rafmacalaba/data-use-mentions-extended huggingface dataset:
a discrete number of inspections in each bin. We do not interpret _N_ as the literal number of shipments, but rather a representation of relative sizes of the number of shipments within each bin.<sup>52</sup> The values of _sl_ and _pl_ should therefore be interpreted as continuous rates. Equations (7), (12) and (13) define the computational model used in our exercises.<sup>53</sup>

# **Data summary**

We report the data that we seek to replicate in Table 5. Using the lagged shipment frequency bins we defined in section 3, we calculate key summary statistics from the 2016 data, statistics that we seek to match in our numerical model. The summary statistics we calculate are: (1) the total share of 2016 shipments in each bin, (2) the number of inspections conducted per 100 shipments in each bin, (3) the average number of lagged shipments (across product-country-firm combinations) in each bin, and (4) the number of inspections per 100 shipments in aggregate.<sup>54</sup> In aggregate, the inspection rate in 2016 was 7 _._ 3 inspections per 100 shipments. The key lesson from Table 5 is that the inspection rate is much higher for shipments that were not observed in the previous year (14 percent of shipments) than for shipments in the other six bins. Inspection rates fall sharply with lagged shipment frequency in the bins with fewer lagged shipments, and then rise somewhat for bins with larger shipment frequencies.<sup>55</sup> Also important for calibration are the bins’ shares of overall shipment activity. Trade flows are exogenous in our model, and we match all of the shipment bin weights exactly in our model calibration. We also calculate the mean number of lagged shipments per bin, a numerical value we use in calibrating ambiguity. The first three lagged shipment bins are

> 52We normalize the total number of shipments _N_ to
who lives in the family, the child’s parents are of course matched, i.e. they are household head and head’s spouse. However, step 1 misses the following case: if household head’s (or head’s spouse’s) child is currently living outside. An important advantage of the CFPS survey is that it collected data on such nonresident children. In the second step, we include these nonresident children in the sample. Our estimation sample thus includes both the nonresident parents of household head and spouse, and also their nonresident children.

The summary statistics for various estimation samples are reported in Table T1.U for the urban households and Table T1.R for the rural households; the upper panel in each table corresponds to China, and the bottom panel to India. For China, the main estimation 15
college or university. This compares to about 25% of the over-18 year old population in Britain (Office for National Statistics, 2016).

As Figure 4 shows, the FMS users’ demographics diverge substantially from the general population. However, the divides in participation on FMS do not track the digital divide. FMS users are older, more male, more educated, and less likely to be an ethnic minority than the general population. While lower levels of education predict lower access to technology in the UK, there is only a small gender gap in access to technology, and it is actually younger people who generally have easier access to technology. Similarly, ethnic minorities have as good or better access than the white population.

Taken together, the participation inequalities on FMS more closely resemble the inequalities in political and civic participation more generally in Britain rather than the inequalities in access to technology.

# **Participant profile → Nature of demands**

FMS participants are sufficiently unrepresentative of the general population for concerns about equality to be relevant. However, it is important to look at whether the requests made through the platform reflect the unequal profile of participants.

The demands made through FMS are much more constrained than those in PB and are limited to local public infrastructure repairs. This means participation’s benefits accrue beyond the individual making the request. Instead, the benefit is felt by people who live in a particular geographic area. We approximate this by looking at the demographics of people who live in the local area where the public good is provided. We use electoral wards (the geographic area represented by local council members) as the areas that approximate the beneficiaries of a public good improvement.<sup>3</sup> We calculate a particular request’s beneficiaries using 2011 census data to estimate the number of people in that group who live

> 3 Ward sizes in the UK vary in size, but typically contain around 5,000 people.

18
and engineers (Islam et al., 2022). Lewis et al. (2022) document that in Indonesia, network sectors are particularly prone to state involvement, as most incumbent firms are owned or substantially controlled by the government. In addition, prices in the network sectors are often either directly regulated or subject to government pricing guidelines. Similarly, in Kazakhstan, retail price controls and regulations are particularly high in the network sector (OECD, 2018a). For example, the government regulates or approves the retail tariffs in air transportation, electricity, and gas sectors, and influences prices for gasoline and liquefied petroleum gas.

A well-developed literature shows that the deregulation of professional services and network sectors is associated with sizable increases in (total factor) productivity, that goes well beyond these upstream sectors (see, for example, Arnold et al., 2008, 2011, 2016; Bourl`es et al., 2013; Duggan et al., 2013; Fernandes and Paunov, 2012; Fried and Lagakos, 2020; Van der Marel et al., 2016). There is still relatively little evidence on the labor market effects of such measures. That said, the existing studies mostly suggest that of pro-competitive reforms in the professional services and network sectors are associated with improved labor market outcomes, even beyond the directly targeted sectors themselves. We summarize these studies below.

