rafmacalaba/data-use-mentions-extended huggingface dataset:
The problem with these data on disbursement lags however is that initial disbursement projections are tentative and often revised over the life of the project. Thus, they are a kind of a “moving target” if we want to measure what actual disbursements delays are. In light of this, we instead measure disbursement delays relative to typical disbursement rates for projects approved in the same region, year, and sector, drawing from Kraay (2010). In particular, for each project we construct a typical disbursement profile based on the actual average annual disbursement rate for all projects in the same sector, approval year, and geographical region. For each year of the project, we then calculate the difference between the cumulative actual annual disbursement rate and the cumulative predicted annual disbursement rate. Finally, we take this cumulative disbursement lag over the first half of the project, and consider it as a potential leading indicator of project success ( _DISBLAGH1_ ).

Table 2 contains summary statistics on the main variables of interest. These are reported for two samples that are defined based on various data availability constraints. Our first sample begins in 1983 (i.e. with projects evaluated in 1983), and contains 6,253 subsequent project evaluations (after eliminating 548 observations with missing data on the relevant right-hand-side variables). In this sample, the outcome rating is a binary satisfactory/unsatisfactory, and we have data available on the indicated basic project characteristics as well as the ISR flags data. There are a further 810 project evaluations between 1972 and 1982 but we do not include them as the ISR flag data, as well as the preparation and supervision costs data, is not available from this earlier period. Our second sample consists of 3,887 evaluations performed since 1995. For this sample, we use the available six-point scale for the outcomes, and also a richer set of control variables including the disbursement lags and a limited set task manager variables, that we describe in more detail below.

# **4.  Results**

#
at monthly time steps, i.e. the BWI values were the average of current and previous month(s) values, and these were regressed against monthly river flow data from a particular month. The results section highlights the best-lagged relations obtained from this process. Likewise, in order to relate the BWI to the precipitation data, we use the XL-stat software to run a quadratic univariate regression equation, with various lags.

# **RESULTS**

# **General model**

We tested the accuracy, significance, and explanatory power of precipitation and BWI models for each basin (Tables 4, 5, and 6). Results identify the accuracy of the flow estimates for each independent variable. The BWI has a superior relationship to the fixed effects of topography and basin area to the lag in downstream runoff, i.e. small and steep basins have a faster discharge of water with limited lag. The best relationships in the model have lagged relationships based on the speed of flow, resembling a Kinematic wave moving throughout the river basin. The accuracy of this relationship increased in the section where wetness values and gauge values were at the low end of their spectrum, which indicates that the wetness values can accurately measure the occurrences of low flow events. The lag in this area demonstrates the period of time that it takes for a prolonged dry period to translate to reduced flow downstream, or conversely, how long excessive rainfall will be detected at the gauge downstream.

# **Model results by basin**

# **_Zambezi_**

In the Zambezi model _Qm_ is a function of _x(BWI)_ (Equation 2), and _Qm_ as a function of _x(precipitation)_ (Equation 3), where ~~,~~ and _n_ = number of months lagged ~~,~~ and _n_ = number of months lagged The results are: 15
.8 to 56.3 percent; Amendola et al. 2023). Even though the Monte Carlo simulations presented by Dang and Lanjouw (2023) show that results remain reasonably robust when this assumption is violated, we extend their framework by using a simulation based on actual survey data for 2022, to provide evidence of robustness. More details are provided in section 4.3.

