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

While it is widely appreciated that poverty is an individual deprivation, household aggregate data are almost invariably used to infer individual poverty. It is almost always assumed that each individual within the household has the same level of economic welfare as measured by household aggregate consumption per person (or per equivalent single adult). An array of antipoverty programs, now found almost everywhere, are targeted on this basis, though typically using readily available proxies for household consumption or income per person.<sup>2</sup> Partly in response to concerns about high chronic undernutrition in certain regions, including Africa, there is an expanding effort at social protection in developing countries.<sup>3</sup> This effort is typically focused on transfers targeted to poor families.<sup>4</sup> For its part, the World Bank has made reaching poor families—as often identified by the poorest two quintiles of people based on household consumption per person—the main objective of its social protection operations.

Reaching deprived individuals using antipoverty programs that explicitly target poor households is an attractive option for three reasons. First, there is a data constraint, namely that standard data sources do not allow us to measure individual consumption. Second, interventions at the individual level may be seen to be paternalistic and intrusive (as they require intervention within families) and may well be costly (to the extent that they rely on fine targeting, constrained by the fact that individual deprivations are not comprehensively observed in large populations). Third, a large literature has documented that poorer households in terms of consumption, income or wealth are more likely to include deprived individuals.<sup>5</sup> Aggregate household resources constrain consumption for all household members. For these reasons, it is not surprising that, in practice, many social policies hope to reach deprived individuals by targeting poor households, or (more commonly) households with characteristics known to be associated with poverty.<sup>
adopting e-procurement. For example, better performing procuring entities or those with a lower propensity for corruption might more rapidly adopt e-procurement—wishing to reap its benefits as early as possible, or in view of signaling their readiness to reform to superiors and the market. 52. Third, procuring entities may sort tenders into e-procurement for strategic reasons (strategic sorting), reflecting their organizational goals and institutional quality. For example, they may initially decide to only use the new e-procurement platform for smaller tenders,23 in view of lowering the risks of making mistakes, due to their lack of experience with the new system. Corrupt procuring entities could try

> 23 In the survey of procuring entity (PE) officials conducted, 108 procurement officials stated that they assigned tenders to e- procurement according to estimated tender value.

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negative impact of 15.7 percent on per capita labor income, while IHD had an impact of 3.1 percent, while WEI had an impact is of 9.4 percent. The example of Hurricane Matthew reflects the importance of considering a measure of asset exposure in the indexes. The impacts calculated from the levels of the three indicators for Hurricane Matthew reflect a significantly higher impact when considering the MSWS index. Indeed, when considering MSWS, the impact seem to be 5 times higher than the impact calculated from HDI, even if the coefficient estimated within IHD is significantly higher than the coefficient estimated within MSWS. As discussed in Section 2, each indicator relies on different assumptions that may have significant variations. In the particular case of Hurricane Matthew, the lower effect presented by the IHD and WEI indicators appear to indicate an overestimation of the effect when not considering any exposure measure in the MSWS.

Table 5 Impact of Hurricanes on Poverty and Income at the Household Level

||Labor Income|Total Income|Extreme Poverty|Poverty|
|---|---|---|---|---|
|||_Non We_<br>|_ighted Results_<br>||
||(1)|(2)|(3)|(4)|
|MSWS|‐0.0300***|‐0.0403***|0.0164***|0.0186***|
|Standard Error|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.430|0.472|0.258|0.317|
|IDH|‐0.0353***|‐0.0473***|0.0205***|0.0232***|
|Standard Error|(0.0002)|(0.0002)|(
entire employment and migration history of a sample of 5,000 migrant workers who had returned to Bangladesh at the time of the data collection. The survey is one of the very few comprehensive surveys on temporary migrants globally, and to the best of our knowledge, it is the first of its kind to have been conducted in a country in which emigration is based almost exclusively on regular temporary work contracts.<sup>3</sup> The dataset includes detailed information on migrants’ personal and family backgrounds, their labor market outcomes before migration, their expectations prior to departure about earning and saving prospects abroad, migration expenditures, sources of income and savings, employment histories at the foreign destination, and labor market activities and earnings after their return to Bangladesh.

> 3For less regulated contexts, in which emigration is more often undocumented, the Mexican Migration Project (MMP) and the Egypt Labor Market Survey (ELMS) have a similar design. While the Egyptian survey covers all workers in the country and is not specifically focused on temporary migrants, it includes a module on past migration of household members currently living in the Arab Republic of Egypt.

