rafmacalaba/data-use-mentions-extended huggingface dataset:
financial institutions only for medium-sized and large enterprises. And larger low-end financial institutions might actually be better in easing access to finance in low-income countries.

Before proceeding, an important caveat is due. Our results derive from cross-sectional variation across countries and although we control for an array of firm and country characteristics, we can therefore not completely exclude the possibility of omitted variable bias. We mitigate this concern, however, by testing for the differential relationship between financial structure and average size of financial institutions, on the one hand, and access to external finance by firms in countries at different levels of GDP per capita, firms of different sizes and firms in industries with different financing needs. It is important to stress, however, that we do not interpret our findings as causal relationships.

The remainder of the paper is structured as follows. The next section discusses the data sources and variables we use. Section 3 presents methodology and section 4 our results. Section 5 concludes.

# **2. Data**

We use data from two main sources to construct our sample. We use the Financial Sector Assessment Program (FSAP) reports, which are jointly prepared by the IMF and World Bank<sup>4</sup> , to construct our measures of the importance and average size of different segments of the financial system and firm-level data from the World Bank’s Enterprise Surveys to measure firms’ access to and use of financial services. Since there is limited overlap between the two datasets, we end up with a total of 54 sample countries and up to 50 countries per regressions. All our countries are developing or emerging countries, with 19 countries in Europe and Central Asia, 10 countries in Latin America, 23 countries in Sub-Saharan Africa, and 2 countries in East Asia and Pacific. The level of economic development, as measured by GDP per capita (in constant 2000 USD), varies significantly across our sample countries, ranging from 134 USD in Malawi to 7,229 USD in Uruguay.

Established in 1999
# _Implementation_

The first-stage estimation is carried out using household survey data in our three respective countries. These surveys are stratified at the region or state level, as well as for rural and urban areas. Within each region there are further levels of stratification, and also clustering. At the final level, a small number of households (a cluster) are randomly selected from a census enumeration area.

Our empirical model of household consumption allows for an intra-cluster correlation in the disturbances (see Elbers, Lanjouw and Lanjouw, 2002, 2003 for more details). Failing to take account of spatial correlation in the disturbances would result in underestimated standard errors. We estimate different models for each region and we include in our specification census mean variables and other aggregate level variables cluster-level effects. All regressions are estimated with household weights. We also model heteroskedasticity in the household-specific part of the residual, limiting the number of explanatory variables to be cautious about overfitting. We approximate both the cluster- and household-level disturbances as either normal or _t_ distributions with varying degrees of freedom.<sup>9</sup> Before proceeding to simulation, the estimated variance-covariance matrix is used to obtain GLS estimates of the first-stage parameters and their variance.

# _Data_

The data used in this study consists of a household survey and a population census from each of Ecuador, Madagascar, and Cambodia. Table 1 presents the basics on each of the data

> 9 Rather than drawing from parametric distributions in our simulations, we can also employ a semi-parametric approach by drawing from observed residuals in the first stage model.  Our results have generally been found to be quite robust to the choice of parametric or semi-parametric draws.

12
2009; Bryceson et al., 2008), local market characteristics (Casaburi et al., 2013), and lack of agglomeration economies and/or human capital (Asher et al., 2018).

Given this contextual uncertainty, evidence exists that the most consistent living standards improvements from rural roads are not economic in nature but instead linked to improved access to services such as health (Van de Walle and Cratty, 2002), finance (Binswanger et al., 1993), education (Adukia et al., 2020; Kristjanson et al., 2005), or administration. Comparing Asian Development Bank projects in the Philippines, Indonesia, and Sri Lanka, Hettige (2006) finds that rural roads most consistently benefit the poor through improved access to services, whereas poverty reduction is conditional on the contextual factors listed above or complementary investments.