Barone and Cingano (2011) conduct research using industry-level data from OECD countries. They

> 5In line with the literature, this section will refer to the professional service and network sectors interchangeably as service sectors.

29
growth in informal non-farm enterprise employment.

A final point on recent employment trends: in OECD countries it is unusual for adults who have entered employment to engage in more than one income earning activity at a time. But in SSA countries, it is not unusual for this to occur as about 40 percent of the labor force reports a secondary economic activity in a different sector. Often, especially in rural areas, the secondary activity is an HE. On average across the countries in our study, about 17 percent of the labor force reported NFEs as primary employment. _However, this only shows about 60 percent of the number of people that are engaged in the sector_ , as another 10 percent, on average, reported non-farm HE or ME as secondary employment (Table 1). Combining primary and secondary employment shows that, on average, 28 percent of the labor force (with a high 37 percent in Ghana) works in HEs or MEs in this sample of SSA countries.<sup>7</sup>

> 7 Even this is an underestimate of total employment because our employment data do not allow us to track which wage and salary employees work in informal NFEs.  However, as shown in the next section, the number is quite small as about 90% of informal NFEs do not have any employees outside of the family.

11
# **(Perceptions of) Inequality, Demand for Redistribution, and Group-Specific Public**

**Goods: A Survey Experiment in India**

Maurizio Bussolo<sup>1</sup> Akshay Dixit<sup>2</sup> JEL Classification: D31, D63, H24, H41, I30, Z13 Keywords: Community; Public goods; Inequality; Distribution; Biased Perceptions.

**Acknowledgments:** We thank Ravi Kanbur for encouraging us to write this paper, participants at the Comparative Politics workshop at Harvard University for helpful comments, and Nayantara Sarma for her active contributions to the research. We are grateful to Mahesh Vyas and Kaushik Krishnan for fielding questions on inequality and redistribution as a part of the Consumer Pyramids Household Survey (CPHS) in July-August 2021, and to the India Country Management Unit for their useful comments.

> 1 The World Bank, mbussolo@worldbak.org

> 2 Harvard University, <u>akshay_dixit@g.harvard.edu</u>
droughts, and landslides in the region abound. In total, since 1950, according to Ötker and Srinivasan (2018), hundreds of disasters have hit small states in the Caribbean, “killing 250,000 people and affecting more than 24 million through injury and loss of homes and assets”.

Preparing for – and responding to – these disasters, requires a people-centered approach and a strong understanding of households’ perceived risks and ability to cope with risks (e.g., U.N. Office for Disaster Risk Reduction, 2023; Upperman et al., 2019). However, many of the Caribbean countries can be considered ‘data scarce’ and there is little evidence on household preparedness for disaster risk from household survey data. With that in mind, this paper provides primary evidence on both households’ exposure to disasters and their ability to cope with these disasters. The paper is based on novel survey data collected in five countries in the region: 1
Policy Research Working Paper 9292

# **Abstract**

The unemployment and labor force participation gender gaps narrowed in Mexico after the 2008 global economic crisis, when female labor force participation increased. This paper aims to understand female labor force participation growth and identify its main determinants. For that purpose, the paper estimates a probit model with data from the National Employment Survey of 2007 and 2017, when the unemployment rate returned to the pre-crisis level. Broadly, the results show that increasing labor force participation of women ages 36 to 65 sustained the growth of overall female labor force participation, women’s educational attainment can offset any individual or household obstacle to women’s employability, and childcare availability significantly supports mothers’ employability.

This paper is a product of the Poverty and Equity Global Practice. 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 gacevedo@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
and Demirguc-Kunt (2011), applying the recent advances made in the risk pricing literature<sup>6</sup> to an international sample of publicly traded banks in 63 countries. Finally, the cross-country nature of our dataset allows us to examine the impact of the institutional and regulatory environment on systemic stability as well as the relationship between competition and correlated risk taking behavior of banks, which are of particular interest for policy.

The rest of the paper is organized as follows. Section 2 describes the construction of the sample and variables. Section 3 presents the empirical results and discusses the implications. Section 4 concludes.

# **2. Data and Empirical Methodology**

# **2.1. Sample**

In this section we describe the data sources used in this paper. We obtain bank level financial information from Bankscope. We use stock market information from Compustat Global for international banks and stock market information from CRSP for U.S. banks. The Bankscope database reports detailed balance sheet and income statement information for both public and private banks and covers over 90% of the total banking assets in a given country. The Compustat Global database provides daily stock price information for both active and delisted companies accounting for 98% of the global stock market capitalization. CRSP is the standard source for stock price information of U.S. companies. Our final sample consists of 1,872 unique publicly traded banks in 63 countries from 1997 to 2009. Sample size varies across regression specifications because not all variables are available for all bank year observations.