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# DOES FINANCIAL REFORM RAISE OR REDUCE SAVING?

By Oriana Bandiera*, Gerard Caprio Jr.**, Patrick Honohan** and Fabio Schiantarelli* (*Boston College, **World Bank)

## _Abstract_

The effect of financia **l** iberalization on private saving is theoretically ambiguous, not only because the link between interest rate levels and saving is itself ambiguous, but also because financial liberalization is a multi-dimensional and phased process, sometimes involving reversals. Some dimensions, such as increased household access to consumer credit or housing finance, might also work to reduce private savings rather than increasing them. Furthermore, the long-term effect of liberalization on savings may differ substantially from the impact effect.

Using Principal Components, we construct a 25-year time series index of financial liberalization for each of eight developing countries: Chile, Ghana, Indonesia, Korea, Malaysia, Mexico, Turkey and Zimbabwe. This is employed in an econometric analysis of private saving in these countries.

We find that the pattern of effects differs across countries. In summary, liberalization appears to have had a significant positive direct effect on saving in Ghana and Turkey, and a negative effect in Korea and Mexico. No clear effect is discernible in the other countries. There is no evidence of significant, positive and sizeable interest rate effects.

For the present, our results must be taken as an indication that there is no firm evidence that financial liberalization will increase saving. Indeed, under some circumstances, liberalization has been associated with a fall in saving. All in all, it would be unwise to rely on an increase in private savings as the channel through which financial liberalization can be expected to increase growth.

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WPS3712

# **Governance and the City:**

# **_An Empirical Exploration into Global Determinants of Urban Performance_**

## **Daniel Kaufmann, Frannie Léautier, and Massimo Mastruzzi**<sup>**1**</sup>

## **Abstract**

We contribute to the field of urban governance and globalization through an empirically-based exploration of determinants of performance of cities. We construct a preliminary worldwide database for cities, containing variables and indicators of globalization (at the country and city level), city governance, city performance (access and quality of infrastructure service delivery), as well as other relevant city characteristics. This city database, encompassing hundreds of cities worldwide, integrates existing data with new data gathered for this research project. We present a very simple conceptual framework and a set of hypotheses, and then test them econometrically. The findings suggest that good governance and globalization (at both the country as well as at the city level) do matter for city-level performance in terms of access and quality of delivery of infrastructure services. We also find that globalization and good city governance are significantly related with each other. There appear to be dynamic pressures from globalization and accountability that result in better performance at the city level. Furthermore, the evidence suggests that there are particular and complex interactions between technology choices, governance and city performance, as well as evidence of a non-linear (u-shaped) relationship between city size and performance, challenging the view that very large cities necessarily exhibit lower performance and pointing to potential agglomeration economies. Our framework also suggests a way of bridging two seemingly competing strands of the literature, namely viewing the city as a _place_ or as an _outcome_ . We conclude pointing to the need for expanding the database and the econometric framework, as well as to more general future research directions and policy implications emerging from this initial empirical investigation in the field of governance and the city.

## World Bank Policy Research Working Paper 3712, September 2005

_The Policy Research Working
Figure 11. Percentage of Population (15+) that has Saved Money in a Financial Institution <!-- Start of picture text --> 2011 2014<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High Rest of Turkey BRIC Rest of High Rest of<br>Developing Income ECA Developing Income ECA<br>World World<br>Lowest quintile q2 q3 q4 top quintile<br><!-- End of picture text --> _Source_ : FINDEX 2011, 2014 Between 2011 and 2014, Turkey succeeded in increasing its savings rate at financial institutions at a faster rate than comparator country groups. However, bank use for savings in Turkey, at 9.1 percent, is still lower than the developing world average of 10.3 percent in 2014. Moreover, decomposing the growth of formal savings in Turkey points to specific cohorts that drive the heterogeneous increase. The increase in the top quintile of income earners was higher in comparison to the lower quintiles. Figure 11 displays the increasing disparity among the income quintiles in Turkey between 2011 and 2014. In addition to the heterogeneity in terms of earnings, individuals older than 65, and the ones who have higher education were able to expand their formal savings rates at a higher pace. Therefore, it will be important for Turkey to build on its success of increasing formal savings by ensuring that this growth is evenly spread across different age, income and level of education cohorts.

