rafmacalaba/data-use-mentions-extended huggingface dataset:
# **<<B>>** Descriptive Statistics

Analysis of the 2010 HFA and other data sets involves simple descriptive statistics (percentages for categorical outcomes). Where data permit, a cross-sectional comparison is made between voucher and nonvoucher sites. Significance tests of proportions are performed to determine whether individuals from voucher and nonvoucher sites are significantly different with respect to the outcomes of interest.

# **<<A>>** Results

The following section describes aspects of program performance and study outcomes including the uptake of different voucher services among the targeted beneficiaries, reproductive health knowledge among the study respondents, voucher-subsidized health care use among the study respondents; costs of service and program management, survey respondents’ out-of-pocket costs; poverty-targeting effectiveness, and differences in quality of care at facilities in the control and intervention study arms.

# **<<B>>** Targeting Beneficiaries

Voucher sales data show that a total of 102,562 HealthyBaby vouchers were sold during the project period (2008–12). Of these, 65,590 (64 percent) were used for SD; 86,894 (85 percent) for one ANC visit; 18,131 (18 percent) for PNC; 8,887 (9 percent) for pregnancy complications; and 4,013 (4 percent) for emergency transport (figure 1). The number of vouchers used for safe deliveries represented 130 percent of the targeted 50,456 deliveries. The annual number of deliveries subsidized by the program increased from 2,837 in 2009 to 28,193 in 2010 and to 35,442 in 2011, reflecting a growing demand for the vouchers. Of the total 65,590 deliveries, 9,044 were by Caesarean section (13.8 percent), which falls within the World Health Organization–recommended rate of between 5 and 15 percent. The impact of the project was exemplified in Kasese district, where seven SPs out of a total of 36 health facilities contributed more than 50 percent of all estimated deliveries
progress towards achieving urban objectives.

Key indicators are both important for policy and relatively easy to collect. They are either numbers, percentages and ratios; Qualitative data or checklists, which give an assessment of areas which cannot easily be measured quantitatively. They are audit questions generally accompanied of checkboxes for yes or no answers.

_UITP, International Association of Public Transport (IAPT) Millennium Cities Database --_ _<u>Mobility In Cities Database</u>_ The International Association of Public Transport has compiled a database of 100 of the world’s cities, known as the _Millennium Cities Database for Sustainable Transport_ . The collected data looks at population, the economy and urban structure, the number of road vehicles, taxis, the road network, parking, public transport networks (offer, usage and cost), individual mobility and choice of transport mode, transport system efficiency and environmental impact (duration and cost of transport, energy consumption, accidents, pollution, etc.). In total, over 200 indicators have been collected for each of the 100 cities for the year 1995.

Indicators per city, statistical analyses and recommendations have been compiled onto CD-ROM available form UITP.

The UITP will publish in October 2005 the results of the Mobility in Cities Database project, __________________________________________________________________________________________________________ 32
if anything, the richest economies tended to have smaller, not greater growth collapses.<sup>7</sup> Third, Table 2 shows regressions of growth collapses on income dummies. Again, the dummies for advanced and emerging economies are not statistically different from each other. If anything, emerging economies suffered greater collapses. On the contrary, low-income economies did suffer smaller collapses.<sup>8</sup> These findings stand in contrast with the performance of emerging and advanced economies around previous crisis episodes. The upper-right panel of Table 2 shows regressions of growth collapses on income and regional dummies during the Russian and Asian crises of 1997–1998, which we use as benchmark. During those crises, emerging and low-income economies across all regions suffered large growth collapses, while advanced economies suffered only modest growth decelerations. In addition, collapses do not seem to be associated to the level or the square of GDP per capita. The results in Table 2 for both the 1997-1998 and 2008-2009 crises are robust to weighting observations by population and by pre-crisis GDP measured in U.S. dollars. Moreover, the results are robust to excluding some or all European economies, as they may disproportionately affect the coefficients for advanced economies.

The timing of the 2008–2009 crisis renders annual data on GDP inadequate for a thorough analysis of the economic cycle that includes, for instance, the duration of downturns: peaks and troughs in economic activity do not necessarily coincide with calendar years. Hence, we turn to data on IP at a monthly frequency. These data allow us to construct different measures of the severity and duration of the recession that cannot be built using data at the annual frequency. Figure 3 displays the evolution of IP indexes across income groups relative to

> 7 This result also holds if we drop the economies with growth collapses greater than 19 percentage points.