The second step of the empirical strategy proposed by Dang and Lanjouw (2023) consists in computing the unconditional measures of poverty mobility as: 10
% discount rate and zero<br>carbon tax). Source: IEA|
|Total electricity demand projections|Fuel tax by fuel-type|CO2intensity by power generation<br>technology.<br>Source: IEA|
||Investment subsidy by technology|Global baseline cumulative power<br>production by technology<br>Source: Our World In Data (history)<br>and IEA three future scenarios, World<br>EnergyOutlook 2022|
||Minimum and maximum generation<br>potential by technology (default 0 Gwh<br>and 3000 Gwh)|Lifetime of different kinds of power<br>plant (years)<br>Source: IEA|
|||Lead construction time of different<br>power plant types (years)<br>Source: IEA|
|||Learning rate by technology<br>Source: Main FTT|
|||International fuel price assumptions<br>based on IEA three future scenarios.<br>Source: World Energy Outlook 2022,<br>IEA|

FTT-FLEX utilizes technology-specific cost data from the IEA's Projected Costs of Generating Electricity (2020) database, which includes capital, operating and maintenance, and fuel costs. Average costs and 7
rural and urban population as well average HH size in rural and urban areas.<sup>19</sup> Based on these estimations, we estimate a country’s current housing stock deficit as where _D_ is the current housing stock deficit for country _c, AHIu,c_ is the _urban_ Adequate Housing Index, _AHIr,c_ the _rural_ Adequate Housing Index, S _u,c_ the average urban household size, S _r,c_ the average rural household size, P _u,c_ the current urban population, and P _r,c_ the current rural population.

The overall housing deficit across emerging economies is denoted as the sum of the countries’ housing stock deficits (Table 4). Based on this calculation, we estimate that the housing deficit in emerging markets affects at least 268 million households or about 1.26 billion people. Across 64 emerging economies, about 26 percent of the current stock of housing units is inadequate.

> 19 We do not draw a distinction between number of households and number of housing units/dwellings. The latter data is available through HOFINET for only a small sample of countries. For these countries, the number of dwellings is approximated closely by the number of estimated households we use.
indicate clearly the extent to which Africa is different from other regions. Simple averages across countries suggest manufacturing shares of urban employment nearly three times greater in Asia than in Africa, and increasing through time, whereas Africa’s shares have declined slightly. Accra appears as the African city with the highest share of employment in manufacturing, at 14.9%, whereas shares in Asian cities rise well in excess of 30%.<sup>4</sup>

> 2 Jones (2016).  Manufacturing shares are predicted values from a quadratic function fitted to the cross section data.

> 3 - Integrated Public Use Microdata Series (iPums) available for download at: https://usa.ipums.org/usa <u>action/variables/group</u>

> 4 Ethiopian data dates from 1994; recent manufacturing investments suggest that it could now exceed this.

3
significant at the 99 percent level. When we add additional controls for education, age, and marital status, the coefficient reduces to 0.015 (p‐value < 0.01, table 3, column 3). This suggests that about 55 percent of the overall gradient is due to spatial sorting, and 45 percent is driven by local differences in the quality of care received.

Within communities, the two most likely mechanisms underlying the wealth gradient are self‐selection into different facilities and within‐facility wealth‐quality gradients. Table 4 reports the relationship between wealth and the facilities where women reported receiving care. We find that a one standard deviation increase in wealth is associated with a four‐percentage‐point increase in the likelihood of receiving care at a referral health center or hospital (p < 0.01, column 1). The coefficient decreases to three percentage points when including village dummies (p < 0.01, column 2). Columns 3 and 4 consider the probability of receiving care in the health center officially serving the catchment area in which the village is located. The regression results presented in column 3 are for the full sample, while those in column 4 exclude villages where none of the women reported using the catchment’s health center.<sup>14</sup> Overall, 73 percent of the women report receiving consultations in the catchment’s health center. But the rates differ significantly by wealth: 79 percent among women from households in the lowest wealth quintile use the catchment’s facility but only 64 percent of women in the top wealth quintile do so. A one standard deviation increase in wealth is associated with a 2.9 percentage point lower likelihood of using the assigned health center for the full sample and 3.4 percentage points for the restricted sample.

Data from the exit interviews conducted at the health facilities also contain some information on the rationales for the different choices made by women of different socioeconomic status. During the exit interviews, the
In summary, the two available studies for the 1974 returnees to Portugal are rather consistent in finding a significant negative effect on wages in the years following 1974 for the agriculture and construction sectors whereas the evidence on employment and unemployment is nonconclusive.

