rafmacalaba/data-use-mentions-extended huggingface dataset:
In this paper, we investigate how the impacts of infrastructure change when considered in isolation or, instead, when bundled together. Moneke (2020) is one of the few studies to have considered interactions of infrastructure investments. Using data from Ethiopia, he shows that the combination of market access provided by roads and the positive productivity effect of electrification on non-agricultural production is key for the achievement of large effects of infrastructure on economic development. Our paper complements Moneke (2020) by expanding the geographic analysis to 27 countries, and by considering the outcomes at household rather than district level. To the best of our knowledge, our paper is the first to study the impact of roads and electricity and their interaction at the continental level in Africa, and to document significant heterogeneities. We find that the effects of isolated and bundled infrastructure differ significantly - between lagging and more advanced regions, across gender, and among various age groups. We complement the findings from the literature that lagging and leading regions benefit differently by documenting and comparing these impacts when investments are bundled and when they are isolated. For instance, Mu and van de Walle (2011) find that, in Vietnam, benefits from rural road improvements are greater in poor communities, where levels of initial market development are lower. Moreover, we find that improved infrastructure in rural areas induces a shift away from agricultural and other lowskilled jobs into more skilled occupations; and, by contrast, that improved infrastructure in urban areas promotes all types of employment. These differential effects suggest that the structural transformation brought about by road and electricity expansions is primarily a rural phenomenon. These findings are new.

The remainder of this paper is organized as follows: Section 2 describes a simple conceptual framework of infrastructure and job creation, and findings from the related literature. Section 3 presents data sources. Section 4 discusses identification strategies. Sections 5 and 6 present findings on the average and heterogeneous effects of employment outcomes from infrastructure investment, with a special focus on the contrasts between the effects on urban and
access to the internet allows facilities to have better information, reach more patients, and better manage their supply chains. This paper aims to contribute to the literature by exploring the extent to which more and better infrastructure might improve the quality of health services provision in low- and middle-income countries by studying the Kenyan context.

Kenya has made great progress in increasing the availability of health care services in the last decade, but much remains to be done (WHO, 2017). A child born today in Kenya is likely to only achieve 55% of her/his potential according to the latest update of the Human Capital Index (World Bank, 2020), which includes measures of child and adult survival rates, stunting and access to and quality of schooling. While Kenyan county governments have prioritized investments in the health sector, especially in the construction of new health facilities and purchase of medical equipment and ambulances, not many efforts have been made to ensure that health facilities have access to reliable infrastructure (Mugo, et al., 2018).<sup>4</sup> According to the latest 2018 Service Delivery Indicators (SDI) report, only 56% of primary-level health care facilities have access to stable electricity and only 15% of health facilities use ICT for supply chain management.

Improving the access and quality of infrastructure remains a challenge in Kenya. Despite an increase in access to energy by 50 percentage points over the past ten years, which has translated into 70% nationwide coverage, 30% of the population still has limited access to reliable electricity (World Bank, 2021). The latest Rural Access Index in Kenya shows that 56.8% or about 13.4 million rural residents are still unconnected to

> 2 See Bleakley, 2010; Case, Fertig, & Paxson, 2005; Conti, Heckman, & Urzua, 2010; Knudsen, Heckman, Cameron, & Shonkoff, 2006.

> 3 Within Millennium Development Goals (MDGs), from 2000 to 2015, there was an increase
Policy Research Working Paper 9131

# **Abstract**

Infant mortality among Hindus is higher than among Muslims in India, and religious differences in sanitation practices have been cited as a contributing factor. To explore whether religion itself is associated with differences in sanitation practices, this study compares sanitation practices of Hindus and Muslims living in the same locations using three nationally-representative data sets from India. Across all three data sets, the unconditional religion-specific gap in latrine ownership and latrine use declines by approximately two-thirds when conditioning on location characteristics or including location fixed effects. Further, the estimates do not show evidence of religion-specific differences in other sanitation practices, such as handwashing or observed fecal material near homes. Household sanitation practices vary substantially across areas of India, but religion itself has less direct influence when considering differences between Hindus and Muslims within the same location.

This paper is a product of the Knowledge and Strategy Team, 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 adukia@uchicago.edu.

_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 by the Research Support Team
A further factor in panel data spanning more than two periods is potential autocorrelation. Wooldridge (2002) suggests first differenced estimators may be preferable to the pair-specific dummies. An alternative is to employ the estimator developed by Baltagi & Wu (1999). This allows one to account for autocorrelation in the error term by specifying a first-order autoregressive process for the error terms, which we do here.<sup>16</sup> A potential rationalization of autocorrelation is lags in the implementation of agreements after signing and in the trade response to the implementation. For this reason, like Baier & Bergstrand (2007), we run specifications with lagged values of the FTAs.