# **2.2. Systemic Default Risk Measure**

We use the Merton (1974) contingent claim framework to measure bank default risk. This approach treats the equity value of a company as a call option on the company’s assets. The probability of default is computed using the “distance-to-default” measure, which is the difference between the asset value of the firm and the face value of its debt, scaled by the standard deviation of
<!-- Start of picture text --> EAP Lorenz curve<br>~ 4Lorenz curve = ~ 4Lorenz curve (zoomed) bs<br>BSa =Qa<br>ae oO aa cop}<br>ge @ i<br>8 8 yf<br>ao © 20 bil ~<br>rs i he<br>2 2 sia<br>a G ty<br>at= ~ GNfe beter S<br>2a 4BvA 2a Sia<br>: | Pag<br>a = "ca =<br>0) 2 4 6 8 1 8 85 9 95 1<br>Cumulative population share, p Cumulative population share, p<br>——— "1988 HA 19GD —wrenrenenes “41997 —:— = 2002<br>——— 2007. —-—'—:- 2012 Equality<br><!-- End of picture text -->

<!-- Start of picture text --> The contribution to EAP inequality<br>Within- (W) and between- (B) country inequality (GE(0), Theil-L)<br>“A 0) al _2 wes 4<br>Between and within-group inequality (Theil-L)<br>MM 8on MB ePxH. ( BOtherEA «69 B-cHN—Ss (I BP<br>GB w-ion) =)s we W-OtherEA [MM w-cHN = [ w-pac<br><!-- End of picture text -->
and middle-income countries. The point estimate for the other resource-dependent economies was below the lowest projection in PNG for all indicators; and for four of the seven total indicators (6 deprivations and the MPM), the lowest projection in PNG is still higher than the highest average projection in other resource-dependent countries.

> Although it is difficult to precisely interpret the difference between GDP-based pro29 Figure 9a uses GDP per capita obtained from the PNG Economic Database, available at (accessed March 2023): ”http:/devpolicy.org/pngeconomic”.

> 30A list of resource-dependent countries that are low- or middle-income, as defined by IMF (2012) is available from the authors upon request.

> 31The estimates are qualitatively identical when including other comparator countries to PNG, such as Peru, Malaysia, and Solomon Islands (e.g., World Bank 2023).

18
Considering the relation between IT use and a higher level of Chinese competition, we calculated an indicator variable based on the change in the share of China over Mexican imports. Comparing IT use between these two groups, as Figure 3 shows in the period 2008-2012, firms that faced more competition from China experienced a higher increase in IT use. Therefore, the data are consistent with the hypothesis of higher competition generating incentives for increases in IT use.

Figure 3: IT use by Chinese competition level 2008-2012 <!-- Start of picture text --> Computers−per−worker Share of labor with computer Share of labor with internet<br>2008 2012<br>.3<br>.2<br>.1<br>Mean of ICT−use variable<br>0<br>Higher Chinese competition Lower Chinese competition Higher Chinese competition Lower Chinese competition Higher Chinese competition Lower Chinese competition<br><!-- End of picture text -->

> 1 Higher competition is defined as a sector that has a change in the share of Chinese imports over Mexico’s total imports above the median.

Source: Authors’ calculations with data from ENTIC 2009 and 2013, INEGI

# **5 Results**

## **5.1 IT use and Chinese competition**

Following what we observed in section 4.4 and in order to further analyze the correlation between IT use and Chinese competition, we estimated a regression of IT use over Chinese competition: 12
and macroeconomics to incorporate firm heterogeneity as a central component. While a frontier topic globally, there exists little evidence on managerial practices of firms in Croatia and the relationship between management and productivity. Nevertheless, there is some evidence on management in countries from the Europe and Central Asia region, where Bloom et al. (2011) find a strong association between managerial practices and firm performance.<sup>1</sup> To quantify the importance of management for the performance of firms in Croatia, and particularly in Eastern Croatia, we conducted a survey on firm capabilities in Croatia, which includes the following modules: (i) Working Time, Employed Personnel and Remunerations; (ii) Training; (iii) Business Capabilities and Entrepreneurship; (iv) Government Programs and Funding; (v) Global Production Chains; (vi) Science, Technology and Innovation and (vii) Access to Finance.

This study analyzes the outcomes for Eastern Croatia along several dimensions of firm performance, including not only labor productivity, total factor productivity (TFP) and profits, but also outcomes relating to average skill intensity, training, innovation and technology adoption. These outcomes are evaluated with management capabilities being the centerpiece, that is, we associate all the measures of firm performance with the adoption of structured management practices.

