rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix. Details on Parameter Calculations and Data Sources**

_Mileage_ . We consider four modes of passenger transportation in the GCMA: auto (including taxi), private microbus, public bus, and rail. Other modes like minibuses and the Haliopolis tram account for a very small share of passenger mileage and are excluded from our analysis.

According to IAPT (2007) the average person in the GCMA travelled 1,344 miles in 2002, where modal shares were auto (28.1 percent), microbus (32.2 percent), public bus (30.0 percent) and rail (9.7 percent).

We were also able to calculate modal shares for non-rail travel for 2005 based on estimates of the number of vehicles, average daily trips per vehicle, average trip length, and average vehicle occupancy obtained by pooling data from Central Statistical Office ( ̳CAPMAS‘), Cairo Transportation Authority, and the Ministry of Finance<sup>16</sup> . Assuming the same share for rail travel as in 2002, this alternative data suggests modal shares for auto (24.4 percent), microbus (33.8 percent), and public bus (32.1 percent). In Table 1, we split the difference between these two estimates, assuming the same overall per capita mileage as in IAPT (2007). The resulting modal shares are also consistent with figures in NKCL and KEI (2008). Vehicle mileage data were estimated using average daily trip per vehicle and average trip length. These information together vehicle occupancy were obtained from NKCL and KEI (2008) and personal communication with Cairo Transportation Authority.

_Fuel economy_ . Fuel economy for different vehicle types was taken from the IAPT (2007) database.

_Fuel prices_ . The gasoline price and subsidy were obtained from the Ministry of Finance (Personal Communication, April 2010), averaging over octane 80 and 90.

_Transit fares and operating costs_ . Personal communication with Cairo Transport Authority.

_Local pollution damages_ . We
and the Visible Infrared Imaging Radiometer Suite remote sensor (2012–

> 12Since the main specification follows a difference-in-difference design, any imbalance in the outcome levels between high and low shares of foreign settlements in municipalities does not threaten the empirical strategy because they are absorbed by the municipality fixed effect in the empirical specification. Nevertheless, we rule out the concern that these characteristics could trend differently by controlling for time trends in socioeconomic baseline characteristics.

17
_2.88_**|**_4.24_**|**_6.23_**|**_6.52_**|

_Source_ : Causes and Impacts of Power Sector Circular Debt in Pakistan. March 2013. USAID and Planning Commission of Pakistan.

The magnitude of the stock of circular debt by the end of 2012 (US$ 6.52 billion) was such that action was desperately needed to avoid the consequences of so much underpayment to the various companies in the supply chain.

The elections in 2013 marked the first time that a party (PPP) had run its full term, and when PML(N) won the election this was the first transfer of power between parties on a regular basis. The power crisis was one of the primary election discussion points among the contestants— violent protests had broken out in various parts of the country due to power outages in 2011, 2012 and 2013.

The incoming government was ideologically opposed to international assistance and the party had campaigned on a platform of economic autonomy but had little choice except to negotiate a loan with the IMF.<sup>15</sup> The previous PPP government had failed to meet the terms of the previous IMF loan and the country was still struggling to repay billions of dollars of that loan. As a condition of the new $5.3 billion loan, the IMF insisted on a complete overhaul of the energy sector with a focus on privatization and power tariffs. Because of the loan, the government cleared $4.8 billion of the stock of circular debt in 2013.<sup>16</sup> Although the government was unable to ensure that no further increases in power sector arrears were incurred, by 2015/2016 the incremental arrears

> 15 _IMF and Pakistan agree to a $5.3 Billion Bailout._ Declan Walsh and Salman Masood. New York Times, July 4, 2013.

> 16 Although government statistical sources mention circular debt, there does not appear to be
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Index of Unmarried<br>Autonomy (Standardized)|-|Core<br>Respondent|No|4|Standardized index of autonomy, measured for<br>core respondents who are not married at the<br>time of the household survey. Includes<br>questions on whether the core respondent<br>needs permission to do certain activities,<br>whether the core respondent can travel alone,<br>and whether the core respondent is allowed to<br>have money set aside for her own use.|
||||||Standardized super index of married<br>empowerment, measured for core respondents<br>who are married at the time of the household<br>survey. It includes 8 components described<br>below: index of decision-making; index of|
|Super-Index of Married<br>Empowerment<br>(Standardized)|IV|Core<br>Respondent|Yes|4|marital satisfaction; index of women's divorce<br>prospects; index of fertility empowerment;<br>index of self-determination in marriage; index<br>of frequency of social contact; index of spousal<br>abuse; and age difference between husband<br>and wife. All variables are constructed so that<br>higher values are an indicator of increased<br>empowerment.|
|Index of Decision-Making<br>(Standardized)|-|Core<br>Respondent|No|4|Standardized index on a series of questions on<br>who makes decisions in the household related<br>to food, clothing and children.|
|Index of Marital<br>Satisfaction (Standardized)|-|Core<br>Respondent|No|4|Standardized index on
women also reported reduced income due to job or business losses than women while more men did so due to reduced working hours than women.<sup>52</sup>