Like most datasets spanning a big range of countries, about 20% of our observations are dropped due to zero or missing trade flows. If the factors driving the recording of positive trade flows also drive the size of those trade flows, there can be bias in the estimates. This can be problematic if the proportion of missing observations is large and, in our application, if we think the bias would affect one type of agreement more than another. Like Baier & Bergstrand (2007), we do not take explicit account of the missing observations and at the same time acknowledge that the use of a reliable method that does so would be a welcome addition to this body of research.<sup>17</sup>

# **5. Results**

This section presents regression output (Table 2) together with comparative statics (Table 3). The first column of Table 2 replicates the result from Baier & Bergstrand (2007: table 5, column 1), which shows a significant positive coefficient on the FTA dummy. The remaining columns distinguish between agreements signed between Northern countries (NN), between Southern countries (SS) and between Northern and Southern countries (NS). In column 2, all three dummies are significant. In other words, regardless of whether the agreement is signed between two developed countries, two developing
.686|0.741|0.126|0.128|
|Observations|71,760|71,760|52,624|52,624|
|Mean Dep. Variable|0.732|0.509|0.003|0.003|
|_Controls (All Panels)_|||||
|Grid FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

_Notes:_ Panel A presents the results of the main specification outlined in equation 1. Panel B shows the results for the non-inferiority test proposed by Bilinski and Hatfield 2018. These results detail the main specification while controlling for an indicator variable representing differential linear pre-trends between the treatment and control groups. SID represents standardized values of the inverse distance from each grid to the refugee camps, and I(Post2017) is an indicator variable with a value of 1 post-2017, marking the onset of substantial Rohingya inflows to Bangladesh. Dependent variables include: (i) Night light density [0 to 63]: This variable represents the average light density at the grid level, obtained by averaging across pixels at the desired level of aggregation. Values range from 0 (no light) to 63 (maximum light density). (ii) Night light density Sin*: This variable is the inverse hyperbolic sine transformation of the original night light density variable, interpretable as a logarithm transformation. (iii) Deforestation _Ha/Km_<sup>2</sup> : This variable corresponds to landsat images from the Hansen Global Forest Change, focusing on identifying forest loss defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. The measure is expressed in hectares per square kilometer. (iv) Deforestation (Sin*): This variable represents the inverse hyperbolic sine transformation of the original deforestation variable, also interpretable as a logarithm transformation. The estimates account for grid and year fixed effects, with clustered standard errors reported at the grid
pendix Table A2 shows that these individuals who remain have significantly lower monthly expenditures at baseline.

The primary analysis therefore focuses on the endline survey, conducted entirely after the intervention ended in November-December 2014. Table 2 shows there was no statistically significant difference in attrition, nor any selective attrition by particular characteristics. The raw levels of attrition, similar to the control group from the midline survey, indicate that the difference observed in the first wave was driven by particularly low attrition in the cash group. This is consistent with the conditionality of the cash transfer driving the low level of attrition. The 2,855 households interviewed at baseline that were interviewed again at endline will make up our main sample.

At endline, we also interviewed new households with a newly pregnant woman or infant born since baseline. This was done primarily to measure the spillovers associated with the information intervention, given that these women would not have been eligible for the cash intervention,<sup>8</sup> but may have elected to participate in the information sessions.

The baseline data indicate that the women and households in our sample are particularly disadvantaged. More than 70% of women interviewed never attended any type of formal school, and about the same number are illiterate. This is somewhat different from data from the 2011 Nepal DHS, where only 44% of women in rural areas who would be considered eligible (by nature of being pregnant or having a child under 24 months old) never attended any school, and only 38% are illiterate. PAF in general targets especially poor areas with little economic development, and this likely explains the difference between women in our sample and women in rural areas from the nationally representative DHS survey.

The level of knowledge among eligible women at baseline indicates that there is substantial room for improvement from the information intervention. Only about half of women indicated that a newborn infant should be fed breast milk exclusively for exactly six months, and about half answered that a pregnant woman should eat more food compared to before getting pregnant
**|**2000**|**2010**|**2013**|
|Somalia|470,174|475,655|770,154|1,121,738|
|Sudan|523,998|494,363|387,288|649,331|
|Dem. Rep. of the Congo|67,423|371,713|476,693|499,541|
|Eritrea|43|376,851|222,460|308,022|
|Central African Rep.|100|139|164,905|252,865|
|Mali|1|364|3,663|152,864|
|South Sudan||||114,467|
|Côte d'Ivoire|2|773|41,758|85,729|
|Rwanda|361,322|119,056|115,524|83,937|
|Burundi|191,622|568,084|84,064|72,652|

Note: Authors’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.

