rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

Infrastructure investments can support economic development through both capital accumulation and structural transformation.<sup>1</sup> Structural change—the movement of workers from lowerto higher-productivity employment—is essential to growth in low-income countries where there is a large share of employment in low productivity sectors. Trade, and economic integration more broadly, can be an important source of productivity gains and structural transformation. However poor infrastructures in low-income countries create transport and information frictions that increase the distance of economic agents relative to one another, limiting the economic specialization within and across neighboring countries. Transport improvements lead to faster journeys, making economic agents closer together, and may also trigger relocation of economic activity, increasing productivity and allowing workers to switch to more productive jobs. Electricity and Internet allow for the use of more modern productive technologies and complement transport infrastructure by boosting firm productivity.

The literature has studied specific infrastructure expansions as potential drivers of development, but little work has been done on the associated structural change or whether the combinations of different infrastructures create a big push development impact. This paper investigates how investments in electricity, Internet, and transport infrastructure and their interactions affect economic development through productivity gains and structural change in countries in the Horn of Africa.

This paper first uses reduced-form analysis to understand the relationship between past investments in electricity, Internet, and road infrastructure and sectoral structure of employment in Ethiopia and Kenya. Geo-identified data on the infrastructure expansion of roads and electricity for Kenya and Ethiopia are collected over time and across space, and linked with information on local economic activity based on household-survey data. Two instrumental variables are developed to overcome the endogeneity concerns for road and electricity investments. Following Moneke (2020), straight transmission lines connecting themain cities with newly opening hydropower dams in Ethiopia and all power plants in Kenya are used for electrification, the construction of an hypothetical least-cost network expansion using a minimum spanning tree algorithm is used for road expansion. Reduced-form results capture the localized effects in the areas that have been
Scars of Pandemics from Lost Schooling and Experience: Aggregate Implications and Gender Differences through the Lens of COVID-19 Roberto Samaniego, Remi Jedwab, Paul Romer and Asif M. Islam<sup>_∗_</sup> JEL: O11; O12; O15; E24; J11; J16; J17; J31.

Keywords: Pandemics; Human Capital; Returns to Education; Returns to Experience; Gender; Female Relative Income; Labor Markets; Development Accounting; COVID-19.

> _∗_ Corresponding author: Roberto Samaniego: Department of Economics, George Washington University, roberto@gwu.edu. Remi Jedwab: Department of Economics, George Washington University, jedwab@gwu.edu. Paul Romer: NYU Stern School of Business, paul@paulromer.net and john@paulromer.net. Asif M. Islam: The World Bank, aislam@worldbank.org. We are grateful to Stephanie Cellini, David Deming, Simeon Djankov, Roberta Gatti, Amer Hasan, Lutz Hendricks, Daniel Lederman, Maurice Kugler, Elira Kuka and Emiliana Vegas for helpful comments. We gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database.
|1.373||||14.24|
|||||(12.11)||||(28.55)|
|Education_×_Informality||||-12.45||||-19.75|
|||||(12.56)||||(23.37)|
|Health_×_Informality||||-16.60*||||-21.10|
|||||(9.896)||||(14.19)|
|Other serv_×_Informality||||1.567<br>(3.242)||||5.053<br>(4.438)|
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|No|No|No|No|No|No|No|
|Lag change in sales|No|No|No|No|Yes|Yes|Yes|Yes|
|<br>Observations|19569|19569|19569|19569|10203|10203|10203|10203|

**N** otes: Each regression also includes a control for the variable interacted with the informality measure. All continuous variables are winsorized at the 1% and 99%. Recovery is calculated as the difference in the changes in sales across rounds of data collection. Robust standard errors in parentheses * p<0.10, ** p<0.05, ** p<0.01 Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
the nearest of India’s ten largest cities<sup>6</sup> as a measure of physical connectivity and across-district infrastructure. This is calculated based on data on India’s road networks using GIS software.

Finally, we model local labor regulations using state-level variation in policies. Several studies link labor regulations in Indian states to economic progress (Besley and Burgess 2004, Aghion et al. 2008), and Ghani et al. (2011c) finds labor regulations suppress Indian entrepreneurship generally. Our measure is taken from Ahsan and Pages (2007), who break down the labor regulations index proposed by Besley and Burgess (2004) into separate components affecting labor adjustment and labor disputes legislation. Using these separate measures, we create a composite labor regulations index by state.

# _Agglomeration Theories_

The above factors are district-level phenomena, and a useful first exercise is to relate the female entrepreneurship ratio to these factors. The focus of our study, however, is on the local industrial conditions of district-industries and how they shape female entrepreneurship. We develop metrics that unite the incumbent industrial structures of cities with the extent to which industries interact through the traditional agglomeration rationales first defined by Marshall (1920). Duranton and Puga (2004) and Rosenthal and Strange (2004) review the subsequent literature in detail. Prior work emphasizes the importance of these conditions for explaining overall entry rates,<sup>7</sup> but this work does not explore the impact of favorable local conditions on the gender balance of entrepreneurs.