**Age Group** : Our method also projects, as in the case of Jordan, that the rate of job loss will be lower for prime age workers (aged 25-45) than for youth and older workers (and the survey-measured job loss projections were more even by age group as in the case of Jordan). Similarly, the GHFPS shows a 27.2 percent rate of private sector job losses for prime age workers relative to 44.1 percent for young workers aged below 25 (Table 5-20). Young workers also experienced greater rates of reduced income due to job or business losses.

_Table 5-19 Georgia: High-Frequency Phone surveys. Labor Market Impacts of COVID-19 by Gender, Percent of Private Sector Employees in March 2020_

||**Lost jobs**<br>**because of job**<br>**or business**<br>**losses due to**|**Stopped**<br>**working to**<br>**avoid exposure**<br>**to the virus**|**Reduced income**<br>**because of job**<br>**or business**<br>**losses due to**|**Reduced income**<br>**due to reduced**<br>**working hours**|
|---|---|---|---|---|
||**COVID-19**||**COVID-19**||
|**Male**|22.9|1.5|56.7|22.1|
|**Female**|40.8|4.3|77
cultivated, owned and otherwise possessed field (also called plots) of the households. It is these plots which are our unit of analysis from which we generate (weighted) district-level statistics. The dependent variables are defined as a percentage of the rented in acreage or rented in plots at a district level. For each plot we have information on the location (within the village or not), soil type (soil texture), land use, acreage, irrigation facilities/flood frequency, as well as the ownership and rental agreement details.<sup>10</sup> Our analysis builds on plot-level data from the tenant side of the market, as all tenants, even landless tenants, are considered in the sampling process.

# **Migration: Census of India**

We use bilateral district-to-district migration data obtained from the 2001 and 2011 Population Censuses of India. The Census is the only data source with information on both the source and origin district of migrants for all the districts of India.

The Census, which is conducted every decade, asks every individual if the place in which they are enumerated during the census is different from their “last usual place of residence”. As in Kone et al. (2018), we consider a person a migrant if they reply in the affirmative.<sup>11</sup> Additional follow up questions allow us to capture bilateral migration flows. These include the location (district) of the last place of residence, reason for migration (marriage, education, employment etc.), and the duration of stay in the current residence since migration. The census also includes information on the sex, education and age of each individual (but notably not land ownership).

Since the census does not provide unit-level data, under a special administrative agreement we requested them to provide us with aggregated data on the volume of migration between every pair of districts. For a given pair 10We omit plots which are categorized as non-agricultural, and might contain lakes,
improved transportation infrastructure has advantages and disadvantages. On the plus side, a network analysis can be a useful tool to support modeling of international trade flows. It can enhance the understanding of the impact of changes in infrastructure or other factors on an interconnected system allowing to extract information from spatial data. Specifically, a network analysis enables the measurement of the time it takes to go from one location to another taking into account the overall quality and quantity of infrastructure, physical obstacles (e.g. rough terrain) or other barriers (e.g. border delays), and relative performance measures (e.g. processing time at ports or when crossing a border). As with any modeling exercise, it should be acknowledged that our network is a simplified version of reality and results are affected by the quality of inputs. For example, due to limited data availability, we cannot include some elements that might influence the definition of optimal routes, such as freight service quality, service frequency, conflicts or adverse weather events.

# **1.1.1 Transportation Time Validation**

Before using it as an input in the general equilibrium counterfactual analysis of trade and infrastructure reforms, we validate the transportation time in our dataset using evidence from Hummels & Schaur (2013) and Hummels

> 12The elasticity is a combination of trade elasticity _θ_ and location specific friction elasticity _δ_ .

> 13To retrieve the aggregation parameter we rely on a PPML estimator.

> 14We repeat this process until the estimated coefficient remains unchanged in its 5th digit. We do this separately for transport times with and without the sea route preference assumption. The procedure converges in 7 iterations in both cases. Without sea preference, the estimated _δθ_ is -0.852, with sea preference, the estimated parameter is -0.780.