The relationship between rural accessibility and human development is pronounced in South Asia compared to global norms. Whereas 29% and 11% of rural and urban populations respectively live in multidimensional poverty globally, in South Asia 64% and 25% do because of the excessive concentration of economic opportunity in urban areas (Asher et al., 2018; United Nations Development Programme, 2017). In India, increasing distance from towns and their services correlates with reduced household earnings (Asher et al., 2018) and improving rural road connectivity increases rural household consumption and reduces rural poverty (Rao and Jayasree, 2003; Khandker and Koolwal, 2011). In Nepal, Jacoby (2000) and Fafchamps and Shilpi (2003) demonstrated that income, cropping intensity, access to non-farm employment, and education levels in rural areas progressively fall as travel times to markets increase. In a small-scale study of 3 Nepali village development committees, Sapkota (2018) found that households with longer travel times to services have higher poverty levels and lower levels of health, education, and reported happiness. Shrestha (2012) found that a 1% increase in travel times to paved roads decreased rural
Policy Research Working Paper 8978

# **Abstract**

The purpose of this work is to analyze the performance of the Ecuadorian pension system, its challenges, and available policy options. Therefore, the study analyzes coverage, financing sufficiency, and sustainability indicators that were created based on information from the _Encuesta Nacional de Empleo, Desempleo y Subempleo_ (National Employment, Unemployment and Underemployment Survey) that was carried out over 2003–16. Likewise, actuarial simulations are made by using the World Bank pension reform options simulation toolkit. The findings show that, although in the latest 13 years there has been coverage extension, resulting from an increase in reported employment and an extension of noncontributory pensions, current coverage is still insufficient. In addition to the challenge posed to coverage extension, in the medium term, population aging would exert some pressure on financial sustainability that, within the current framework, would imply a deficit trend starting in the mid-2030s. However, some public policy areas, parametric as well as structural, have been identified that, together with an extension of noncontributory coverage, may provide a more supportive and sustainable scheme.

This paper is a product of the Social Protection and Jobs 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 author may be contacted at iapella@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
**_<u>________________________________________________________</u>_**

- 11a. ICT patents as a percentage of national total (EPO) in selected countries

- 11b. Share of countries in ICT patents at the EPO

12. Trade in ICT goods and services 13. Top 50 telecommunications firms and IT firms 14. Contribution of ICT-using services to value added per person engaged 15. Contributions of ICT investment to GDP

# _<u>International Telecommunication Union (ITU) Database</u>_

The World Telecommunication Indicators database includes time series data for 200 economies for 1960, 1965, 1970 and annually from 1975-2003 for around <u>80 sets of telecommunication indicators</u> covering telephone network size and dimension, mobile services, quality of service, traffic, staff, tariffs, revenue, and investment. Selected demographic, macro-economic, broadcasting, and information technology statistics are also included.

The data are collected from an annual questionnaire sent out by the Telecommunication Development Bureau (BDT) of ITU, as well as data obtained from reports provided by telecommunication ministries, regulators and operators, and from ITU staff reports. Additional data are obtained from reports provided by telecommunication ministries, regulators and operators and from ITU staff reports. In some cases, estimates are made by ITU staff; these are noted in the database.

The database is available either for one-time purchase (single issue) or as an annual subscription. The one-time purchase is available either via the July 2004 edition of the CD-ROM or as a download via the Internet (edition of February 2005 now available). Updates and revisions of the data will be posted to the ITU website approximately every three months. Subscribers are entitled to BDT Telecommunication Data & Statistics reference services such as clarification of data, sources and methodology used.

Reports can also be ordered in hardcopy form. Some examples include: telephone mainlines (per 1000 people); waiting List (in thousands); International telecommunications, outgoing traffic (minutes per subscriber); Number of personal
(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exports and Imports|0.032<sup>***</sup>|0.024<sup>***</sup>|0.022<sup>***</sup>|0.017<sup>***</sup>|0.011<sup>***</sup>|0.014<sup>***</sup>|
|_(% of GDP, logs)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Market Concentration|0.020<sup>***</sup>|-0.002|0.021<sup>***</sup>|0.006<sup>***</sup>|0.007<sup>***</sup>|0.003<sup>**</sup>|
|_(Herfindahl Index)_|(0.000)|(0.499)|(0.000)|(0.000)|(0.000)|(0.002)|
|Product Concentration|-0.038<sup>***</sup>|-0.024<sup>***</sup>|-0.027<sup>***</sup>|-0.020<sup>***</sup>|-0.018<sup>***</sup>|-0.011<sup>***</sup>|
|_
We find that with schooling, controlling for experience and type of employment, the difference in male-female earnings tips in favor of males in Bolivia, Georgia, and Ukraine (the total earnings difference ranges from 0.44 in Colombia to 0.99 in Georgia). Without selection correction, however, we find wages favoring males across all countries with differences ranging from 0.15 to 0.45. Differences in educational attainment between men and women explain about 13 percent of the earnings gap in Bolivia and a staggering 70 percent of the earnings differential in Vietnam. In the ECA countries (Armenia, Georgia and Ukraine) we find that schooling does not explain differentials in the wages of men and women. This follows from the high average educational attainment among men and women in these countries (see Table 3).