# IDPs in Colombia

Colombia has a long history of internal violence that claimed hundreds of thousands of lives since the late 1950s. Such violence has been mainly linked to the emergence of powerful revolutionary groups including the Revolutionary Armed Forces of Colombia (FARC) and the National Liberation Army (ELN) and to paramilitary groups that initially emerged to contrast these revolutionary groups. In the 1980s, internal violence intensified due to the expansionary ambitions of the revolutionary groups that led to a civil war against the state and the increasing violence perpetrated by military and paramilitary groups. As a consequence of this violence, many civilians who had been caught in the fighting were forced to flee. The conflict affected mostly the North-East of Colombia and almost five million people have been estimated to have fled this area since the early 1980s. These internally displaced persons were mostly from rural areas and settled mostly in urban areas and had a level of education comparable with low-skilled workers in urban areas.

Calderon-Mejia and Ibanez (2016) use household survey data and an IV approach to assess the impact of IDPs on the hourly wages of host communities. They find that a 10 percent increase in the share of IDPs reduces hourly wages by 0.88% with this effect being larger for women as compared to men. The effect is smaller (0.63%) but still negative and significant for manual male workers and management and professional female workers (0.64%) whereas is non-significant for female manual labor and male management/professional labor. The most affected workers are independent/self-employed workers with females (2.28%) suffering more than males (1.31%), particularly those with high school education or less (2.0%).

Morales (2017) use survey, census and registry data
*R14**|144|457|Kenya Railways Corporation|70453|Railwaytransport|
|**R14**|148|**Road Transport**||||
|**R14**|148|475|Transport Licensingand Registration|70451|Road transport|
|**D14**|Development<br>Budget|**Ministry of Transport**||||
|**D14**|140|**General Administration and**<br>**Planning**||||
|**D14**|140|572|Shippingand Maritime Affairs Department|70452|Water transport|
|**D14**|144|**Railways **||||
|**D14**|144|457|Kenya Railways Corporation|70453|Railwaytransport|

Source: World Bank (2006) _Economic code mapping_ . There are numerous ways of generating budget lines and/or recording expenditures according to their use. When filling in fiscal data template 10, the data collector needs to group (and in some cases delineate) spending categories so as to map them to the GFSM 2001 economic categories. By way of example, table 3.6 presents an economic code mapping for Rwanda in which 43
(3.27)|(5.47)|(‐2.56)|(‐2.09)|(‐0.80)|(‐2.09)|
|Year 2012‐14|<br>139.8|<br>‐101.7|<br>‐128.8**|<br>96.08||<br>202.4||
||(1.35)|(‐1.46)|(‐2.24)|(1.04)||(0.71)||
|_cons|‐1943.4**|188.1|‐784.5*|‐542.3|‐1461.6|‐5280.7**|‐12451.3***|
||(‐2.25)|(0.39)|(‐1.70)|(‐0.73)|(‐1.38)|(‐2.17)|(‐3.05)|
|N|7957|952|3250|5804|2792|2153|1109|
|R‐sq|0.017|0.171|0.076|0.020|0.035|0.029|0.050|

Notes: Table indicates coefficients estimated from ‘Panel’ model using Ordinary Least Squares and differences over time for time‐ variant variables. * 0.10 ** 0.05 *** 0.01 significance level. Values in parentheses indicate standard errors corrected for cluster correlation at commune‐level.