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Policy Research Working Paper 10421

# **Abstract**

This paper investigates the interplay between structural and behavioral barriers that discourage pregnant women from accessing institutional care in Haiti, where despite some improvements in the past decades, maternal mortality remains a significant challenge. The analysis complements household survey data with data on service provision and qualitative data on beliefs, perceptions, and attitudes toward maternal health care. Using a mixed-methods approach, the paper confirms that transportation and poverty are important barriers that decrease the likelihood of attending maternal health care services. At the same time, the findings show that women suffer from optimism bias and are uncomfortable with the current model of received care. These barriers discourage women from seeking, reaching, and receiving maternal health care services at health institutions. Tackling structural barriers while finding ways to encourage women to shift their beliefs, perceptions, and attitudes are key recommendations to improve maternal health in Haiti.

This paper is a product of the Poverty and Equity Global Practice. 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 afruttero@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 by the Research Support Team
# **3. Human Capital and Migration**

A striking feature of most FSU countries is that their human capital is much greater than is typical for countries with similar levels of per capita income, or conversely their per capita incomes are much lower than is typical for countries with similar endowments of human capital, which is likely to be a legacy of the strong educational and health systems which existed during the Soviet period and, in some countries, notably the Kyrgyz Republic, Tajikistan and Uzbekistan, continued high levels of public expenditure on education as a share of national income. It may also reflect other constraints, common to all the FSU countries, which prevent human capital generating the same level of returns than is the case in other regions of the world.

**Table 5 Ranking of FSU countries by HCI and GDP at PPP per worker**

|Country|Human<br>Capital Index<br>(0-1, a higher<br>score denotes<br>more human<br>capital)|HCI ranking<br>(1 = highest<br>ranking<br>country)|GDP PPP per worker<br>(average 2015-17, in<br>constant 2011 US<br>dollars|GDP PPP per<br>worker ranking<br>(1 = highest<br>ranking<br>country)|
|---|---|---|---|---|
|Armenia|0.572|75|22,915|83|
|Azerbaijan|0.597|66|33,257|64|
|Georgia|0.614|58|22,522|84|
|Kazakhstan|0.746|29|46,968|50|
|Kyrgyz Republic|0.580|73|11,142|108|
|Moldova|0.580|72|18,549|89|
|Russian Federation|0.729|32
Given constant expenditure shares, the sectoral Törnqvist price index reduces to a geometrically weighted average of the construction and equipment indices using the 2010 weights. In the two decades ending in 2016, housing investment rose in price by 0.9% per year relative to all investment goods as a result of consisting almost entirely of structures (Figure 5). At the other extreme, business sector prices fell in relative terms by 0.5% per year due to the high share of expenditures on equipment. The relative prices of investment in the infrastructure and government sectors rose by 0.7% and 0.5% per year, respectively.

There is thus a substantial gain in accuracy from using sector specific price indices rather than using the same price index to deflate the investment of each sector. For the period prior to 1981 when the price indices for equipment and construction are not available, sectoral deflators have been calculated by assuming that the price of each sector follows the same trend relative to the published deflator for GFCF as did the movement of sectoral price indices relative to the overall price index of fixed asset investment in the period 1995 to 2016.

# **Figure 5 Sectoral price indices for fixed asset investment**

<!-- Start of picture text --> Estimated sectoral price relative to total price index for FAI<br>1.25<br>1.20<br>1.15<br>1.10<br>1.05<br>1.00<br>0.95<br>0.90<br>0.85<br>Housing Infrastructure Government Business<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> _Source:_ Author estimates based on NBS data.

# **Results**

# **_Investment flows_**

The estimates of sectoral investment flows quantify the
is already under preparation. Improved collection of training data <mark>beyond the country’s five main crops c</mark> ould help to expand the range of outcome variables considered while donors could help ensure that future programs are designed in a way that makes them more amenable to impact evaluation.

The rest of the paper is structured as follows. Section 2 documents the context of Ukraine’s agriculture and PSG implementation modalities. Section 3 provides details on war effects and PSG access based on phone survey data. Section 4 expands on this by using administrative and remotely sensed information to assess targeting quality and program impact on area cultivated with summer crops in the 2023 season, directly after resources were received. Section 5 concludes with implications for policy and research.

# **2. Context and setting**

Given its turbulent history of collectivization and famine in the first half of the 20<sup>th</sup> century and privatization at the beginning of the 21<sup>st</sup> century, Ukraine’s agricultural sector is distinctly different from what is found in other European countries. Coming at a time of reforms to improve transparency and create preconditions for a functioning and socially inclusive market economy, the war triggered by the invasion led to a severe disruption of agricultural of grains and oilseeds, major damage to the stock of physical capital, and unprecedented population displacement. To help address some of the resulting challenges and adopt key 5
Existing empirical evidence on the effects of maternity leave on female employment in the period immediately after giving birth and in the long-run is mixed. That is, for the period just after childbirth, while some studies show that maternity leave is associated with a faster return or higher probability of return to work post childbirth (Waldfogel et al. 1999, Berger and Waldfogel 2004), others show just the opposite (Lalive and Zweimüller 2009, Hanratty and Trzcinski 2009, Ludsteck and Schonberg 2007), or no effect of maternity leave (Klerman and Leibowitz 1997, 1999, Dahl et al. 2016).