We also want to explore the importance of whether the gender profile of the incumbent industrial structure affects the gender profile of new entrants. The role of business networks among women in developing countries is frequently mentioned by development economists, but we are not aware of any prior work that systematically considers female businessownership across regions and industries in multiple economic sectors. For simplicity, we describe our metrics below in a generic language that discusses district-industry employment. Our empirical 6These are Ahmedabad, Bangalore, Bhubaneshwar, Chennai,
*Endogenous debt dynamics**<sup>**/2**</sup>|-4.4|-0.9|-1.6|-1.9|-1.9|-2.5|-2.8|-2.8|-2.6|
|Contribution from nominal interest rate|0.8|0.6|0.6|0.7|0.7|0.7|0.7|0.7|0.7|
|Contribution from real GDP growth|-2.1|-2.0|-1.3|-1.9|-2.1|-2.2|-2.4|-2.4|-2.3|
|Contribution from price and exchange rate changes|-3.1|0.5|-0.9|-0.7|-0.5|-1.0|-1.1|-1.1|-1.0|
|**Residual**<sup>**/3**</sup>|-4.9|-4.0|-2.2|-0.4|-1.7|-1.2|-0.6|-1.1|-1.3|
|of which: exceptional financing|-1.7|-2.4|-0.4|-0.3|0.2|-0.3|-0.3|-0.2|-0.2|

Source: Authors' calculations based on 33 SSA countries DSAs done by IMF and World Bank staff.

1/ Includes both public and private sector external debt.

2/ Derived as [r - g - ρ(1+g)]/(1+g+ρ+gρ
374.36)|1,419|
|Job Search (Weeks)|-0.80|(2.69)|[0.67]|5.63|(8.68)|306|
|Focus Respondent Childcare & Chores (Hours)|-6.91***|(2.02)|[0.01]|17.76|(18.39)|1,418|

**_Notes_** :

- The table shows the regression results on pre-specified labor market outcomes of the Focus Respondent using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Total Earnings is defined as the total of business profits and net wage salary in all jobs and is transformed using the inverse hyperbolic sine transformation. Total Labor Hours [Monthly Average] is defined as average weekly wage-employment hours multiplied by the numbers of months worked in last 12 months multiplied by 52/12 to obtain a monthly average of all jobs. Wage-Employment Adult Labor Hours Last Week is the sum of respondent hours and total hours where adults (other than respondent) in the household were working. Net Wage-Employment Income is defined as gross salary plus benefits minus taxes in all jobs. High-Skill Service Employment refers to employment in the sectors of Utilities, Transportation and warehousing, Health care and social assistance, Arts, entertainment, recreation, and Accommodation and food services. Low-Skill Service Employment refers to employment in the sectors of Wholesale or retail trade, Information, Finance and insurance, Real estate, Professional, scientific, and technical services, Management, Administrative support, and Education.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- Monetary values are in USD PPP. Total Earnings in Last 30 Days, Taxes Paid in Last 30 Days, Net Wage-Employment Income Last Month, Self-Employment Profits, Expenses, and Revenues Last Month and Year
affected social groups (e.g. beneficiaries) if the moderator is categorical in nature (e.g. SES). If the moderator is continuous in nature, then the evaluator can employ a range of methods, for example QTE as described above, to generate patterns of benefits across the distribution of individuals or groups along the continuum.

In module 3, the enhanced CBA APP provides the outputs of the economic analysis. In summary, the APP allows for cost-feasibility analysis (i.e. comparing the overall costs of the project against the budget), cost-savings analysis (i.e. comparing the costs of the project against the savings generated from avoided crimes), cost-effectiveness analysis (i.e. comparing the costs of the project against the number of units of output such as the number of crimes prevented) and CBA (i.e. comparing the costs of the project against the overall benefits – avoided crimes and other benefits such as enhanced safety). Tables and plots are also included to display, for example, net costs, net benefits, cost-benefit ratio, net benefits by bearers and potential efficiency gain (PEG - as described above).

# **_Data employed to test modules 1 to 3 using the enhanced CBA APP_**

We employ data from the Pathways to Prevention Project (Homel et al., 2006) to test modules 1 to 3 of the enhanced CBA APP. The Pathways to Prevention project was established over twenty years ago as an early intervention, developmental crime prevention initiative focussed on the transition to school in one of the most disadvantaged urban areas in Queensland, Australia (Homel et al., 2001). Here, the goal was to provide positive pathways to individuals at-risk of later learning and behavioral problems that may eventually lead to crime and deviance. The project comprised two components: (i) a preschool intervention program (PIP), which was a child-focused and school-based set of activities; and (ii) a family independence program (FIP),
# 3.2.2 Bulgarian corruption risks from the broader European perspective

The observed trend toward lower competitiveness and higher risks in Bulgarian public procurement follows broader European trends, albeit the magnitude of deterioration is greater.

