rafmacalaba/data-use-mentions-extended huggingface dataset:
able to form a small but influential special interest group, represented by Non-Governmental Organizations, or they may be able to form a coalition with non-poor sub-groups who see it as in their interests to not have the burden of cuts fall on the poor.

This paper first establishes that the incidence of cuts is unclear on theoretical grounds even when the poor are a powerless minority. So it is an empirical question. Good data are now available for addressing the issue in Argentina, which has undergone a number of sharp fiscal contractions over the last two decades. The paper studies two main sources of data. Firstly, section 3 uses aggregate times series data on public spending allocations to see how the composition of spending changes with aggregate contraction and expansion. Secondly, the paper turns to a new data set (constructed for the purpose of this paper) on one of the programs explicitly introduced by the Government of Argentina (with support from the World Bank) to deal with the effects of a macro crisis on the poor. Section 4 describes the program and how its performance in reaching the poor is to be measured, while section 5 tests how the program's performance in reaching poor areas was influenced by spending cuts. The paper's conclusions are then summarized in section 6.

# **2. Are budget cuts passed onto the "powerless poor"?**

In settings in which the majority of voters are not poor, one might well argue that the poor will be obliged to bear a disproportionate share of a budget cut on the grounds that they are the least powerful. However, there are some obvious problems with this reasoning. If the poor have little or no power, and power is all that matters to the allocation of public spending, then the same reasoning would suggest that the poor gained little from public spending before the cuts - in which case they can have little to lose from cuts. It might be argued instead that the non-poor care positively about spending on the poor (either through altruism,
Croke et al. (2016) We call the sample of 11 treatment effect estimates from 10 studies used in the TMSDG meta-analysis for the impact of MDA on child weight gain the “TMSDG sample.” See Table 1 for a list of these studies. Figure 1 verifies that our estimation procedure yields results similar to TMSDG when using this sample. In particular, this figure presents a forest plot with TMSDG estimates; inserting the effect sizes, standard errors, and sample sizes reported for each of these studies in TMSDG's text and figures into the relevant formulas provided in Borenstein, Hedges and Rothstein (2007) using the R statistical software package replicates the TMSDG results.

Using the data presented in TMSDG, the hypothesis of a common zero effect of deworming on child weight gain is rejected at the 10% level (p=0.089). Figure 1 shows the fixed effect estimate used to test this hypothesis.

The null hypothesis of a common zero effect is more strongly rejected when applying either of two study classification approaches used in previous Cochrane Reviews (prior to TMSDG).

1. The first versions of the Cochrane Review (Dickson et al. 2000; Dickson et al. 2007) did not create separate categories for test-and-treat (i.e. treatment only of children who have been diagnosed with STH) and MDA studies (i.e. treatment of the whole population), but rather considered all the data together. At a minimum, reporting results with the full sample before turning to the subgroup analysis seems reasonable, since if deworming has a positive effect on infected individuals, and if there is no effect on uninfected individuals, then deworming must have a positive effect on weight in a population that includes infected individuals. In the case of the TMSDG dataset, using all of the multiple dose at longest follow-up studies

22
17 Comparisons of city data over time indicate that average population densities are falling in virtually all high income cities as both employment and residences decentralize. Overall urban population growth occurs through expansion of the urban perimeter by annexation of surrounding municipalities with lower densities than the core city.

When city effects are controlled for in first difference estimates, many of the relations differ from the cross-city panel estimates (unlike at the national level, where the panel and first difference results are broadly similar). In particular, the first difference estimates suggest that urban road length barely increases with per capita income over time; the income elasticity of urban road length is around 0.1 (table 4). Using the techniques pioneered for vehicle ownership, it is possible to estimate the saturation level of urban road provision, which is 23 kilometers of road length per square kilometer, a level very close to the 24.9 kilometers observed in Tokyo in 1960.

These results indicate that urban road length is increasing much more slowly over time than urban vehicle ownership. A 1 percent increase in income produces a 1 percent increase in the number of urban vehicles and a 0.1 percent increase in urban road length. Relatively little new road length is being constructed in urban areas, presumably because the cost of new rights of way is high in both economic and political terms. Most increases in urban road length come from annexation of areas contiguous to the city which are the sites of new urban growth and have lower land costs, more open space, and less congested roads. Hence, most new road capacity in urban areas comes from spreading development over space and not by increasing the density of roads in existing built up areas.

# **6. Producing motor vehicle transport services**

Thus far the analysis has focused on vehicles and roads, but what consumers and firms actually seek or demand are transport services-that is, the movement of goods and passengers from one point to another. How do we relate our information about vehicles and roads to these transport
# **Figure 36. Services Trade and Endowments, 2013**

<!-- Start of picture text --> A. B.<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>IND IND<br>ZAFTURBRARUSIDNCHN IDN ZAFCHNTURBRARUS<br>0 0<br>-2 -1 0 1 2 0 20 40 60 80 100<br>Human Capital Index Internet users (percent of population)<br>Services Trade/GDP Services Trade/GDP<br><!-- End of picture text --> _Source_ : World Development Indicators; World Economic Forum.