> 8 As a consistency check, we report in Table 1 several measures of GDP growth, collapse, and recovery. For example, we arrive at
002)|
|Constant|0.191***|0.151***|0.167***|
||(0.005)|(0.007)|(0.009)|
|Adjusted_R_-squared|0.146|0.118|0.114|
|Number of observations|15,322|6,014|5,237|
|Number of countries|95|95|95|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Model: _EG_ = _b_ 0 + _b_ 1All 15 Obstacles + _b_ 2Firm Characteristics + Region + Country Fixed Effects + _e_ . * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

26
While, proportionally, this number of imbalances is statistically expected through the randomization procedure, these are variables that may be correlated with our main outcomes of interest. In a robustness check, we include these variables as covariates to the first specification and find that our estimates do not change in sign or significance (see results section).

# _Program Exposure_

We first provide reassurance that our careful selection of radio channels was successful in terms of the similarity of their broadcasting by examining characteristics of their regular listeners using our baseline data. If programs are similar, we expect that they also appeal to broadly the same audience. Table 2 presents descriptive statistics by radio channel listenership. With the exception of age, we find no statistically significant differences between participants who reported to listen to SUD FM (control channel) and RTS (treatment channel) most of the days, respectively. In fact, the two audiences appear to be very similar with respect to gender, household size, income, main outcome variables, and knowledge about the new technology.<sup>31</sup> Program exposure is assessed in two ways as presented in Table 3: measuring listenership through verified lottery call-in records, and through self-response of radio listening practices. Overall, we find evidence that our encouragement approach worked. First, approximately 33 percent of our entire sample called the radio station during a call-in contest event. By matching telephone numbers, we track all call-ins made for the lotteries held during the radio sessions when solar lighting promotional clips were being run. The encouragement was also successful in incentivizing control and treatment households to tune to their respective radio channels. Both groups record statistically similar call-in rates and participation. Only 1 in 500 people called to the incorrect radio station (this was also balanced across experimental groups).<sup>32</sup> Second, we observe an increase of self-reported listening rates for both groups. We find that control households are 30 percentage points more likely to report listening to SUD FM as their main radio station
provinces of China from _A Survey of Chinese SOEs: 1980-1989._ (See the data appendix for more details.) To take into account the potential differences in technology for firms in different industries, we decomposed the firmns into 12 industry-size categories. The four industries are chemical, light, machine, and material industries.<sup>9</sup> For each of these four industries three dilfferent technologies are allowed depending on size classification (large, medium, and small)."<sup>0</sup> The firms in the sample can be classified into two groups, those that eventually adopted PCs (the participation group), and those that did not (the non-participation group). Let _di_ represents these two groups: _di=I_ for the participation group, and _di=0,_ the other, where the subscript i denotes an individual firm. We assume the following Cobb-Douglas production function": non - participants: Y,,i = _8I + /8L In LJ,, + f3j ln k,' +_ **_/JRR;,_** + _r,_ + _u,,_

> participants: _Y-1 =_ A _+f_ lnL, +/ _lnk,_ **_+IJRRIf_** _+D,a, +D,,7,a 2 +rt +Ui' (1)_

where i indexes firn,j indexes industry-size category, and t indicates year. The variables are defined as follows:

- Yit: log(value added per worker) (see the data appendix for the construction of variables and the associated deflators.)

lnLi,: the number of employees, excluding those absent for more than half a year.

- 9We follow Li (1995), which uses the same data set. The data contains firms in 36 two-digit industries, but many industries contain too few observations.

> 0 Large, medium, andl small firms had, on average, 4083, 1486
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with data from Brazil, has suggested that greater fiscal dependency can act like a “political resource curse”, reducing the quality of local politicians (Brollo et al, 2013). In municipalities where national fiscal transfers constitute the vast bulk of local revenues political challengers might be more willing to invest their resources into vote buying, in anticipation of accessing public resources coming from above, and hence not relying on local taxation. The data do not show any evidence of this, however.