Empirical studies on the long-run impact of maternity leave on female employment also show a mixed picture, although such studies are relatively scarce. Using census data for the U.S. for 1980 and 1990, Klerman and Leibowitz (1997) find no significant long-run impact on female employment of maternity leave sanctioned under the federal Family and Leave Act. Similarly, Hanratty and Trzcinski (2009) find that the 2000 reform in maternity leave legislation in Canada that increased maternity leave from 25 to 50 weeks had no impact on return to work by mothers beyond the duration of the maternity leave. Lalive and Zweimuller (2009) also find no significant effects of increased maternity leave in Austria on female labor supply in the long-run (over 10 years after birth). In contrast, Kluev and Schmitz (2018) estimate policy impacts of a generous parental benefit in Germany. Their estimates for the medium or long-run impact (i.e. 3-5 years after childbirth) show an increase in the probability of mothers’ employment by up to 10 percent, higher job continuity with the pre-birth employer, and better quality of jobs. On the other hand, Schonberg and Ludsteck (2014) find that maternity leave expansion led to a fall in women’s employment 6 years after childbirth, but this decline was small. Ondrich et al. (2003) also arrive at a similar conclusion for the case of Germany.

4
calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for ownership dummy categories—relative to Romanian private firms, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year and NACE 4-digit sector fixed effects, as displayed in equation (11). More details about the original ownership classification in SBS, and aggregation used for the analysis, can be found in table A1.3 and table A1.4 in annex 1. See table A1.5 in annex 1 for the complete set of results. n.s. = not statistically significant.

**Larger firms and older companies wield higher markups when compared with their counterparts, implying that firm size and age are relevant characteristics that influence markup differences.** As shown in figure 8, old companies (older than 15 years) display higher average markups than young ones (1–5 years old), even when they share similar attributes (in terms of ownership structure, size, and export and R&D profiles) and operate in the same NACE 4-digit sector, county, and year. The estimated average markup premium for old companies (relative to young ones) is 5.4 percent for the whole economy. It varies depending on how data are clustered across large sectors: 7.9 percent in manufacturing, 4.8 percent in services, and 22.8 percent in mining. Higher markup premiums for older companies can be interpreted in two ways: it can reflect that more experienced firms are able to transpose learning returns—which could positively affect their productivity or reduce their marginal costs—into higher markups, or that older firms are able to lobby better and create barriers to entry, making it more difficult for younger firms to exert competitive pressure. Firm size also matters to explain markup performance: large companies (with more than 250 full-time employees) tend to display higher markups compared with small companies, even when they operate in the same product market and share
have attendant negative consequences for market participation of “outsiders,” such as indigenous-African SMEs. This is indicated through the determinants of access to supplier credit. In addition to longer-term, cumulative efforts to built up high-quality formal institutions, policy interventions will be needed to improve the platform for relation-based governance mechanisms and to address the exclusionary effects of tight networks.

# **I. Markets and Private Support Institutions in Sub-Saharan Africa**

# **_Economic Environment, Institutions, and Transaction Costs_**

The RPED surveys provide us with a broad picture of the characteristics of manufacturing firms and the business climate in SSA (Biggs and Srivastava 1996; Bigsten et al 2000a; Fafchamps 2004). Firms are mainly small with few assets and limited access to finance. At the upper end of a largely bi-modal size distribution is a small group of large companies (200 employees or more), with more access to finance (and often significant debt), which do business mainly in sparse local markets. The middle of the size distribution of firms is still relatively empty. Technical and management skills are low on average, and absenteeism and acts of employee pilfering are numerous. Product standardization is relatively low. Businesses operate under conditions of considerable uncertainty. Financial and insurance markets are severely underdeveloped, limiting access to credit and insurance. And market exchange is underpinned by weak public institutions of property rights and contract, poor governance, and poor infrastructure services. These market features combine to increase the uncertainty of business relationships and raise transaction costs of exchange.

Small companies with few assets reduce the efficacy of legal actions. There are few assets to seize in the case of default and transactions are generally too small to justify the time and money involved in formal court actions. Many business deals simply avoid problems by engaging in self-enforcing, cash-on-delivery spot transactions. These kinds of business relationships facilitate exchange in many cases, but if exchange is confined to 5