_Notes_ : GDP = Gross Domestic Product. RUS = Russia, BRA = Brazil, IND = India, IDN = Indonesia, TUR = Turkey, CHN = China. Panel A shows the simple relationship between a country’s human capital index and the extent of services exports in GDP. In addition to the number of people with an educational degree, the x‐axis also takes into account qualitative aspects of education at all levels and information on both the present and the future workforce. This proxy is taken from the World Economic Forum’s Human Capital Report.

60
Policy Research Working Paper 9126

# **Abstract**

Post-election violence is a common form of conflict, but its underlying mechanisms are not well understood. Using data from the 2007 Philippine mayoral elections, this paper provides evidence that post-election violence is particularly intense after narrow victories by incumbents. Using a density test, the study shows that incumbents were substantially more likely to win narrow victories than their challengers, a pattern consistent with electoral manipulation. There is no evidence that the increase in post-election violence is related to the incumbent’s political platform or their performance in past elections. These results provide support for the notion that post-election violence is triggered by election fraud or by the failure of democratic ways of removing unpopular incumbents from office.

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bencrost@illinois.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
1989, 1991) that the fraction of the consumers are liquidity constrained varies with the degree of financial liberalization.

The structure of the paper is as follows. Section II describes the main channels through which financial liberalization may affect savings and briefly reviews the relevant empirical literature. Section III describes the financial reform process as it occurred in each of the eight countries being studied here. This section also explains and graphs our index of financial liberalization and provides summary statistics and bivariate correlations with financial depth and savings. Sections IV and V present the econometric results based on the saving function and on the augmented Euler equation for consumption, respectively. Section VI concludes.

# II. Financial Liberalization and Savines: Theoretical Back2round and Review of **the** Empirical Evidence

Although financial liberalization can enhance the efficiency with which saved resources are channeled into productive use, the effect on the quantity of savings is theoretically ambiguous.2 The mechanisms at work here include both long-term and short-term effects. Once it has settled down, a competitive liberalized financial system will typically be characterized by improved savings opportunities, including higher deposit interest rates, a wider range of savings media with improved risk-return characteristics, and in many cases more banks and bank branches, as well as other financial intermediaries. Bank lending rates will typically be higher for those borrowers who had privileged access in the restricted regime, but access to borrowing should be wider. These long-term effects of liberalization on aggregate private saving will be felt through changes in rates of return and in the degree of credit restrictions. Moreover, if financial liberalization also has a

- 2 It should be stressed at the outse **t** hat our evidence is based chiefly on national accounts definitions of saving. These need to be distinguished from intennediated saving or from capital flows. Dornbusch and Reynoso (1989) observe that capital flight through mis-invoicing of trade serves to conceal saving that is being hidden abroad: an apparent increase in saving may really be a reduction in capital flight. Furthermore, they note that, as
7 length and weight of one of those children (randomly selected). Salter scale (Model 235 6S) and wall-mounted measuring rods (portable baby/child length/height measuring system) were used.

The WASH governance index combined questions about the presence of a water committee, frequency of committee meetings, WASH expenses (excluding maintenance), presence of a maintenance plan, whether the committee tracks health conditions in the community, and whether it tracks hygiene and sanitation.

Secondary outcomes were access to improved water and sanitation facilities, water quality at water points and in homes, hygiene knowledge and behaviors, observed handwashing, perceptions of WASH governance, children’s school absenteeism, child weight-for-age Z score, and child weight-for-height Z score.

Structured observation of handwashing was done in four households per village. We first attempted to observe the four households that were interviewed at the five-month follow-up; if any were unavailable or unwilling, we randomly selected from the six new households to replace them. A research assistant spent two hours in each of these households, recording if handwashing occurred at critical junctures such as before preparing food (see S8 Table for full list of junctures). This took place before the interview, to minimize Hawthorne effects.

Access to improved water and sanitation was self-reported, i.e. respondents reported whether their main water source is improved or not according to the Joint Monitoring Program standard definitions (e.g. boreholes are considered improved, while unprotected springs or surface water sources are not). Cost paid by households for water and time spent collecting water were also self-reported.

