a decade of discussion.”.

- Pham, Phuong N, Laila Fozouni, Abdulrazzaq Al-Saiedi, Kevin Coughlin & Patrick Vinck. 2021. “Association between distress and displacement settings: a cross-sectional survey among displaced Yazidis in northern Iraq.” _BMC Public Health_ 21(1):1–11.

- Pham, Phuong N, Patrick Vinck & Eric Stover. 2008. “The Lord’s Resistance Army and forced conscription in northern Uganda.” _Human Rights Quarterly_ 30:404.

- Pham, Phuong & Patrick Vinck. 2010. “Transitioning to peace: A population-based survey on attitudes about social reconstruction and justice in Northern Uganda.” _UC Berkeley Initiative for Vulnerable Populations_ .

- Pham, Phuong, Thomas O’Mealia, Carol Wei, Kennedy Kihangi Bindu, Anupah Makoond & Patrick Vinck. 2021. “Hosting new neighbors:
Another challenge is that of girls in rural areas having less access to menstrual hygiene management technology and private sanitation facilities compared to girls in urban areas, which can cause them to miss school during their menstrual periods (Adukia, 2017; Kazianga, Levy, Linden, & Sloan, 2013). With both examples, geolocated data together with household and industry surveys could be useful in assessing the incidence of these challenges along the rural-urban continuum and how to address them.

The third and final element to be considered is that of differences in labor demand along the urbanization continuum, which have heterogeneous effects on children’s decisions to attend school. Children are more likely to participate in school when there is an increase (perceived or real) in the educational skill premium, which is likely to be greater in urban areas due to access to more jobs that require differing levels of education, as they did after the introduction of call centers (Jensen, 2012) or the construction of paved roads connecting rural areas to nearby urban areas in India (Adukia, Asher, & Novosad, 2020). These perceptions – which are usually lower in rural areas – affect household investments in education. The urban–rural dichotomy is a poor indicator for this, since access to markets may vary more based on access to urban areas than on whether one is actually based in an urban area. New research can and should explore the reverberations of changes in the educational skill premium in urban centers as one moves further and further away from them, and how the effect of distance is mediated by the size of the urban center of reference.

Alternatively, if changes in labor demand increase the opportunity cost of attending schools, then educational participation can decrease. This happened, for example, after the introduction of manufacturing jobs in Mexico (Atkin, 2016) or an employment guarantee program in India (Adukia, 2020). Understanding the distinct opportunity costs related to school participation across the ruralurban continuum, and how curricular materials can be designed to match the
population (and not just those who have a job) our data can be used to study changes in employment in response to the policy.

We also observe in the data the employer of the worker (or self-employment). For employers with double-entry bookkeeping we can merge balance sheet information from the Hungarian tax authority.<sup>19</sup> We restrict our main analysis to men because there was a change in retirement rules affecting women throughout the period studied here. We document women’s employment responses in Section 7.1.

An individual is defined to be a private sector employee if she is employed on the 15th of a month at a private sector firm with double-entry bookkeeping.<sup>20</sup> We include part-time workers, but adjust the employment indicator by working hours (e.g. working 20 hours per week is considered as 0.5 employment). Our main outcome in the wage regression is the (full-time equivalent) net wage as of May of each year. We follow Saez, Schoefer and Seim (2019) and define net wage (sometimes abbreviated to wage) as wage earnings net of employer payroll tax. This net wage measure is calculated before income tax and employee social security contributions are deducted and includes base payment, bonuses and overtime pay.

Appendix Table B1 provides a comparison of employment statistics based on the administrative data we use with official statistics which are based on the Hungarian Labor Force Survey. These statistics are very similar, indicating the reliability of the employment indicators we based on the administrative data.

We generate firm-specific indicators that we use in the heterogeneity analyses. Our baseline indicator of firm quality is the value added-based total factor productivity (TFP).<sup>21</sup> As another indicator of firm quality, we perform an Abowd, Kramarz, Margolis (AKM) style decomposition of wages (Abowd, Kramarz and Margolis, 1999) and calculate firm wage 19The monthly labor force status and wage indicators originate from the Hungarian Social
Program (RWSSP) there is still an urgent need to reconfigure the institutional framework and retool the sustainability approach to achieve a lasting and high-quality delivery of water services in Tanzania.

# **3. Literature Review**

In recent years, statistics on water points functionality have been collected in a number of countries. One of the more comprehensive database is Akvo (2015) developed by the the Akvo Foundation. It is currently monitoring data for over 120,000 water points across 37 countries. Globally, 20 percent of the water points are not functional while an additional 10 percent are functional but with problems<sup>9</sup> . Other studies indicate that in Sub-Saharan Africa, it is estimated that about 30 percent to 36 percent of water points are not functional (Baumann, 2009; RWSN, 2009) with countries such as Cote d’Ivoire (65 percent) and Sierra Leone (65 percent) experiencing high rates of water points failures and countries such as Madagascar (10 percent) and Benin (22 percent) experiencing lower levels (Table 1, Annex)<sup>10</sup> .

Another problem is that there is no widely agreed upon definition of functionality. This is an important aspect of data accuracy, because the inaccuracy of the data collected – especially when the definition of functionality is not clearly defined – could adversely affect programs that are designed based on flawed data (Jimenez & Perez-Foguet, 2010). While most surveys use the binary distinction (Cairncross et al., 1980) of working or not working, which at times is considered insufficient (Carter & Ross, 2016), other surveys included the concept of partial functionality (Wilson et al., 2016). Initiatives such as the Akvo Flow and the Water Point Data Exchange (WPDx)<sup>11</sup> were created to not only collect information and indicators on water points across different countries but also harmonize the data definitions which are often collected and measured differently. A recent World
poverty-ridden conditions and one half earned less than one dollar per day. Nearly half of the population had no access to healthcare services, and 58 percent did not have access to drinking water. Furthermore, risks are widespread in Haiti, as people regularly face various forms of covariate shocks—such as increases in food prices, hurricanes, floods, droughts and irregular rainfall—as well as a number of idiosyncratic shocks such as human or animal diseases<sup>10</sup> , casualties or death of a household member.

In the wake of the 2010 earthquake, Hurricane Tomas, the cholera epidemic, the rising prices of basic foodstuffs, and the socio-political uncertainty following the election results of

> 9 One explanation is that, at the time of the February survey, households who got aid shared with those who did not. So, this may muddy the observed relationship between the food consumption score and access or not to food assistance.

> 10 Haiti has had several covariate shocks on animal and plant diseases in recent history. However, declaration of households concerned here their own animals.

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