rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9390

# **Abstract**

The introduction of prenatal sex-detection technologies in India has led to a phenomenal increase in abortion of female fetuses. This paper examines the impacts of this on girl relative to boy mortality rates after birth, using data from 1973–2005. The analysis finds a narrowing of the gender gap in under-5 mortality rates, in line with surviving girls being more wanted. The estimates show that for every three aborted girls, one additional girl survives to age five. Investigation of the mechanisms finds a narrowing of gender gaps in parental investments in children, moderation of son-biased fertility stopping, and shrinking of the gap between actual and desired fertility. Heterogeneity in fertility responses suggests a shift in the distribution of girls toward lower socioeconomic status families. The findings have implications not only for counts of missing girls, but also for the later life outcomes of girls.

This paper is a product of the Development Research Group, 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 sanukriti@worldbank.org.

_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
We followed the same procedure as in Section 5.2.1 and plot the results in Figure 12. As with above, we fit a linear regression model with the given empowerment variable as the outcome and treatment status and the covariates as additive predictors and included the full matching weights in the estimation. We include the same set of control variables as Figure 4 and we again use clustered standard errors with matching stratum membership as the clustering variable.

<!-- Start of picture text --> Trust G<br>ControlOverLife G<br>Contribute Events in municipality G<br>Contribute Events in Mindanao G<br>Contribute Events in Barangay G<br>Attend Meetings G<br>−0.50 −0.25 0.00 0.25 0.50<br>Estimate<br>Displacement G Urban<br>Variable Philippines<br><!-- End of picture text --> Figure 5: Matching Within the Displaced Population in the Philippines In Figure 12, we find that among IDPs, urban respondents have higher levels of trust (ATT=12.7%) and improved perceptions of self-agency (ATT = 11.1%). In contrast, we find that urban IDPs are less likely to perceive themselves as able to contribute to events in Mindanao (ATT=8.7%) or in the Barangay (ATT=12.9%). We do not find significant changes in attending meetings or the ability to contribute to events in the municipality.

# **5.3 Iraq**

Finally, we analyze survey responses collected in Mosul, Iraq, in the wake of the defeat of ISIS. As with the other surveys, our survey sampled IDPs and non-displaced persons. We use differences 21
group of the World Bank’s Poverty and Equity Global Practice.<sup>7</sup> The GMD is an ongoing initiative and new surveys are added each year. As of June 2016, it contained 443 household surveys from 134 countries. The surveys are as old as 1993, but more than 90 percent of the surveys are from 2005 or later, and surveys from 2009 to 2013 account for 56 percent of the collection of surveys.

The GMD is the richest collection of nationally representative, globally harmonized household survey data on poverty.<sup>8</sup> The figures on poverty are based on a measure of household welfare, which is either household per capita income or household per capita consumption, depending on which concept is used to measure national poverty in a particular country. Of the 89 countries in our sample, income is used in 30 of them, most of which are located in Europe, Central Asia, Latin America, and the Caribbean. The GMD is particularly suitable for profiling the global poor because its welfare aggregates are identical to those used to compute the global poverty estimates published by the World Bank, except for the notable exception of China.<sup>9</sup> China is a special case because the World Bank does not have access to the individual level records of the Chinese Household Budget Survey (HBS), which is the source of official Chinese poverty statistics. The World Bank’s international poverty estimates for China are instead based on an approximate distribution derived from grouped data, which cannot be used to profile the characteristics of the poor. This study therefore utilizes household level data from the 2013 Chinese Household Income Project Survey (CHIPS), made available to the public by Beijing Normal University. The CHIPS is drawn from the same sample frame as the HBS, and an analysis of an earlier round of the survey, collected in 2007, yielded similar poverty rates as the official HBS-based estimates (Zhang et al, 2014). The poverty rate
2013; Hansen and Rand, 2014a; Hansen and Rand, 2014b). This study, in line with Beck et al. (2018), moves beyond the traditional focus on credit access, showing the importance of improving more broadly access to financial services, including payment services, as tool to stimulate business performance.

To summarize, the study contributes to the literature in the following ways. First, it uses nationally representative firm-level data for 16 economies across Sub-Saharan Africa, making it one of the most exhaustive studies in the region providing new information on mobile money penetration at the firm level. Second, it affirms the finding that mobile money increases investments by firms that was initially found for four economies in the region mostly in East Africa and finds that the relationship between mobile money use and investment by firms in Sub-Saharan Africa is largely driven by female-owned businesses. Finally, it uncovers suggestive evidence that supplier and customer relationships and the demand for credit may be the mechanisms through which mobile money leads female-owned firms to invest but not male-owned firms.