To measure water quality, we tested samples collected (i) at each of the water points used by members of each village, and (ii) at household water storage containers in six randomly selected households per village, on average. Testing was done concurrently with the household interviews. We used the Aquagenx Compartment Bag Test E. Coli +Total Coliform Most Probable Number (MPN) Kit. This measured the MPN of fecal indicator bacteria
The result of this, as seen in Table 3, is that there is essentially no difference in the responses of firms at the low end of the distribution no matter what the country DB value is, but substantial differences in the higher end. So for instance, the range of the DB data on imports is 80 days (84 versus 4) while the difference in the 10<sup>th</sup> percentile is only 2.6 days and the 25<sup>th</sup> percentile is 4.7 days. This implies the ―favored‖ firms in the ―worst‖ rated countries by the DB indicator do much better than the ―disfavored‖ firms in the ―best‖ rated DB countries. So the 10<sup>th</sup> percentile firm in Uzbekistan, the country with the highest DB ranking of 84 days, is predicted to get imports through customs in 3.7 days versus 4.4 days for the 75<sup>th</sup> percentile firm in Estonia, with a DB ranking of only 4 days.

But on the other side, nearly all of the action happens at the upper end, the ―disfavored‖ firms. As detailed above, the DB range of 524 days from the ―best‖ to ―worst‖ in construction permits produces a predicted 25<sup>th</sup> percentile difference of less than 2 days. However, the 90<sup>th</sup> percentile firm has a difference of 140 days between ―best‖ and worst‖—and again, essentially all of this increase happens between the lowest DB value (77days) and the 25<sup>th</sup> percentile (155 days).

20
shortly after 2001 and peaked at the end of the decade. The advent of the Arab Spring in 2011 put significant downward pressure on both males and females, but between 2015 and 2020 the gap stabilized to between 10 and 11 percentage points.

**Figure 1 Unemployment rates by gender: Tunisia, 1990–2019**

<!-- Start of picture text --> 12<br>30<br>10<br>25<br>8<br>20<br>6<br>15<br>10 4<br>5 2<br>0 0<br>1990 2000 2010 2020<br>Men Women<br>Gender differnce<br>Unemployment rate (%)<br>Unemployment rate difference (%)<br><!-- End of picture text --> _Sources_ : Modeled estimates, International Labour Organization (ILO); World Bank, World Development Indicators (WDI), 2019, https://databank.worldbank.org/source/world-development-indicators.

Aggregated averages often mask important and substantial variations. Features such as age, education, and location can provide a more nuanced picture of the key characteristics of men and women that are likely to influence their decision to enter the labor market and subsequently find employment. For example, women in urban areas are more likely than women in rural areas to participate in the labor market. In addition, women in the 25–33 age group have the highest labor force participation rate (34 percent) of all age groups. However, this rate drops beyond this age group, possibly following marriage. By contrast, men’s labor force participation rates remain high across age groups and only drop for the 55–65 age group (figure 2).

6
||0.0242***<br>(3.55)|0.00724<br>(1.00)|
|WBLaw Index|||-0.00560<br>(-1.85)|||0.00658***<br>(17.51)|
|Observations|8184|8184|8184|17012|17012|17012|
|Mean of outcome variable|0.214|0.214|0.214|0.259|0.259|0.259|
|WB p-value|0.002|0.002|0.738|0.57|0.742|0.002|
|N cohorts|44|44|44|54|54|54|
|Time trends|No|No|Yes|No|No|Yes|
|Controls<br>Source: Authors Calculations -|Yes<br>Demographic|Yes<br>and Health Sur|Yes<br>vey (DHS): Moroc|Yes<br>co (1992, 2003|Yes<br>) and HCP Ho|Yes<br>usehold survey|

Source: Authors Calculations - Demographic and Health Survey (DHS): Morocco (1992, 2003) and HCP Household survey data for 2007 and 2014). Notes: The dependent variable is a binary indicator for whether the interviewed woman is currently working. Columns 1 to 3 report results for a restricted period representing 15 years around the reform year between 1992 and 2007. Columns 4 to 6 report results for the full covered period between 1992 and 2014. The estimation samples are restricted to married women whose youngest child's age is around 5 years window around the custody cutoff and whom did not have children after the reform. Wild bootstrap (WB) p-value using one thousand replications. *p<0.10, **p<0.05, ***p<0
off workers. In turn, this encourages older firms to retain their labor force despite being less productive. Furthermore, the bottom panels of <mark>Figure 8</mark> demonstrate that more productive firms face

> 23 Ecuadorian formal firms are legally obliged to pay three compulsory allowances for each worker. The “thirteenth salary” allowance is equal to a worker’s monthly salary and paid to all workers during Christmas time (December). The “fourteenth salary” allowance is equal to a minimum wage payment that is paid to all workers before the school year begins (August/September). Lastly, the “unemployment reserve funds” allowance is paid monthly to every worker and equals 8.3 percent of each worker’s salary.

> 24 According to Article 97 of Ecuador’s Employment Regulation, firms are obliged to share 15 percent of their profits with their labor force.