8
be classified as top earners otherwise, but only make the cut because we observe their ownership of corporations and assign them profits. Between 10 - 20% of individuals within the top 0.1% have distributed income below the MW. This is one indicator that directly assigning individuals undistributed profits from the corporations they own generates meaningful re-ranking of the identity of top income earners, and also that a meaningful share of these earners do not receive large distributed incomes.

While we have established that observing corporate ownership data matters for the identity of those on the top, an important remaining question is whether it matters for estimates of top income shares using the DINA methodology. Clearly, adding undistributed corporate profits to total realized income leads to large increases in measured income concentration - Alstadsæter _et al._ (2016), for example, show that it more than doubles the share of top 0.1% income in Norway. But the usual DINA approach also adjusts total income to include national account measures of corporate profits – it usually does so by distributing these profits in proportion to _realized dividends_ , instead of directly measuring corporate ownership. To assess whether these matter, we replicate our results ignoring the corporate ownership data and instead distributing 16
and economic stress that grant further analysis. First, since the shock is truly exogenous to the financial sector, the overwhelming response from policymakers has been to relax regulatory requirements and use capital buffers: During our sample, 91% of countries used prudential measures. There is, however, no prior analysis of such a coordinated type of response, and no clear evidence about the medium- and long-term effects of these policies. To the best of our knowledge, our paper represents the first global analysis examining the market response to financial policy measures during the pandemic.

# 2. **Data**

We use data from Refinitiv on all publicly traded banks and non-bank financial companies across 53 countries between May 2, 2018 and May 1, 2020. The data set includes information on the daily stock prices, quarterly financial statements, and state ownership. We choose only stocks traded on major exchanges. Of the 4,978 stocks in the data, we drop 612 stocks that are not common stocks or are stocks with special features, such as depository receipts, real estate investment trusts, and preferred stocks. We further drop 1,084 stocks that traded less than 30% of the business days in each country throughout the sample period.<sup>8</sup> Finally, we drop from the dataset 211 banks that are owned by corporate groups whose core business is different from banking.

We use daily Brent crude oil prices to measure the degree to which an individual bank is exposed to the oil sector prior to the crisis. Specifically, we define oil sector exposure as the slope coefficient of an OLS regression of bank’s stock returns on a constant, the market return where

> 8There are 28 firms in the sample with multiple ordinary shares. For these cases, we keep the stock with the highest trading volume.

8
reported increased exports, and bigger shares reported decreased exports.<sup>18</sup>

**Figure 6: SMEs cope with the crisis differently than large firms**

<!-- Start of picture text --> If decreased workforce, number of workers laid off due to the<br>COVID-19 outbreak<br>Current proportion of workforce working remotely<br>Percent of firms adjusting or converting their production or<br>services<br>Percent of firms that started or increased remote work<br>Percent of firms that started or increased online business activity<br>Percent that started or increased delivery of goods, services or<br>carryout<br>-40 -30 -20 -10 0 10 20<br><!-- End of picture text --> Source: World Bank Enterprise Follow-Up Surveys Note: Chart illustrates the average difference between SMEs and large firms within a country-sector pair with their standard errors. Country-sector fixed effects are included. Stratification weights adjusted for the followup enterprise surveys according to median eligibility were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market

- 16 The usage of messenger apps could be one key measure.

17 This is supported by the World Bank’s COVID-19 Business Pulse Surveys (COV-BPS) that capture firmlevel impacts, which found that digital solutions are more likely to be adopted by larger and formal firms. 18 This also likely depends on the sectoral composition of these firms.

12
.644<br>[0.008]|0.451<br>[0.005]|0.334<br>[0.006]|0.637<br>[0.008]|
|Poverty-Gap (p1)<br>0.182<br>[0.003]|0.121<br>[0.003]|0.279<br>[0.005]|0.172<br>[0.003]|0.12<br>[0.003]|0.257<br>[0.005]|0.171<br>[0.003]|0.119<br>[0.003]|0.252<br>[0.005]|
|Squared Poverty-Gap (p2)<br>0.095|0.061|0.148|0.088|0.06|0.133|0.087|0.06|0.13|
|[0.002]|[0.002]|[0.004]|[0.002]|[0.002]|[0.004]|[0.002]|[0.002]|[0.004]|
|Observations<br>21,239|11,551|9,688|21,239|11,551|9,688|21,239|11,551|9,688|
|Standard errors in brackets<br>*** p<0.01, ** p<0.05, * p<0.1<br>Source: 2012 1-2-3 Survey, DRC|||||||||

The adoption of the VAT eroded the purchasing power of all households. Household aggregate food consumption decreased, with the highest losses, measured by the compensating variation,
WPS3652

# **Growth Spillover Effects and Regional Development Patterns:**

# **The Case of Chinese Provinces**

## **Xubei Luo**<sup>*****</sup>

### **<u>Abstract:</u>**

This paper discusses regional development patterns in China, and examines effective ways of using development aid to attain regional balanced growth through optimizing growth spillover effects.