There is high dissimilarity in savings using financial institutions, but not compared to countries with similar coverage. Compared to all 145 countries in the FINDEX database, the dissimilarity index is high at 33.7 (Figure 30). However, countries with similar coverage also have very high inequity in
in income with respect to changes in light is 0.17 (Table 1, column 3). This elasticity stays in the range 0.169 to 0.171 whether six province-level aggregates of small MDs (used to preserve confidentiality) are included or excluded, and whether topcoded household incomes are modeled at the topcoded value or 1.5 times that amount (not shown). The analogous t-statistic ranges from 2.96 to 3.08.

For the present study, several steps were taken to convert the pixel-level lights data into cities. Figures 4–6 document these steps for Tanzania. The 30 satellite-years of lights data were first combined into one binary grid encoding whether a pixel was lit in at least one satellite-year (Figure 4). These ever-lit areas were then converted to polygons: contiguous ever-lit pixels were aggregated, and their digital numbers were summed within each satellite-year. Polygons not corresponding to a known city, based on census populations with latitude-longitude pairs, were dropped.<sup>20</sup> Figure 5 shows all the lit polygons and city points. For those light polygons that did contain one or more census cities, the populations of all such cities were summed to obtain a population. Most lights correspond to at most one census city.<sup>21</sup> Figure 6 shows those lights that correspond to known cities. In most countries, census information about cities with populations as small as 10 thousand was available, but in some, the cutoff was higher. For all regressions below, I restrict to cities with combined population over 20 thousand and lit in at least 2 years. The dropped lights most likely correspond to forest fires or random noise not flagged by NOAA’s algorithm, or smaller towns, and contain 13 to 16 percent of total DN in the 15-country sample. The total DN was recorded for each city polygon for each year, averaging across multiple satellite-years where necessary.<sup>22</sup> The light in each country with
-23-

# 4.2.3 Subsidies

To the extent that men control cash in thelr own Interests, cash expenditures will benefit disproportionately males. Generalised subsidies, which merely lowered the price level, would thus be gender-biased. Subsidies are, however, additionally targeted on certain goods, particularly food. Food subsidies directly benefit net purchasers of food, namely urban households. In much of Africa the urban population is still disproportionately male, so that this involves a gender bias. Indirectly, some of the subsidy may accrue to sellers, who are disproportionately women.

# 4.3 The Composition and Level of Government Revenue

To the extent that males make net cash transfers to females, income taxes fall directly on males whereas expenditure taxes fall on females. The use of generalised sales taxes to restore budget balance may therefore have some gender bias. Further, male and female expenditure patterns are quite distinct, and so the incidence of partlcular sales taxes can be highly gender-specific. Existing budget survey data for many countries makes a quantiflcation of tax incidence stralghtforward, (except for general equilibrium effects).
and opportunity, ranking 75 globally. Nevertheless, similar to other countries in the region, Georgia performs worst at health and survival, ranking 124 of 144. The main underlying factor for the low health performance is the high sex ratio at birth of boys to girls, which places the country at place 138 out of a total of 144. Thus, gender inequality remains a challenge in Georgia.

This paper complements the literature on gender wage gaps in Georgia by investigating the contribution of occupational and sector segregation. To this end, the paper is organized as follows: section II presents a brief overview of the gender wage gap trends observed in Georgia in the last decade, section III documents the existing gender segregation in terms of occupations and sectors in Georgia, and section IV describes the methodology for the remaining analysis. Main findings of the role of segregation in the gender wage gap are presented in section V, and finally section VII concludes and comments on policy implications of our findings.

# **II. Data and descriptive statistics**

This study uses data from the Georgian Household Budget Survey (HBS) for the period 2004-2015. The HBS is a quarterly survey of about 3,000 households that follows a rotating panel design. Surveyed households remain in the sample for four quarters before being replaced by a new cohort. We limit the sample to 25–55-year-old individuals to avoid issues related to the selection into 2
and Arbitrations variables is retained.

_Third_ , do the main results stand when alternative time periods, namely the individual years of 2006, 2007, and 2008, are considered? I do not consider years prior to 2006 because the explanatory indicators of FDI regulation cover prevailing regimes during or before 2006. Table 10 reports results obtained by estimating the model using data for 2006. GDP, GDP per capita, and tariffs averaged over the 32 Fuel products are SITC Section 3; metals and ores are SITC Divisions 27, 28, and 68.
The paper also reports a second surprising result: that often balanced attitudes towards bonding and bridging predict life satisfaction better than extreme bonding or bridging attitudes. For example, individuals who attach more importance to the family than to friends, or more importance to friends than to the family, appear to be less satisfied with their lives than individuals who take a more balanced view. The same applies for the importance of work with respect to leisure. Individuals who hold balanced views on the importance of work and leisure seem happier than individuals who have unbalanced preferences. Overall and with one exception, bonding variables tend to deliver more happiness than bridging variables, a finding consistent with previous literature on social capital and life satisfaction.