> 25 According to Article 188 of Ecuador’s Employment Regulation, a worker that is fired and has been employed less than three years will receive firing compensation equivalent to three months’ salary. A worker that is fired and has been employed more than three years will receive firing compensation equivalent to the number of years worked in the company times their monthly salary (up until 25 monthly salaries).

> 26 Bolivia and the República Bolivariana de Venezuela are excluded as data is not reported withing the World Bank’s _Employing Workers_ Database.

> 27 Chile and the República Bolivariana de Venezuela are excluded as data on minimum wages is not available in the ILO’s Global Wage Report 2020 – 2021.

> 28 For this estimation, firms’ total labor costs consist of wages plus social security contributions, severance pay costs, mandatory compulsory allowances (i.e., “thirteenth” salary, “fourteenth” salaries, unemployment reserve fund), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, childcare).

> 29 For this estimation, firms
altitude. Finally, we include an interaction term between temperature and temperature change in Column (4). In overall, the results are qualitatively similar to our main finding.

Second, we replicate the results in Figure 2 but using alternative thresholds to define hot and cold days. In Panel A of Figure A1 (Appendix A), we present the results of the temperature bin approach using the 2-degree bin, while Panels B and C show the results using the 4-degree bin and 5-degree bin, respectively. We find that when our definition of hot and cold days is less (or more) demanding, the implied effects on income inequality remain consistent.

Third, we present the results using alternative measures of poverty and income inequality at the subnational level. In Table A3 (Appendix A), we employ the multidimensional poverty indicators, which complement the traditional measure by capturing the acute deprivations in different aspects including monetary, education, electricity, sanitation, and drinking water. Similarly, we plot in Figure A2 (Appendix A) the effects of temperature using alternative measures of income including (i) the 90/10 ratio, (ii) the 80/20 ratio, and (iii) the Palma ratio. This helps address potential concern of using Gini and Theil indices as they are more sensitive to changes in the middle-income group. In overall, the results reaffirm the negative effects of higher temperature on poverty and income inequality.

Fourth, we provide further tests in Table A4 (Appendix A) to ensure that our results are not sensitive to the choice of temperature measures. We do so by using (i) log of temperature (Column 1); (ii) temperature measured in degrees Fahrenheit (Column 2); (iii) the temperature data at 0.5<sup>◦</sup> resolution from the Climate Research Unit of the University of East Anglia (CRU) (Column 3); (iv) the number of days that temperature is above 28<sup>o<
from the CBD (Panels C and D in Figure 1). Most informal houses are located within 10 km from the CBD. Given such spatial distribution of residences, 35 percent of household heads walk to work, while 43 percent of household heads use minibuses for commuting.<sup>7</sup> Only less than 10 percent of household heads commute using their own car in Nairobi. The share of household heads walking to work is larger in informal settlements (41 percent) than in formal 6 Distance from the CBD is measured based on Euclidian distance without considering road networks. We also carried out all the analyses using network distance from the CBD and obtained similar results (not reported).

7 According to the 2015/16 KIHBS, 39 percent of Nairobi residents walk to work, and 38 percent of workers use minibuses for commuting. Private cars account for only 5 percent. Walking is the main commuting mode for the poor (75 percent), followed by minibus (15 percent).

6
, more papers have turned to the importance of labor demand shocks. Using real-time surveys, a number of researchers have documented the impact on layoffs by private firms of sales loss, closures, or liquidity issues (Bachas, et al., 2020; Bartik et al., 2020; Adams-Prassl et al., 2020; Fairlie, 2020). Apedo-Amah et al. (2020) use a dataset measuring the impact of the COVID-19 pandemic on the private sector, which covers more than 100,000 businesses across 51 countries. They find that firms that have experienced a larger reduction in sales experienced a larger reduction in employment. Using real-time data on vacancy postings on online job portals in Sweden, Hensvik et al. (2020) find that postings fell by 40 percent even during the earlier period of the pandemic. It has also been clear that, due to either social distancing measures or consumer risk aversion, contact-intensive sectors, such as travel, restaurant, and other services, have been greatly affected (Mongey et al., 2020). The large job losses in other sectors, such as manufacturing, have not been as obvious to all _a priori_ , whereas in other sectors, such as construction and agriculture, they may have been over-predicted due to the focus on the ability to work from home.

As the literature details, firms have been affected through multiple channels. First, the crisis affects levels and patterns of consumer spending. Domestic consumers reduce their mobility to reduce the risk of infection and avoid products and services that involve interactions with others (Andersen et al., 2020; Balleer et al., 2020). For example, Abay, Tafere and Woldemichael (2020) estimate how much the crisis affected demand for selected services across 182 countries using real-time Google search data. They find that the crisis led to a 63 percent reduction in demand for hotels and a comparable rate of increase in demand for ICT services. Firms have responded to reduced cash flow and high uncertainty by adjusting their investment (