How are structured management practices measured? The survey module on Business Capabilities and Entrepreneurship includes 15 core questions of the US Census Management and Organizational Practices Survey <u>(MOPS) main module. The survey was deliberately designed to replicate the US Census MOPS to maximize</u> comparability with the United States as well as other countries where this survey has been conducted (e.g., the United Kingdom, Canada, the Russian Federation and Mexico). We exploit the survey data on 585 small and medium enterprises (SMEs) in the manufacturing and services sectors. Of these 585 firms, 145 firms are from Eastern Croatia. The survey was conducted from January 2019 to June 2019. The FINA database
||(1.115)|(1.134)|(1.029)|(0.989)|(0.964)|(1.352)|
|Initial IT*Competition|-13.430***|-13.859**|-12.133**|-10.802**|-9.779*|-14.529|
||(4.971)|(5.663)|(5.408)|(5.118)|(5.054)|(10.937)|
|F-frst stage IT|14.5|12.9|11.9|12.7|12.2|9.4|
|F-frst stage IT*competition|13.1|14.0|15.4|15.3|15.3|15.1|
|Sargan|0.28|0.32|0.40|0.44|0.45|0.30|
|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,
women‟s market work participation in Nepal in 2004. Further evidence in Menon (2009) indicates that given the dominance of agriculture as the main source of employment, and the fact that agriculture is subject to the variability of rainfall, households in rural Nepal strive to diversify their sources of income by investing in off-farm work such as non-agricultural wage or self-employment. Since non-agricultural wage employment tends to be dominated by men and is relatively less attractive for women in rural Nepal, the added worker effect in this context may have manifested itself in the guise of more self-employment being undertaken by women. In order to account for effects on self-employment that may differ from overall employment, this study estimates impacts separately for self-employment.

# **III. Grouping of Districts into Conflict and Non-Conflict Sub-Regions**

Our research design centers on the idea that regions in Nepal characterized by greater forest coverage, higher elevations, more rainfall, and fewer roads were more conducive to guerilla activity and conflict. Following the strategy developed in Angrist and Kugler (2008) and in order to avoid feedback effects, we classified regions based on geography from a time period that precedes the conflict (1994). Geographical measures from a pre-conflict time period were used as instruments to approximate conflict intensity from 1996 to 2006. In a “first stage” procedure, we tested the predictive power of these instruments in explaining conflict intensity where conflict intensity was measured by the total number of casualties due to state and Maoist action from 1996 to 2006.<sup>2</sup> Note that the Nepal employment and conflict data are based on a geographical coding scheme that divides Nepal into 75 districts, which are further classified into five regions (Eastern, Central, Western, Mid-Western, and Far-Western) and three categories of physical terrain (Mountain, Hill, and Terai grasslands). In order to facilitate parameter estimation in the 8
# **Figure A1: Financial data**

# A.  10-year government bond yields

<!-- Start of picture text --> Percent<br>Median Interquartile range<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text -->

# C. Sovereign spread (EMBI+)

<!-- Start of picture text --> Percent<br>Median Interquartile range<br>25<br>20<br>15<br>10<br>5<br>0<br>E. Equity prices<br>Percent Median Interquartile range<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text -->

# E. Equity prices

# B. 3-month interest rates

<!-- Start of picture text --> Percent<br>Median Interquartile range<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>D. Real exchange rate<br>Percent Median Interquartile range<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>F. Portfolio and other capital inflows<br>Percent of GDP<br>Median Interquartile range<br>15<br>
9 is presented in section 4. The insights emerging from the meta-analysis are highlighted in Section 5. Section 6 offers concluding remarks.

# **2 Methodological Considerations in Tracking Poverty via Poverty Predictors** **_The Adapted Small Area Estimation Technique_**

Following Kijima and Lanjouw (2003) and Stifel and Christiaensen (2007) we employ an adapted version of the small area estimation (SAE) methodology originally proposed by Elbers, Lanjouw and Lanjouw (2002, 2003) to impute a definition of consumption from one household survey into the other. Consider two household surveys covering a given population in two separate time periods, designated round 1 and round 2. The core intuition behind the adapted SAE methodology is to predict per capita consumption at the level of the household in a survey fielded at time _t+1_ using the available information on these households at time _t+1_ (e.g. consumption subcomponents and/or non-consumption assets) as well as the parameter estimates derived from a model of consumption estimated using a survey fielded at time _t_ (including those concerning the distribution of the error term). By restricting the explanatory variables to those that are comparable across surveys, the method ensures an identical definition of consumption (welfare) across surveys, but assumes that the relationship between consumption and its correlates remains stable over time. If non-consumption assets are used, it also circumvents the need for price deflators.

More formally, let _W(ct)_ represent the value at time _t_ of the welfare measure (for example poverty or inequality) based on the distribution of household-level per capita