_Source: Authors’ calculation based on data from Brauer et al, 2015, Hansen et al., 2013, Vu et al., 2014b, Harmonized World Soil Database, Climate Research Unit, Bangalore et al., 2016, Winsemius et al., 2015, and VHLSS 2010, 2012 and
Figure IV presents the geographic distribution of the intensity of migration outflows and inflows of individuals as a share of the mean population between 1995 and 2010. The upper panel of the figure presents the intensity index, which reveals that the municipalities that lost a significant portion of their population to forced displacement were mainly located on the west and between the middle and the south of Colombia. The mean intensity index for all the municipalities is 0.11, suggesting that, on average, all Colombian municipalities lost approximately 11% of their population to forced migration between 1995 and 2010. The variance, however, is high (s.e. 0.25). There are also approximately 45 municipalities that saw an intensity index of 100 percent, suggesting that their population was depleted by forced migration. The figure also shows that the forced migration that originated in the center of the country was of relatively low intensity.

The lower panel of the figure shows the pressure index, defined as the total number of forcefully displaced individuals who arrived in a municipality as a share of the municipal mean population between 1995 and 2010. The pressure index is a good proxy for the congestion of public goods and services as a consequence of forced displacement in the municipalities where these populations arrive. The pressure index has a mean value of 10 percent (s.e. 0.19). Although forced migration was substantial during the period of analysis, the figure reveals that most of the receiving municipalities did not face pressure indexes that are extremely large. In fact, only 38 municipalities have pressure indexes of more than 50 percent, suggesting that forcefully displaced migrants tend to move to urban areas with large populations. Similar results were documented by Carrillo (2009) and Lozano-Gracia et al. (2010). For instance, according to the data from RUV, Bogot ́a, the Colombian capital city, received almost 10 percent of the total number of migrants. Migrants may decide to move to urban areas in search of better economic opportunities (i.
# **Informality and Profitability: Evidence from a New Firm Survey in Ecuador**

**_Denis Medvedev and Ana María Oviedo_**<sup>♦</sup>

**Keywords** : Informality, profitability, firm survey, Latin America

**JEL classification** : O17, D22, L26

> ♦ World Bank staff. The views expressed here are those of the authors and should not be attributed to the Ecuadorian authorities, the World Bank, its Executive Directors, or the countries they represent. The analysis in this paper has been carried out as part of the World Bank report “Ecuador: The Faces of Informality / Las Caras de la Informalidad” (World Bank, 2012). We are grateful to María Bru Muñoz and Ivanna Echegoyen Ferreira for excellent research assistance, and to Palomas Anós Casero, Loli Arribas Baños, Oscar Calvo Gonzalez, Facundo Cuevas, Leonardo Iacovone, Leonardo Lucchetti, Auguste Tano Kouame, David McKenzie, José Guilherme Reis, and Carlos Silva-Jauregui for helpful comments. Special thanks are due to the Ecuadorian micro and small entrepreneurs and workers who generously provided the valuable information used in this manuscript. _Habitus Investigacíon S.A._ carried out the collection and initial processing of the data, and conducted focus groups. Address for correspondence: 70L 1-019, The World Bank Group, 70 Lodi Estate, New Delhi, 110003, India; dmedvedev@worldbank.org.
and administrative expenses to total assets, Equity is the ratio of firm equity to total assets, Net Loans is the ratio of net loans to total assets, Fi are firm fixed effects and Yt are the full set of year dummies. A constrained fixed effects regression with time dummies is estimated under restrictions of symmetry and homogeneity of degree one in the price of inputs.

We use bank-level data from Bankscope to calculate the Lerner Index. Only banks classified as commercial, cooperative, Islamic, savings, and bank holding companies are considered in the analysis.

> 20 We follow the literature on the Lerner index mentioned in footnote 11.

33
2019) find that official statistics overstated growth by 1.7 percentage points in 2008-2016. In contrast, linking trading-partner data on exports to China and China’s GDP, Fernald et al. (2019) conclude that official statistics have become generally more reliable over time.