The rest of the study is structured as follows. Section 2 provides the conceptual framework; section 3 provides data details, summary statistics, and mobile money adoption patterns in the region; section 4 details the empirical strategy; section 5 provides the results; section 6 explores several mechanisms; section 7 provides robustness checks and section 8 concludes.

# **2. Related Literature and Conceptual Framework**

The relationship between transaction costs, access to credit and investment has been well established. In the neoclassical model, the accelerator model, and the Q theory of investment, the optimal level of investment is achieved with zero transaction costs and no budget restrictions; as per the Modigliani-Miller theorem (Modigliani and Miller, 1958), perfect capital markets carry the implication that the firm’s financial structure has no role in investment decisions. Subsequent literature has challenged the assumption of perfect capital markets with asymmetric information leading to credit rationing (Stiglitz and Weiss, 1981) and has thus made the availability
allows for consistent price comparison across countries. These data can then facilitate the extension of the scope of the analysis to affordability assessments of property price data and the segmentation of the housing market – particularly focusing on EMDEs.

# 3. Data Collection and Processing: House Price Data in Emerging Economies

In this section, we outline how we collect house price data through a web scraping approach for five markets: Albania, Costa Rica, Morocco, Pakistan, and South Africa. We demonstrate how a big data approach, hitherto employed mainly in developed economies with good data quality, can also be extended to EMDEs to collect price data efficiently. We collected 200,000 unique property transactions for these five countries in an otherwise data-scare environment. The web scraped data reflect the entire housing market and complement the available indexed data that (mostly) report average property prices only.

We selected these five economies to cover different regions and factors in varying country contexts to highlight specificities of web scraping and data processing in EMDEs. These include, for instance, types of properties listed, unique forms of data entry specific to EMDEs, or cultural aspects. We do not strive for the representativeness of these five countries for all EMDEs but seek to exemplify the unique challenges of applying a web scraping approach in EMDEs. Nevertheless, transferring this approach to other EMDEs, especially those with lower data quality, will come with additional unique challenges (as discussed in more detail in Section 3.3).

## 3.1 Web Scraping House Prices in EMDEs

The transaction price would be the ideal source to obtain comparable property price data. Typically, these data can be found in land registries or tax authorities, collated from real estate agencies, collected through online surveys, or obtained through appraisals or valuations as part of the mortgage process. However, none of these sources are feasible for automated data collection in EMDEs as the various institutions holding these price data do not yet have a standardized way of collecting, publishing, or even digitizing them. In markets
difficult to find and use in a consistent way. We used satellite images of annualized nighttime lights (VIIRS for 2016, DMSP for 1992-2013) and population rasters from World Pop to calculate the percentage of the population that was electrified (lived in settlements that produce some light at night). We compared the results for two metrics: a dummy that is equal to one if at least 10 percent of the population has access to electricity and the share of the electrified population per district. Such methods have been used before to estimate electricity access in remote areas and guide grid extension programs.<sup>5</sup> This method assumes that locations that emit lights at night are in settlements that have electricity access and that their electricity is most likely supplied from an electrical grid. It assumes that small off-grid systems do not emit enough light to be captured by satellites but that larger isolated power networks do. We cross-checked the numbers obtained with country estimates of electrified population from the World Bank.<sup>6</sup> Figure A2 in the appendix shows the share of the population in Ethiopia and Kenya with access to electricity.

We also collected information on transmission grids based on past efforts to harmonize data for infrastructure from primary sources and recent mapping strategies to infer the electricity grids based on satellite data. For past data, we used electricity grids from the Africa Infrastructure Country Diagnostic (AICD), which collected primary data covering network service infrastructure from 2001 to 2006 in 24 African countries (Foster and Briceno-Garmendia, 2010). To complement these data, we relied on a recent effort by the World Bank; Facebook; and other institutions (the KTH Royal Institute of Technology, the Energy Sector Management Assistance Program, World Resources Institute, and the University of Massachusetts Amherst) to use remote sensing, machine learning, and big data to map connected populations and the systems that support them. This group created an algorithm for estimating the location of medium-voltage infrastructure based on nighttime lights and the location of roads assuming that
**Table 7:** Effects of AIA on total investment and cloud: robustness to continuous measure

|Regressions|(1)|(2)|
|---|---|---|
|Dependent variable|Total investment|Cloud|
|AIA available allowance|4.260***|-2.663***|
|(continuous measure)|(1.043)|(0.678)|
|Observations|23,711|2,200|

Note: Time Period: Investment regressions use annual data for the years 2007 to 2013. Cloud regressions use data for 2007 and 2013. The AIA allowance is calculated taking the average investment values for firms in 2005/06 and calculating how far this lagged investment average is above or below the AIA threshold in any given year. This is expressed in millions of pounds. All regressions are estimated using the approach of Gardner (2022). All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age, not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

of investment. This removes around 110 observations from column 1 and a little under 220 from column 2. We also test whether the results for cloud adoption are driven by the use of firms that make very little investment in capital, including IT capital. In columns 3 and 4 we also exclude the smallest 5 and 10 percent of firms. The results for cloud adoption are robust to this sample restriction, with the effect of the AIA being negative throughout. Importantly the coefficient estimate remains broadly stable across these regressions.