In sum, a highly detailed scrutiny of the sources of variation in vote buying practices, applying the available theory to very rich survey data, finds a few variables to be robustly significant and with considerable explanatory power—congressional district fixed effects, measures of clan domination in local politics, and indicators of mayoral economic powers. In column (2) of Table 1, these variables together explain 33 percent of the variation in reported vote buying across municipalities. Adding more variables does not change the R-square substantially, as can be seen from subsequent columns. In contrast, social or economic variables that one might expect to be systematically correlated with both vote buying and health service delivery, possibly driving a spurious correlation between the two, are not robust correlates of the variation in vote buying.

The informative value of these correlations for our main purpose (of examining the equilibrium policy consequences of vote buying politics) is the suggestion that the sources of variation in vote buying in this sample (almost all municipalities within one province) are likely to be idiosyncratic and not systematically correlated with factors that could have independent correlation with health services (outside of the channel of clientelism). Vote buying might be 14
and Zhu 2009).<sup>20</sup> Increasing trade credit by one σ (0.195) would increase investment intensity by 0.015. The effects of formal finance through the banking system are therefore five times as large as those of informal trade financing arrangements. The availability of collateralizable assets (UCA) is also positively associated with investment. Increasing UCA by one σ (1.64) would increase investment intensity by 0.05.

Most importantly for our analysis, normalized cash flow is significantly and robustly associated with investment in our sample. Increasing it by one σ (1.43) would increase investment intensity by 0.15. For other control variables, notable findings are that non-state firms tend to have higher investment intensities, and that younger firms tend to invest more. Those findings are consistent with the notion that young non-state firms have higher growth opportunities, as was suggested by the summary statistics.

Some of the recent papers on financial constraints in China have employed estimation methods that deal with the potential endogeneity of the cash flow variable. We follow Poncet, Steingress, and Vandenbussche (2010) using lagged values of cash flows as instruments for current cash flows. Because we have information on cash flows for only the year of the survey and two years prior, and because the cash flow variable is deflated by lagged capital stock by construction, we are able to use only a single lag of the cash flow variable in the IV regression in column (6). In the first stage, the instrument is highly statistically significant, with a t-statistic of 14.5, and thus satisfies the relevancy criterion for a good instrument. It is questionable whether the lagged cash flow satisfies the validity criterion, that is, is not correlated with the error

> 20 The importance of trade credit for explaining firm performance in general and African firm performance in particular is also observed in cross country firm samples ( Harrison, Lin and Xu 2011).

22
|||||
|Blackout Today Only|-0.01|-0.20|0.07**|0.72*|
||(0.02)|(0.17)|(0.03)|(0.36)|
|Lagged Blackout Only|0.04***|0.35***|0.09***|0.98**|
||(0.01)|(0.11)|(0.03)|(0.37)|
|Blackout Both Today and Lagged|-0.03|-0.39*|0.08**|0.73*|
||(0.03)|(0.21)|(0.04)|(0.40)|
|Outcome Variable Average|0.84|7.76|1.67|15.52|
|Observations|4,711|4,711|4,711|4,711|

Source: Author calculations from The Hohoe Garment Makers Project Data 19
_Measures of food security_ ―Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life‖ (Food and Agricultural Organization, 2008, p. 1). It includes four main factors: availability, access, utilization, and stability. Availability refers to the physical existence of food, which relates to production, stocks and trade. Access refers to a household‘s ability to obtain food, which depends on income, prices, and market access. Utilization refers to an individual‘s ability to process nutrients and energy from food; many factors contribute to food utilization, including dietary diversity and nutrient absorption, intra-household allocation of food, and hygienic preparation. The final factor refers to the stability of the other three factors over time (Food and Agricultural Organization, 2008).

We construct three measures of household food security to be used as dependent variables in the regression analysis: the value of per capita monthly food consumption; per capita daily caloric intake; and household dietary diversity. The first two relate to the access to food and the last one relates to the utilization of food. The value of food consumption is a core component of poverty indictors. Caloric intake is a widely used measure of health and undernutrition; however recent literature suggests that it is not a sufficient indicator of nutritional status, therefore we also include a measure of dietary diversity (Deaton & Dreze, 2009). Recent research highlights the relationship between child and adult nutritional status and household dietary diversity (Ruel, 2003).