To better understand and interpret the above risk profiles and trends in Bulgaria, we compare selected risk factors to other European countries. These comparisons are based on a narrower dataset than the one used so far to make the composition of compared procurement markets more similar. We use the TED dataset which is the online version of the 'Supplement to the Official Journal’ of the EU, dedicated to European public procurement. All tenders and contracts announced on TED follow the EU procurement directives, which implies that the regulatory frameworks are largely comparable across countries. TED publishes about 700,000 procurement award notices a year, including 250,000 calls for tenders which are worth approximately €670 billion. Tenders published on TED are different from national sources in many aspects, most importantly they tend to be of higher value (publication on TED is mandatory for high-value contracts albeit thresholds differ by supply type, among others). In spite of these differences between TED and national Bulgarian public procurement data, the subsequent comparisons can help contextualize and interpret corruption risks in Bulgaria.

To further increase comparability of corruption risk trends between European countries (European Economic Area [EEA] hitherto) and Bulgaria, we narrow our focus to a standard risk indicator: single bidding. Single bidding reflects the competitiveness of tenders and therefore can be a proxy for measuring potential corruption risks due to deliberately excluding non-connected firms from tenders. Single bidding is one of the most universally applicable CRIs, as it does not require identifying risk thresholds or classifying categories into risk groups. When there is only one bid submitted in a tender, the single bidding variable takes the value of 1, otherwise it is 0. For the sake of comparability, average annual values for each country are used.

The share of single bidding contracts has increased steadily in Bulgaria since 2011 and it
|
||-0.038||-0.0735|-0.0483|-0.0611|
|year_2000|-0.0427||0.351***|-0.273***|0.659***|
||-0.038||-0.0731|-0.0482|-0.0612|
|Observations|70719|8640|14130|31740|16209|

Source: Elaborated from CHER database. Dep. Var. = Income satisfaction. Panel sample. Ordered Logit estimations. Standard errors under coefficients. (*) significant at 10% level; (**) significant at 5% level; (***) Significant at the 1% level.

28
.

Our results are also robust to alternative definitions of the conflict exposure measure. Table A16 shows that the estimated effects of conflict on our main outcomes remain qualitatively unchanged when exposure is defined using: i) smaller buffers around the firm (5 km and 10 km radii in columns 1–2); ii) a larger buffer (40 km radius, column 3); iii) a weighted measure based on the inverse distance of conflict events (column 4); and iv) conflict events occurring within a 20 km radius over the previous two years (column 5). Additionally, we examine how our findings depend on the types of events included in our conflict exposure measure, namely all hostile events in the ICEWS dataset which are defined to be violent in Amodio et al. (2021) (see Table A6). Table A17, column 1, shows the results when we exclude from this list those events that may be less likely to have a direct economic impact on the firm. Column 2 reports estimates obtained by excluding events related to protests and riots. The next two columns show the results when we apply a _quantitative_ rule for event selection using the intensity score (see Section 2.2). Column 3 reports the results when conflict exposure is measured considering only very violent events, i.e. with intensity scores between -8 and -0, while column 4 shows the results when we consider only extremely violent events, i.e. with intensity score -10, such as _killings or suicide attacks._ In all cases, our baseline results are confirmed. Finally, 14
The positive distributional consequences of targeted SHS programs from the simulation result mainly from the fact that the concentration of non-electrified households and the poverty rate are closely correlated across districts. In Figure 3, the poverty map which is constructed based on household consumption per capita<sup>11</sup> is contrasted with the map of concentration of nonelectrified households at the upzila level, which reveals the strong spatial overlap between the two indicators.

Improving the efficiency and equity of SHS projects requires better project targeting as well as better integration of renewable energy projects with existing development projects on the ground. To this end, it useful to overlay the location choice indicators for SHS program delivery with the poverty map to target SHS programs. For example, SHS programs should be placed in localities where the affordability rate is sufficiently high and where there also exist poverty alleviation programs to avoid duplication while maximizing the impact through resource pooling and coordination. Yet, so far, many of the SHS programs have been implemented in isolation, with little attention being paid to the integration into the existing social program operated at the local level. With the rapid increase in carbon finance for climate mitigation projects in developing countries, the issue with regard to how renewable energy projects should be targeted and integrated with poverty programs should, therefore, be highlighted in the policy-making in order to maximize the carbon mitigation and development impact.

