rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Description of the Sample**

The means for the variables that we used to analyze the determinants of learning are presented in Table 1. The PISA data contain missing values among the family background characteristic variables, and in. Table 1, we show where those missing values occur. While some might choose to impute the missing values, we decided not to do so in this case. Therefore, if a variable had any missing values, we dropped the observation in its entirety from our analysis.

We realize that deleting cases with missing values can have dangers, as demonstrated by Little and Rubin (1987). Deleting cases is based on the assumption that the deleted cases occur at random and are a relatively small representative proportion of the entire dataset. However, this may not necessarily be the case. The missing data may be indicative of some pattern and cannot safely be assumed to reflect randomness. In such circumstances, deletion can introduce substantial bias into the study. Moreover, the loss in sample size can appreciably diminish the statistical power of the analysis.

As a rule of thumb, if a variable has more than 5 percent missing values, it is advisable not to delete cases, and many researchers are much more stringent than this (Little and Rubin, 1987). Deleting incomplete cases has its attractions, mostly the virtue of simplicity, but one loses information in doing so. This approach also ignores the possible systematic difference between the complete cases and incomplete cases, and the resulting inference may not be applicable to the population associated with all cases, especially with a smaller number of complete cases to take into account. Some techniques exist to impute missing values, ranging from correlations, single imputation, and a multiple imputation procedure (Rubin, 1987). However, very few of our variables had missing values that made up more than 5 percent of the total. Overall, the sample for 2003 dropped from 10,761 students to 8,828, and in 2006, the sample went down from
low income clients. In addition, high minimum balance requirements create strong barriers to access even for basic financial services such as savings accounts. The situation is exacerbated by multiple documentation requirements resulting from strict anti-money laundering regulation which reduces the eligibility of low income clients for formal financial services.

5. Financial exclusion is associated with lower and more uncertain incomes, lower education and closer links to the informal sector. A household survey conducted in Bogotá for this study reveals that 70% of the unbanked earn less than one minimum wage per month, are three times more likely to be unemployed than the banked, and have lower education levels. The unbanked save and borrow largely in the informal sector. At the same time, however, high home ownership rates show that the unbanked have the capacity to build assets, demonstrating that they have ‘bankable’ characteristics.

15
office for Latin America and the Caribbean with the support of the World Bank and others. The annual data for the ten countries cover the period 1985–2000.<sup>18</sup> An advantage of this data is that it is quite disaggregated while at the same time it has been measured using a reasonably consistent methodology across countries and over time. In addition the public expenditure data as provided is comprehensive, covering the vast majority of the items in which the public sector spends money in the rural areas. The level of detail of the data allows us to obtain a fairly accurate measure of the evolution of the level and composition of government expenditures in rural areas over the period.

The data for public expenditures in the rural sector cover all rural expenditures, including expenditures for agriculture as well as rural infrastructure, animal and plant sanitary protection, environmental expenditures, and social services to rural areas. The latter group covers

> 18 The countries for which the data covers the full period are Costa Rica, Dominican Republic, Ecuador, Honduras, Jamaica, Panama, Paraguay, Peru, Uruguay, and Venezuela. For a detailed description of the data see Kerrigan (2001).

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25

**Fig.** 2.6. Households' plans to improve housing conditions in the near future (by quintiles based on per capita income; % of each quintile)

<!-- Start of picture text --> 35 - 35 - 35 -<br>30 -30 -30-<br>5.7<br>25 25 - 9 25 -<br>6.5<br>20 12920 V10 20<br>15e15 15 33<br>10.9 97 9.5 5<br>10 2.710 S 10 8.3<br>20 \0i14 7<br>1 2 3 45 12345 1 2 5<br>Moscow, 1996 Vladimir, 1996 Gorodets, 1996<br><!-- End of picture text --> Note: 1- lowest quintile, 5 - highest quintile <!-- Start of picture text --> Sources: on a chargeable basis EL on free-of-charge basis<br><!-- End of picture text --> Sources: The Moscow Longitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Survey, December 1996.