The value of monthly food consumption (in Afghani) is constructed by mapping district price data to food quantity data.<sup>3</sup> Since not all food items were available in all district markets at all times of the year, we imputed the missing elements to obtain a complete price matrix, which provides prices for those items that households may have produced at home
Policy Research Working Paper 8865

# **Abstract**

Sectoral segregation is often used to explain a large part of a well-documented gender earnings gap in business profits. Women tend to sort into different sectors than men, and the sectors dominated by women tend to be less profitable. This paper investigates the horizontal dimension of sectoral segregation by studying global data on female and male enterprises operating in sectors that are typically dominated by the same and opposite sex. The analysis uses the novel _Future of Business dataset_ , which spans 97 countries and was administered to enterprise owners, managers, and employees who use Facebook. The analysis finds that some of the earnings gap can indeed be explained by sector choice: female-owned businesses in male-dominated sectors make significantly higher profits than those in traditionally female sectors. The evidence points to a hierarchy of earnings, with male-owned businesses in male-dominated sectors earning the most, women in male-dominated sectors and men in female-concentrated sectors in the middle tier, and women in female-concentrated sectors at the bottom. Correlational analysis suggests that women who own businesses in male-dominated sectors are younger, married, and more likely to have inherited the business than women in female-concentrated sectors. They have similar education to women in female-concentrated sectors and present higher self-efficacy but lower entrepreneurial identity and commitment to the sector. Male support networks appear to be key for female-owned firms, with co-ownership with husbands and male role models factoring into the decision to cross over.

This paper is a product of the Gender Innovation Lab, Africa Region and Gender Global Theme. 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 mgoldsten@worldbank.org, pgonzalezmartine@worldbank.org, and spapineni@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas
Table 9 synthesizes the key components of what has been effective in several countries.

**Table 9: Financial Inclusion Approach and Examples**

|**APPROACH**|**1. Data**<br>**underpins**<br>**Reforms and**<br>**Innovation**|**2. Public and**<br>**private sectors**<br>**agree financial**<br>**inclusiongoals**|**3. Public sector removes**<br>**regulatory barriers and**<br>**monitors progress on goals**|**4. Private sector**<br>**develops new financial**<br>**services and delivery**<br>**mechanisms**|
|---|---|---|---|---|
|South Africa:|FinScope Data<br>Survey|Financial Sector<br>Charter|Financial Advisory and<br>Intermediary Services Act,<br>Dedicated Banks Bill|Basic bank accounts and<br>mobile phone banking|
|UK:|Family<br>Resources<br>Survey|Financial<br>Inclusion Task<br>Force|Progress monitored by<br>Financial Inclusion<br>Indicators|Basic Bank Accounts,<br>Financial services<br>through Post Office|
|Canada:||Canadian Banking<br>Code|Financial Consumer Agency<br>of Canada|Low cost bank accounts<br>Basic bank accounts,|
|India:||Banking Codes<br>and Standards<br>Board of India|Reserve Bank of India‟s<br>guidance on Financial<br>Inclusion|Innovative use of<br>technology to reach<br>customers|

Source: Pearce & Bajaj, 2006.

_Data
essential infrastructure and services or quickly developing the competence to do so. I measure three dimensions of government’s performance in fulfilling its fiscal obligations to citizens.

First, I include a measure of respondents’ food security, specifically, whether a respondent or a household member ever went without sufficient food in the year preceding the survey. Sacks and Levi (2010) find that food security is strongly related to government performance in providing and maintaining infrastructure, reliable bureaucracies, and law and order, controlling for income, rainfall levels, the competitiveness of elections, and the presence of civil liberties and political rights.

Second, I include two measures of citizens’ perceptions of government service delivery. I include an indicator of citizens’ perceptions of how well the government is maintaining roads and bridges. I also include an indicator of citizens’ perceptions of how well the government is providing water and sanitation services. Third, I include two objective indicators of government service delivery. Survey enumerators identified whether there was a sewage system and school in each of the primary sampling units.<sup>7</sup>

# **6.2.4 Perceptions of Administrative Competence and Honesty**

I include two measures of citizens’ perceptions of the honesty and transparency of the bureaucracy. First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I include a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows.<sup>8</sup> Third, both the size of a country and the size of the government may affect a government’s ability to detect and punish evaders. I include the

> 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of