In a second robustness test, we exclude firms that were eligible for other policies during our sample period which may have influenced investment and cloud adoption behaviors. Until 2008
Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. Exit is a dummy variable equal to 1 for the last year the firm exited the sample. Firm-level measure of allocative efficiency is computed as a cross product between two terms defined at the 4-digit NACE sector, year, and county level: (a) the deviation of a firm’s market share from the average market share at the sector-year-county level, and (b) the deviation of a firm’s (labor) productivity from the average firm-level productivity at the sector-year-county level. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-24.9%, 25-49.9%, or 50% or more, respectively, in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. Where indicated, the model includes age groups: Young (3-5), Mature (6-10), and Old (>10), with Startups (0-2) being the reference category, and size group: Small (10-49), Medium (50-99), Large (100-499), and Very large (500+). with Micro (1-9) being the reference category. For outcome variables in level form, the age and size dummies are defined using contemporaneous age and size variables, but they are defined using lagged values for outcome variables in growth (or change) form.<sup>**†**</sup> indicates values are for the state ownership model using 10-100% stake. All models include a constant term.

**Given that Romanian SOEs are less likely to exit the market, they could hold on to scarce resources, hampering allocative efficiency**
A rich set of household and community characteristics is included in _Xhjkt_ including education and sector of employment of the household head (plus highest education of males and females in the household), controls for demographics (household size and composition), assets and access to services, distance to the nearest market and road quality. This is done to ensure a good estimate of the non-stochastic element of household welfare is available for the vulnerability analysis. However with cross-sectional data as explicit in the equation above, there are still possibly some unobserved household characteristics, _μh_ , that are important for determining household welfare. This is therefore a caveat to the interpretation of our results.

# **2.2 Data**

The base datasets for the analysis are the nationally representative Household Income Consumption Expenditure Survey (HICES) and Welfare Monitoring Surveys (WMS) of 2010/11 and 2004/05 (henceforth 2011 and 2005 respectively). These contain information on just over 20,000 households in each year. The HICES captures information on expenditure on food and other items. The WMS records household assets and characteristics as well as a fairly detailed module on self-reported adverse shocks. In both years they were administered by Ethiopia’s Central Statistics Agency (CSA). The advantage of using the HICES-WMS for vulnerability analysis is that they are relatively large, nationally representative, comparable across years and allow measures of vulnerability to be estimated at the household level that can be related back to the official poverty measures calculated by the Government of Ethiopia (Woldehanna and Porter, 2013). This is extremely useful in a context where covariate shocks in particular (as our results show) can impact significantly on poverty numbers. The household level data allow us to look at the relative importance of geographic and household factors in determining vulnerability, and also how vulnerability varies across certain groups of households.

Data were collected on a rich set of self-reported idiosyncratic shocks experienced in the last 12 months.<sup>5</sup> As discussed above, self-reported shocks are
14.9 T|
|Aggregate economic cost as a share of total spending on basic||||
|education|6.5%|15.2%|24.3%|

Source: Authors’ calculation. Simulation 1 results based on latest available LAYS of 157 countries, reported for the full sample and for the subsample of 92 countries for which PISA or PISA-D data is available (unweighted averages). All dollar figures are expressed in 2017 PPP dollars.

52
making their lending relationships more stable and generally improving access to credit for all firms (Giannetti and Ongena, 2009b). Similarly, other firm-level evidence showed that lending by foreign banks was associated with growth in firms‘ sales, assets, and leverage (Giannetti and Ongena, 2009a). Again, however, those results were from before the global crisis studied in this paper.

Although the signs of the coefficients for the foreign participation variables are the same when we run these regressions on the full BEEPS sample, magnitudes and significance levels are smaller than for the balanced panel of BEEPS respondents (regressions not reported). It should therefore be kept in mind that these results hold for a sample that differs slightly from the full one as described above.

# **D. Financial Constraints during the Crisis – Discussion**

At first blush, it may seem surprising that the changes in reported financial constraints were more severe for large firms than others during the crisis. Their size and age makes them more transparent for lenders to evaluate, often they have more and better collateral to pledge, and they are more likely to have long-standing relationships with multiple banks, all of which could help them obtain or retain external finance to a greater extent than other firms during the crisis. In this sub-section, we therefore compare the tendency to seek external finance and the evolution of financial constraints from 2005 to 2008-9 for large versus small firms.