> 7 An alternative way to calculate the growth contribution of TFP is to attribute to technology both the direct increase in GDP due to TFP growth and the indirect effect that results from the endogenous increase in capital in

3
Policy Research Working Paper 8024

# **Abstract**

This paper uses successive rounds of National Sample Survey Organization data from 1993–94 to 2011–12, and draws from census data. This paper (i) provides a description of nearly two decades of patterns and trends in female labor force participation in India; (ii) estimates the extent of the recent decline in female labor force participation; and (iii) examines and assesses the contribution of various demographic and socioeconomic factors in explaining the female labor force participation decision and the recent the drop. The analysis finds that female labor force participation dropped by 19.6 million women from 2004–05 to 2011–12. Participation declined by 11.4 percent, from 42.6 to 31.2 percent during 1993–94 to 2011–12. Approximately 53 percent of this drop occurred in rural India, among those ages 15 to 24 years. Factors such as educational attainment, socioeconomic status, and household composition largely contributed to the drop, although their effects were more pronounced in rural areas. Specifically, the analysis finds a U-shaped relationship between levels of educational attainment and female labor force participation. The decomposition of the contribution of these various determinants to the female labor force participation decision suggests that stability in family income, as indicated by the increasing share of regular wage earners and declining share of casual labor in the composition of family labor supply, has led female family members to choose dropping out of, rather than joining, the labor force. The findings of this paper suggest that conventional approaches to increasing female labor force participation (such as education and skills and legal provisions) will be insufficient. Policies should center on promoting the acceptability of female employment and investing in growing economic sectors that are more attractive for female employment.

This paper is a product of the Social Development Unit, South Asia Region. 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
# **4. Data and Variables**

To test these hypotheses, we create a unique database using a number of data sources. Our primary data source is the February 2002 edition of the Morningstar database for each U.S. mutual fund with a stated objective of investing primarily in emerging market equities.<sup>13</sup> All of the sample funds are primarily equity funds with more than 90 percent of their investment in equities. We include three classifications of U.S. mutual funds: Diversified Emerging Markets, Pacific/Asia excluding Japan, and Latin America. We exclude Diversified Pacific/Asia mutual funds because the majority of their investments are in countries that are not considered emerging markets, such as, Japan, Hong Kong and Singapore. Similarly, we exclude European funds because of their emphasis on developed Western European markets. In the Pacific/Asia excluding Japan sample of funds, we exclude 11 funds that invest 90 percent or more of their assets in 3 or less countries. We also exclude multiple classes of the same emerging market mutual fund. Certain funds have multiple classes that have identical portfolio holdings but different fee structures. For example, one fund may have an exit fee but the other does not. Finally, since our analysis focuses on active portfolio allocation decisions of U.S. mutual funds, we also exclude exchange-traded funds and funds that explicitly follow passive indexing strategies. Our final sample consisted of 111 funds consisting of the three categories; Diversified Emerging (73 funds), Latin America (14 funds), and Asia (24 funds). The country level data is taken from various sources including Datastream, IMF and La Porta et al. The data for individual firms is obtained from Worldscope, Datastream, Bloomberg and hand collected data from 20-F filings.

> 13 Consistent with the MSCI Emerging Markets Free Index, we define the following 30 countries as emerging markets: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Ghana, Greece
# Self-Employment and Migration<sup>_∗_</sup>

Samuele Giambra<sup>_†_</sup> David McKenzie<sup>_‡_</sup> August, 2019 internal international se- _Keywords:_ migration; migration; self-employment; migrant lection; randomized experiment.

_JEL codes:_ F22, J61, O15.

> _∗_ We thank Chris Blattman, Suresh de Mel, Pam Jakiela and Owen Ozier for sharing their data; John Giles and Xin Meng for helpful advice on Chinese migration data; and Anna Luisa Paffhausen for research assistance. We also thank Jan Stuhler and participants at the 12<sup>th</sup> Conference on Migration and Development for helpful comments.

> _†_ Department of Economics, Brown University. E-mail: `samuele` ~~`g`~~ `iambra@brown.edu`

> _‡_ Development Research Group, World Bank. E-mail: `dmckenzie@worldbank.org`

1