Fig. **2.7.** Share of Moscow households who plan to improve housing conditions by income (by deciles based on per capita income; % of each decile) <!-- Start of picture text --> 35-<br>32.8<br>30-<br>8.128<br>25<br>20-<br>10~~~~~~~~~~~~~~1<br>15<br>10<br>I 5 6 7 S 9 10<br>-01993 ~ Z1996<br><!-- End of picture text --> Note: 1- lowest decile, 10 - highest decile Sources: Household Survey in Seven Cities, Moscow,
investments in increasing labor markets and employment opportunities in African cities.

Specifically, this initial benchmarking opens up a number of avenues for further policy oriented research and operational engagements. First, the simulation of counterfactual scenarios, by comparison to the baseline, can provide some orders of magnitude as to the employment accessibility impacts of urban plans and/or transport investments. This can be useful for local decision makers which aim to compare different projects or assess the benefits of a specific project.

Second, in follow-up work, we will seek to link urban form indicators (such as job-housing balance, land use patterns and others) to the performance of urban areas in terms of employment accessibility. We could equally attempt to quantify the requirements in terms of transportation infrastructure investments needed to offset the impacts of spatial structures that are less conducive to employment accessibility. This work would be meaningful for policy makers as it could provide some estimates of the benefits of acting on urban forms and of the costs of failing to do so.

Third, this work provides a solid basis for thinking about how access to employment opportunities impacts employment outcomes (status of employment, formal or informal job, wages...). Some recent works are going in this direction (Franklin 2015) and the constitution of this accessibility database could constitute the first step in expanding this growing body of literature over several African cities.

Last, but not least, in the context of defining metrics to inform the Sustainable Development Goals (SDG), in particular, SDG 11.2, we hope that this analysis can serve as a demonstration 29
School of Management Working Papers, number 436, Yale School of Management.

Clarke, G. and S. Wallsten (2002), “Universal(ly Bad) Service: Providing Infrastructure Services to Rural and Poor Urban Consumers”, World Bank, Policy Research Working Paper, 2868 Chisari, O., A. Estache and C. Romero (1999), “Winners and Losers from the Privatization and Regulation of Utilities: Lessons from a General Equilibrium Model of Argentina” (1999 _), The World Bank Economic Review,_ Vol. 13, No 2.), pp. 357-378 Chisari, O., A. Estache and C. Waddams-Price (2003), “Access by the poor in Latin America’s utility reform: subsidies and service obligations” in Ugaz, C. and C. Waddams Price, ed. (2003), **Utility Privatization and Regulation: A Fair Deal for Consumers?,** Edward Elgar, Northampton, MAS, USA (with O. Chisari and C. Waddams Price) Cremer, H., F. Gasmi, A. Grimaud and J.J. Laffont (2001), "Universal Service: an Economic Perspective Overview", _Annals of Public and Cooperative Economics_ , vol. 72, n. 1 Danziger, S. H. and R. H. Haverman (2001), eds. **Understanding Poverty** , Russell Sage Foundation, New York Deaton, A. (1996) **Microeconometric Analysis for Development Policy: Approach to Analyzing Household Surveys** , John Hopkins University Press, Baltimore Dinar, A. (2000), **The Political Economy of Water Pricing Reform** , Oxford University Press Duclos, J.Y. , P. Makdissi and Q.Wodon (2004), “Poverty-efficient Program Reforms: The Role of Targeting and Allocation Rules _," Journal of Development Economics_ , forthcoming Duflo, E (2003), Scaling Up and Evaluation, MIT, mimeo
are unable to smooth the shock that their banks receive. We also find evidence that other firm outcomes (liabilities, assets, and sales) were negatively impacted by the municipality’s exposure to the energy sector. An increase of one standard deviation in a municipality’s exposure to the energy sector reduces total firm liabilities by around 2.9 percent and total firm assets by around 2.6 percent. However, we do not find any impact on total sales. Overall, we uncover evidence suggesting that non-energy firms experienced a larger contraction in their liabilities

> 13 Orbis information tends to refer to the month of December. For the credit registry outcomes (total loans, loans for working capital, and loans for investment) for each firm-year pair, we selected the December value.

17
2018; Bernhardt et al., 2018; Heintz, Kabeer, and Mahmud, 2017; Klasen, 2019; Bridges, Lawson, & Begum, 2011).