While all firms were more likely to apply for loans in the 2008-9 BEEPS than in 2005 or 2002, the increase was especially pronounced for large firms (Table 6). Whereas 39.2% of large firms applied for a loan in the 2005 survey, 71.8% did so in the 2008-9 survey. For small firms the share that applied for loans hardly increased over the two surveys (from 25.8% to 26.7%). This does not necessarily imply that small firms were less affected by the drop in demand that accompanied the crisis than large ones. Both
procedures for starting new business have been simplified. According to the 2009 World Bank ́s Doing Business report, which covers 10 indicator sets in 181 countries, the regulatory environment for business is - among our country group - most favorable in Estonia, Lithuania, and Latvia. In some of the other countries, notably Croatia, Poland, the Czech Repbulic, and Slovenia there are still important barriers for business activity (Table 14).

**Table 14: Ease of Doing Business (rank among 181 countries)**

|NewEUcountries andCroatia|Bestperformers:|
|---|---|
|Estonia(22)|Singapore(1)|
|Lithuania(28)|NewZealand(2)|
|Latvia(29)|United States(3)|
|Slovakia(36)|Selected European countries:|
|Hungary (41)|Denmark(5)|
|Bulgaria(45)|United Kingdom(6)|
|Romania(47)|Finland(14)|
|Slovenia(54)|Sweden(17)|
|Czech Republic(75)|Germany (25)|
|Poland(76)|Spain(49)|
|Croatia(106)|Greece(96)|

_Source:_ World Bank, Doing Business 2009.

43
<br>proportion of GDP|0.024|-0.002|0.000|0.001|
||(7.981)|(-0.607)|(-0.003)|(0.232)|
|Index of regulatory<br>governance 0-4|0.056|0.043|-0.026|-0.011|
||(2.982)|(3.444)|(-1.067)|(-0.638)|
|Regulator under 1year|||0.090||
||||(1.465)||
|Regulator 1-3years|||0.187||
||||(2.398)||
|Regulator aged over 3<br>years|||0.353||
||||(4.370)||
|Age of regulator||||0.055|
|||||(4.132)|
|(Age of regulator)<sup>~~2~~</sup>||||-0.002|
|||||(-2.635)|
|||Fixed|Fixed|Fixed effects|
|Estimation method|OLS|effects|effects||
|Adjusted R-squared|0.764|0.952|0.954|0.954|
|<br>S.E. of regression|0.605|0.272|0.267|0.266|
|F-statistic|465.943|372.079|352.169|365.770|
|Durbin-Watson<br>|0.043<br>|0.161<br>|0.163<br>|0.153|
|No of observations|577|577|577||

_Note: t statistics in parentheses_ 26
but it is unfortunate because rural non-farm enterprises account for about 35-50% of rural income and roughly a third of rural employment in developing countries (Haggblade, Hazell and Reardon, 2010) and because women account for an important share of such non-farm activity (FAO, IFAD and ILO, 2011). Moreover, the sector appears to be growing (Lanjouw and Lanjouw, 2001) and rural off-farm diversification is widely considered a potentially promising poverty alleviation strategy as the vast majority of poor people continue to live in rural areas (Dercon, 2009, Chen and Ravallion, 2010).

This paper draws on Rural Investment Climate Pilot Surveys from Bangladesh, Ethiopia, Sri Lanka and Indonesia, unique matched household-enterprise-community datasets recently collected by the World Bank, to analyze gender differences in non-farm entrepreneurship rates as well as differences in entrepreneurial performance. More specifically, the paper addresses two questions:

- 1) _Which income-earning activities do men and women engage in and what accounts for gender differences in activity portfolios_ ? In particular, how do human capital, household characteristics, domestic responsibilities such as childcare and the investment climate<sup>2</sup> affect the decision to run a non-farm enterprise?

- 2) _How and why does non-farm enterprise performance, in terms of productivity, vary by gender?_ To what extent are gender differences in performance driven by i) differences in endowments in (access to) factor inputs and human capital ii) sorting into different activities and iii) differences in returns, either due to gender differences in returns to human and physical capital, or differences in returns to scale and iv) differences in constraints.

The remainder of this paper is organized as follows. Section 2 selectively reviews related literature and discusses the country context. Section 3 briefly describes the data and presents a bird’s eye view of the rural non-farm sector. A more detailed explanation of how our key variables of interest are defined is provided in the appendix. Section 4 examines gender differences in activity 3