These results are obtained by carefully examining the available literature and the available proxies for bridging and bonding social capital in the latest version of the combined World and European Values Surveys (WVS-EVS) and by applying a multistep factor analysis for the identification of the bonding and bridging latent variables. This approach has induced us, in particular, to pay more attention than in the past to self-reported values and beliefs such as the importance attached to family, work and children’s qualities. This is in line with the theoretical and empirical arguments on the role of culture in economics (Guiso et al. 2006, Tabellini, 2010).

The paper is organized as follows: section 2 conducts an in-depth review of the literature on bonding and bridging social capital in order to evidence the motivation of the paper; section 3 describes the data and the model used; section 4 analyses how bonding relates with bridging indicators; section 5 sets out the results on the relationship between the bonding/bridging dimension and individuals’ life satisfaction; and section 6 concludes.

# **2 Motivation and literature review**

The importance of social capital for economists is evident from the variety of economic phenomena that have received better explanation with the use of the concept of social capital, such as economic growth (Knack and Keefer 1997), and,
# **Appendix C:** Estimation of Treatment Effects on Children

This appendix provides an overview of the empirical issues involved in estimating treatment effects on child outcomes when the intervention under investigation targets prospective mothers and starts prior to their pregnancies. As the intervention may have altered the composition of children subsequently observed, we suggest a simple sequence of assumptions and steps in an attempt to move from the total reduced-form effect of the intervention to a more standard causal effect on the children actually born.

The natural experimental literature has recognized that maternal selection and differential mortality represent plausible causal pathways when analyzing child outcomes. Using data from the US, Buckles and Hungerman (2013) show that nearly half of the well-documented effects of season of birth on later-life outcomes can be explained by variation in the types of women giving birth across seasons. Aaronson, Lange and Mazumder (2014) show extensive margin selection also contributes to the quantity-quality tradeoff for children. This phenomenon is well documented in the developed world, and yet Currie and Vogl (2012) suggest that these extensive margin effects are likely to be more pronounced in the developing world where differential mortality, as well as differential fertility, is an operative channel. It has now become standard in the natural experiments literature to test for the presence of selection effects when studying child outcomes (see, for example, Almond 2006; Black et al. 2014; Adhvaryu et al. 2016). In many cases in this literature, the obvious selection effect suggests that the simple casual effects are lower bounds, but we present a context in which selection and treatment effects go in the same direction. It is therefore critical to attempt to control for the selection mechanism as a mediator, in order to isolate how much of the observed effect may have a simple causal interpretation.

To use counterfactual outcomes to represent impacts on subsequent children, we must define the universe as being the _potential children_ : all those who might exist under either the treated or control state.<sup>38</sup>
M., K.A. Ward, G.R. Goldberg, L.M. Jarjou, S.E. Moore, A.J. Fulford and A. Prentice. 2013. Critical windows for nutritional interventions against stunting. _American Journal of Clinical Nutrition_ , 97(5): 911–18.

- Saenz de Tejada, S. 2009. Assessing the impact of the financial crisis in high-migration communities in the western highlands of Guatemala: A qualitative study. Report submitted to the World Bank: Guatemala City, Guatemala.

- Smith, J.P. and D. Thomas. 2003. Remembrances of things past: test-retest reliability of retrospective migration histories. _Journal of Royal Statistical Society Series A_ , 166(1): 23– 49.

- Stark, O. and D.E. Bloom. 1985. The new economics of labor migration. _American Economic Review_ , 75(2): 173–8.

- Stillman, S. and D. Thomas. 2008. Nutritional status during an economic crisis: Evidence from Russia. _Economic Journal_ , 118(August): 1385–1417.

- Taylor, J. E. and J. Mora. 2006. Does migration reshape expenditures in rural households? Evdence from Mexico. World Bank Policy Research Working paper 3842. The World Bank: Washington DC.

- Thomas, D., J. Strauss and M-H. Henriques. 1991. How does mother’s education affect child height. _Journal of Human Resources_ , 26(2): 183–211.
0|10.9|10.0|8.5|8.1|
|F-frst stage IT*competition|14.9|17.2|23.2|22.1|20.2|20.1|
|Sargan|0.91|0.98|0.99|0.98|0.95|0.90|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

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