# **6. Conclusions**

Using the Bangladesh national household survey, this study provides three key findings. First, dissemination of SHS for rural electrification can generate substantial carbon benefit in the context of rural Bangladesh. The annual carbon avoided from kerosene displacement as a result of SHS installation would be equivalent to about 4% of total annual carbon emissions in Bangladesh in 2007 if all households without electricity access were provided with SHS. This figure will go up to about 15% if the grid-electricity generation is used as the energy baseline to estimate the carbon benefit from SHS.

Second, under the assumption that the existing micro-credit scheme plus a cash subsidy
levels. Since data are often missing for particular quarters, the analysis is limited to year-on-year changes for comparable quarters. Tables A2 and A3 in the appendix list the types of surveys used to generate employment and earnings indicators. In three quarters of the countries analyzed, employment statistics were derived from labor force surveys, while in the remaining one fourth, employment levels were obtained from establishment surveys or administrative data. As a result, in a quarter of countries analyzed employment trends may refer only to formal employees. By contrast earnings data comes from establishment surveys in 70 percent of cases and the countries, and in these countries under-represents informal or self-employed workers. In a few countries, (Mexico, Colombia, and Turkey), earnings data from establishment surveys only cover the industrial or manufacturing sectors, and most countries in Eastern Europe only collect earnings data for non-agricultural workers. Finally employment and earnings data often do not cover the full country. At least 16% of countries – typically countries in Latin America, as well as China – only collect employment data in urban areas.

_Results should be interpreted with appropriate caution._ Much of the analysis focuses on the change in the _wage bill_ as a convenient summary measure of the total magnitude of the labor market adjustment borne by workers. The wage bill is the product of employment and average monthly earnings. Given the nature of the data, changes in the wage bills often reflect changes in establishment earnings and total employment. Many of these countries have large informal sectors and the cyclicality of earnings in formal and informal establishments may differ, adding additional uncertainty to our estimates of the precise role

> 5 The five countries from the Middle East and Africa are Egypt, The West Bank, South Africa, Morocco and Mauritius.

> 6 A detailed list of sources and comments on the data are available from the authors upon request.

> 7 Usually, quarterly data is available. However, Peru and Indonesia only have half-yearly results.

3
Quality of roads**|**Quality of port**<br>**infrastructure**|
|---|---|---|
|**Ghana**|76|69|
|**Kenya**|91|84|
|**Lesotho**|113|114|
|**Nigeria‐**|112|122|
|**Senegal**|78|54|
|**Tanzania**|108|120|
|**Bangladesh**|95|113|
|**Dominican Republic**|70|58|
|**Honduras**|74|36|
|**Vietnam**|102|99|
|_Correlation_||_0.9272_|

Source: Global Competitiveness Index 2009 The importance of access to quality roads and port infrastructure is highlighted once the poor conditions experienced in the overall economy are taken into account. Table 7 shows the ratings from the 2009 Global Competitiveness Index (World Economic Forum, 2009) on the quality of road and port infrastructure in each country in the study. It shows a high correlation between the rankings of road 26
3 We find very clear and robust effects of conflict on fertility. Three effects are particularly noteworthy. First, we can identify a strong replacement effect of conflict on fertility. This holds both for the short- and the long-term post-war period. Second, we find that sibling mortality, which we consider as a strong indicator of direct exposure to conflict, has a noticeable impact in the short-term only, perhaps associated with the stigma of being part of a genocide victim household. Third, we observe a clear impact of conflict on marital status and the marriage market and hence on fertility. The persistence of the institution of marriage in determining fertility is perhaps the most surprising finding of this study.

The paper advances the literature in several ways. By defining and contrasting several conflict exposure measures, our analysis disentangles the impact of conflict into the direct individual effects of conflict and cohort-specific excess mortality rates that operate through the marriage market. By pooling multiple, very detailed and large-N cross-sectional surveys we succeed in studying very differentiated impacts of mass violent conflict on fertility across time, cohorts and sub-groups. Population pressures on land and relative changes in ethnic population shares may have contributed to the genocide in Rwanda in 1994. Fertility, if not reduced sustainably, continues to be a potential driver of future tensions in Rwanda.

The paper proceeds as follows. Section 2 reviews the current knowledge on the links between violent conflict and fertility, followed by a section introducing the case study and summarizing the events of 1994 in Rwanda. The data used and the conflict proxies are discussed in Section 4 while Section 5 outlines the estimation approach. The next section presents and discusses descriptive and multivariate results. Section 7 concludes.

# **2 Review of previous research and hypotheses**

Fertility in developing countries is shown to be determined by the socio-economic status of each woman and her partner (age, ethnicity, religion, education and employment opportunities, although the latter two may be endogenous to fertility), partnership characteristics (civil status, marital duration, number of