The role of norms may explain why, in the past two decades, FLFP has continued to be low in the region despite significant gains in GDP growth, women’s educational attainment, and total fertility rate<sup>2</sup> - factors typically associated with improvements in women’s economic participation (Mammen and Paxson, 2000; Goldin, 1994; Olivetti, and Petrongolo, (2016); Luci _et al._ , 2012; Hosney, 2016; Zaheer and Qaiser, 2016; Verick, 2014). Indeed, trends observed in South Asia also do not conform to the traditionally hypothesized U-shaped relationship between economic development and FLFP (Boserup, 1970; Durand, 1975; Goldin, 1995). Examination of data from two time periods – 1995 and 2020 – shows that FLFP in most South Asian countries tends to fall substantially below global employment rates in the U-shaped curve (see Figure _1_ ), at the same level of GDP per capita compared to the rest of the world.

Social norms often influence behavior in combination with or through interactions between households, markets, and formal and informal institutions (The World Bank, 2012). To understand the drivers of gender inequality, it is therefore important to look at changes over time in various macro and micro level

> 1 For women 15+. Source World Development Indicators, using the International Labour Organization, ILOSTAT database. Data retrieved on June 15, 2021. The main outlier in the region is Nepal with rates over 75%. 2 See Annex Figure A1 for graphs on observed trends over time in FLFP, gross domestic product (GDP) growth rate (data from WDI), female educational achievement (data from DHS), and the national total fertility rate (TFR) (data from WDI) across South Asian countries.

2
# **3.1 Price Survey**

_Design of Study_

**A systematic difference in retail prices for identical goods in two markets is probably the most reliable indicator of barriers to trade between those markets.** Absent physical and regulatory barriers to trade, price differences will be arbitraged away. This is why we look at prices first.

**For a rigorous comparison of prices on both sides of the river we selected 57 precisely defined products that allowed us to collect and compare prices for “identical” products.** Even on Congolese markets, where the degree of product differentiation is considerably lower than in highincome economies, great care must be taken to compare like with like, i.e. to define meticulously the type and quantity of the good whose price is to be recorded. The 57 goods were chosen in close consultation with local experts so as to offer a representative sample of Congolese consumption baskets. The list of products and summary price statistics are given in Appendix 2.

**Price data were collected carefully from representative samples of retail markets in both Kinshasa and Brazzaville.** We relied on a local expert who recorded prices using a consistent methodology to make prices comparable among vendors, as prices are commonly determined through bargaining. Four large retail markets were selected in each city, two in the respective city center and two in the suburbs.<sup>13</sup> We obtained some complementary price data from the RC Planning Ministry, collected in Brazzaville over the same time period as our own price data, and we consider these data points as our “ninth sample market”. Where possible, prices were recorded for each product from up to three vendors per market.<sup>14</sup> Some of the Brazzaville prices were obtained from wholesalers, and are thus not directly comparable to the prices recorded at the retail level. All prices were recorded in local currency, between 26 August and 3 September 2010. We asked vendors for the origin of their
that the deflationary pressure starts to dissipate as the harvest season progresses and the supply-side effects begin to take effect.

# **4. Robustness Analysis**

This section presents results of tests performed to check the robustness of the main findings. One of the threats to the internal validity of the study lies with the definition of treatment, namely the triple interaction ( _Shock_ x _After_ x _Growing Cycle_ ), which may be capturing changes in the outcomes of interest caused by factors other than the weather anomalies, or simply a spurious relationship. The lack of comparable household surveys to measure the outcome variables for at least two data points over a pre-shock period forbids testing the identifying assumption of common 17
|Dominican<br>Rep.|Dec 81|5648 F|U.N. 1988 revision<br>(prelim.)|<br>U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|
|Ecuador|Nov 82|8051 F|U.N. 1988 revision<br>(preLim.)|<br>Based on 1987 DHS|1980-85,<br>U.N. 1988 revision<br>(prelim.);<br>1985-90;<br>Bank<br>projection|
|Egypt, Arab<br>Rep. of|Nov 86|48205 F|U.N. 1984 assessment|Based on UNPVSR<br>4/87|BasedanU.N. 1984<br>assessment|
|I Salvador|Jun 71|3555 F|U.N. 1988 revision (prelim.)|Celade printout 2/87|Celade<br>printout<br>2/87|
|Equatorial<br>Guinea|Dec 60|246 F|Bank projection<br>from census<br>and 1982 household listing<br>(350 thousand)|Bank est.|Bank est.|
|thiopia|May84|42169 F|Bank projection from census|Bank assessment of U.N.<br>data, with correction<br>for<br>1984 famine|Bank sector report, taking<br>into account 1984 famine|
|aeroe Island|s Sep 77|42 J|UNPVSR 4/87 (officiat<br>est.)|--||
|Falkland<br>Islan|ds<br>Dec 80|2 F|UNPVSR 1/88 (official<br>est.)|||
|Fiji|Aug86|715 F|Bank projection<br>from<br>officiaL<br>1986 est.|
financial institutions respect their public commitments according to these guidelines.