Based on provincial panel data from 1978 to 99, the paper constructs an indicator “neighbor performance” to measure the geographic spillover effects of aggregate growth from and to different provinces according to their relative richness and geographic position. Analysis of a Solow-type growth model suggests that positive spillover effects dominate negative shadow effects at the national level as well as the regional level; and some coastal provinces provide growth pull and growth push forces for their neighbors and serve as locomotives.

The results show that the rapid take-off of the coastal provinces has the largest spillover effects on the entire Chinese economy, but at the expense of a widening regional gap. A policy of encouraging the growth of the non-coastal regional hubs would have strong forward and backward linkages with the inland/western regions and thus reduce the regional development gap without sacrificing much aggregate growth. The paper offers support for the policy of developing inland hubs, and argues that directing development aid to Hubei and Sichuan would optimize the growth spillover impacts on inland regions.

#### **JEL** : O18, O47, R11

**Key words** : Spillover effects, Regional development pattern, Balanced growth,

China

### World Bank Policy Research Working Paper 3652, June 2005

_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
not reap political benefits from learning gains alone (Habyarimana, Opalo, & Schipper, 2020), yet an additional year of schooling can lead to very different levels of learning (Singh, 2020; World Bank, 2018). Using LAYS as a metric of progress allows a focus on additional years and learning together. At the same time, the LAYS framework also helps make a clear case for future impact evaluations to use standardized approaches to measuring learning, for example by using test scores from internationally applied assessments such as the Trends in International Mathematics and Science Study (TIMMS), the Programme for International Student Assessment (PISA), or the Early Grade Reading Assessment (EGRA). This shift would greatly improve the likelihood that policymakers can make informed decisions about which interventions to prioritize and scale up.

In summary, this paper uses learning-adjusted years of schooling to provide guidance on which policies and interventions are the most efficient investment in education, given the state of evidence and data available today. The LAYS metric has recently been incorporated into large-scale policy efforts to improve education. It is a component of the World Bank’s recently launched Human Capital Index (World Bank, 2019), and is being used by the World Bank, UNICEF, and United Kingdom’s Foreign, Commonwealth & Development Office (FCDO) to prioritize cost-effective 29
take time.

The paper proceeds as following: Section 2 provides a description of account ownership around the world. Section 3 discusses the evidence on the benefits of financial inclusion organized around four major types of formal financial products: payments, savings, credit, and insurance. Section 4 discusses some of the challenges to achieving greater financial inclusion and directions for future research.

> 2 See World Bank (2014) for an overview of data sources on financial inclusion. 3 https://www.imf.org/external/np/sec/pr/2015/pr15455.htm

4
; whether using written contracts more frequently with new trading partners than old trading partners; whether using written contracts more frequently with new private enterprises than other new trading partners? (The survey of Russian Enterprises).

- Informal lending: the amount, maturity, the existence and amount of collateral, and provisions in case of default? (RPED survey)

The data gathered through these surveys has allowed researchers to better understand the role of contracts and legal institutions on firm's performance. In particular, Hendley, Murrell, and Ryterman (1999) focus on the importance of laws and legal institutions for Russian enterprises 15
21

|ountry,<br> <br>economy,<br>or<br>territory|Last owbtis<br>P<br>Date|hed censu<br>opulation<br>(1000) *|s<br>Source for population<br>(mid-1985)|Source<br>for fertility<br>(1985-90, unless indicated)|Source formortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|,aoPeople's|Mar 85|3585 F|Bank projection from census|Bank est.|Bank est.|
|Dem.Rep.||||||
|Lebanon|Nov 70|<br>2126 F|U.N. 1984 assessment|Basedon U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|.esotho|Apr 76|<br>1217 F|Bank projection<br>from|Based on 1977 World|Based on 1977 WorLd|
||||urpwtlished<br>4/86 census|Fertility<br>Survey|Fertility<br>Survey|
|Liberia|Feb 84|<br>2102 F|U.N. 1988 revision<br>(prelim.)|1986 DhS|U.N. 1988 revision<br>(prelim.)|
|Libya|Jul 84|<br>3637 J|Bank projection<br>from census|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Liechtenstei|n Dec 81|<br>26 F|UNPVSR<br>7/86|||
|Luxeatourg|Mar 81|365J|UNPVSR<br>7/86 (official<br>est.)|Eurostat 1987|Eurostat