<u>Example US</u> – Government regulation: Through the community reinvestment act (CRA) of 1977, the US government obliged banks to reinvest deposits in the communities where they are received from, and having institutions serve the areas in which they have a physical presence. CRA established a point system which rates banks according to their provision of lending and savings services to low income neighborhoods. These ratings were subsequently published and taken into consideration in the approval of decisions on bank mergers and acquisitions and other regulatory decisions. Bank compliance with the CRA is subject to regular reporting and monitoring by the Federal Financial Institutions Examination Council.

9. **With the exception of housing finance, no regulatory incentives exist to enhance the provision of credit to low income clients.** Following the significant decline in available housing finance after the banking crisis, the Government put in place a framework to increase micro housing lending (see Box 3). As the household survey shows, demand for personal loans is high, in particular among the lower income groups (see Chapter 3). Measures to provide incentives for the “downscaling” of banks toward the microcredit market could be considered, based on the experience of other countries<sup>29</sup> .

> 29 However, as the case of Brazil has shown, the provision of microcredit at subsidized interest rates, or based on earmarked resources, is not a sound basis for expansion.

34
Index`|<br>`-0.160*`<br>`(0.0860)`|<br>`0.00443`<br>`(0.00382)`|<br>`0.00668`<br>`(0.00751)`|<br>`0.495`<br>`(0.572)`|
|`Private Monitoring Index`|`0.119`<br>`(0.109)`|`-0.0164**`<br>`(0.00801)`|`-0.0187`<br>`(0.0122)`|`0.465`<br>`(0.480)`|
|`Official Supervisory Power Index`|<br>`-0.0511`<br>`(0.0443)`|`-0.00285`<br>`(0.00342)`|`-0.00377`<br>`(0.00588)`|`0.206`<br>`(0.308)`|
|`Entry Requirements Index`|`0.0289`|`0.00805`|`0.0128`|`3.030`|
||`(0.223)`|`(0.0102)`|`(0.0253)`|`(2.105)`|
|`Restrictions on Activities Index`|<br>`-0.0509`|`0.00100`|`-0.00498`|`-0.276`|
||`(0.0577)`|`(0.00286)`|`(0.00575)`|`(0.510)`|
|`Government Owned Banks`|`-0.00168`
35 Table 6. Subsidized food as a source of calorie availability by expenditure quintile group for urban Egypt, 1997

|||Percent<br>availability b|of lotal dail<br>y ?er capit|y per capita<br>a expendit|calorie<br>ure quintile||
|---|---|---|---|---|---|---|
|Subsidized food|Lowest<br>'[1]|Second<br>[2]|*rhird<br>[3]|Fourth<br>[4]|Highest<br>[5]|All|
|Baladi bread|33.0|29.2|26.3|22.7|18.1|25.0|
|Baladi wheat flour|3.1|3.9|2.5|1.2|<br>1.4|2.3|
|Edible oil|3.7|4.2|4.4|3.4|2.5|3.5|
|Sugar|3.6|3.2|3.2|2.8|2.6|3.0|
|Percent of total daily per capita calorie<br>availability from subsidized food|43.5|40.4|36.4|30.0|24.6|33.8|
|Total daily per capita calorie availability|<br>2,704|<br>3,012|3,302|<br>3,533|<br>3,664|3,306|

# Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,078. Urban population is divided into quintile groups on the basis of total per capita fitted expenditures.
logarithm of the total population ( _ln(population) 2010_ ), and the regulatory quality indicator from the Worldwide Governance Indicators ( _Regulatory Quality 2010_ ). All the control variables are as per year 2010.<sup>21</sup> The summary statistics are provided in Table 1, Panel A. On average countries in our sample have seen an increase in their total regulatory capital ratio of approximately 1.5 percentage points with a wide range between -12.5 percentage points and 16.2 percentage points. The Tier 1 capital ratio also increased about 2 percentage points with a range of -10.7 percentage points to 15.3 percentage points (Table 1, Panel A). The change in common equity ratios has been more muted, on average 1.2 percentage points with a range of -8.8 percentage points to 9.6 percentage points. On average, there has also been a decline in the stringency of the Tier 1 definition from 2010 to 2016. Roughly 16 percent of the countries in our sample experienced a system wide banking crisis between 2007 and 2009. Overall there is moderate correlation between the variables except between changes in total regulatory capital and Tier 1 capital, and between GDP per capita and regulatory quality which are strongly correlated (0.646 and 0.828, respectively as reported in Table 1, Panel B).

21 For more information on the Worldwide Governance Indicators, see https://info.worldbank.org/governance/wgi.

26
census sample were cluster randomly assigned by neighborhood to weekdays for the weekly surveys, in an effort to spread daily recall randomly across days (if, for example, the weekend interlude makes it easier or harder to recall certain information). Data collection began on Thursday, March 5th, 2015, referencing daily blackouts and hours worked recall for Thursday, February 26th through Wednesday, March 4th, and weekly sales, orders, and expenses recall for that same seven day period. The four other weekday survey groups were started on Friday, March 6th, Monday, March 9th, Tuesday, March 10th, and Wednesday, March 11th. Data collection continued in this weekly manner through to Wednesday, April 22nd, 2015. Field staff conducted make-up surveys

> 7Individuals were included in the original census sample if they met three criteria. First, they had to report the ability to produce at least one of three commonly sold bespoke garment products: a man’s shirt, a woman’s slit and kabbah (a fitted top and long skirt), or a caftan (the attire traditionally worn by Ghanaians from the Northern part of the country). Second, they had to report owning a garment making business, though the business need not have a permanent physical location. Third, they had to report that the business was currently operational or was planned to be in operation over the next year.

> 8Appendix Table 1 compares baseline firm characteristics between the full census sample and those surviving to the panel survey. There are few observable predictors of survivorship, with the exception that older firms owners and older firms are less likely to exit.

> 9Appendix Table 2 compares baseline firm characteristics between all survivors and those survivors with electricity access (the analysis sample). Firms with access to electricity are, unsurprisingly, larger, more formal, and more likely to be owned by a man.

7
# Figure VI-8: Oil and Credit Cycles

<!-- Start of picture text --> Real Oil Prices<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real oil price (y/y)<br>50<br>0<br>-50<br><!-- End of picture text --> _Source: BP historic data_ <!-- Start of picture text --> Saudi Arabia: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>10<br>Percent 0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Iran: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>10<br>5<br>Percent 0-5<br>-10<br>-15<br><!-- End of picture text --> <!-- Start of picture text --> Kuwait: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>10<br>5<br>Percent 0-5<br>-10<br>-15<br><!-- End of picture text --> <!-- Start of picture text --> Bahrain: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<
Additionally, surveys offer a wide array of information on the labor market profiles and demographic characteristics of the individuals.

Using SILC data, we are able to classify each individual based on his/her labor market profile, such that we can identify if the individual is employed, unemployed, or inactive, and if employed, whether formally or informally. Within the scope of this paper, we divided the 14+ noninstitutionalized Turkish population into four labor market categories, namely, formal-employed, informal-employed, unemployed, and inactive. We classified the individuals as employed if they had

> 7 Non-institutionalized population comprises all the population excluding the residents of schools, dormitories, kindergartens, rest homes for elderly persons, special hospitals, military barracks and recreation quarters for officers.

11