In Panel B, the addition of noncognitive skills scores increases the total difference in the earnings of men and women in Bolivia and Georgia. Differences in men and women’s noncognitive skills (emotional stability, specifically) accounts for an additional 3 percent of the wage gap in Bolivia, controlling for schooling and other observed characteristics. The role of noncognitive skills in Ukraine indicates that differences in personality traits (specifically, extraversion, emotional stability, and conscientiousness) are associated with gender differences in earnings. In Vietnam, noncognitive skills over-explain the wage gap, as seen in Panel A of Table 8.

The role of cognitive skills in explaining the gender wage gap, controlling for experience, education, and personality, is examined using reading proficiency scores (Panel C, Table 8) and computer use on the job (Panel D, Table 8). For reading proficiency scores we find that the wage gap, although still favoring males, is slightly smaller in Bolivia. The differences in cognitive skill endowments are significant in Bolivia, Vietnam, and Kenya but do not significantly explain the gender wage gaps in these countries over and above schooling and noncognitive skills. In contrast to these findings, Nordman et al. (
**Figure 3. Employment informality**

<!-- Start of picture text --> A. Evolution of employment informality B. Levels of employment informality<br>Percent of total employment Indonesia (ILO) Percent of total employment 2019<br>70 Indonesia (NSO) 80 EMDEs (2019)<br>EMDEs<br>65 EAP 70 EAP (2019)<br>2021<br>60 60 2020<br>50<br>55<br>40<br>50<br>30<br>45<br>20<br>40 Self- Informal Employment<br>employment employment outside the<br>formal sector<br>Source: Elgin et al. (2021, updated), ILO (2023), and SAKERNAS from Indonesia NSO.<br>A. Shaded areas are capturing recession years. The blue lines show values for Indonesia (solid from ILO, dashed from NSO).<br>Other lines show the simple averages for East Asia and the Pacific excl. Indonesia (EAP, in orange) and EMDEs excl. Indonesia<br>(in red). Here self-employment (% total employment; ILO 2022) is used as a proxy for informal employment.<br>B. The dark blue bars show values for Indonesia in 2019 (the light blue horizontal bar shows the value in 2020, and the diamond<br>shows the value in 2021). The other bars show the simple averages for East Asia Pacific excl. Indonesia (EAP; in orange) and<br>EMDEs excl. Indonesia (in red) in 2019.<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text -->

# _Firm informality_

23. **Firms in Indonesia are dominated by Micro Small and Medium Enterprises (MSMEs).** The 2016 Economic Census, the last one
agriculture over the 1990-2000 period in the United States.

37 Food manufacturing PACs, forestry and nursery PACs, agriculture service PACs and agriculture distribution PACs contributed more than did farm PACs.

38 Based on Herfindahl indices computed by the authors.

39 Brooks, Cameron and Carter (1998). Lopez (2001) also finds that PAC contributions influenced agricultural policies in other sectors.

40 The PAC data are in three relational data files: candidate information files (CN), PAC committee information files (CM), and files containing transactions between PACs and candidates (PAS). For each election cycle, aggregate contributions by each PAC to every candidate were computed from the PAS 16
show much evidence that assistance through a minimum living standard guarantee program (the _dibao_ ) is an important source of income support for rural households, including the elderly.<sup>4</sup> Part of the reason for this may stem from the fact that the rural _dibao_ was still being phased in at the time of 2005 population sub-sample survey and even when work was being completed for the World Bank Poverty Assessment. Funds available through the rural _dibao_ program are not substantial, and are not likely to be more significant than family support for those rural elderly who are unable to work but still have living adult children.

Also notable in Table 1 is the inability of the elderly to earn income from property. In contrast to the United States or Europe historically, or other developing countries at present, the elderly in China have not grown old in an environment in which they could accumulate land wealth. One legacy of collective ownership of land under China’s land tenure system, the lack of land wealth, limits the ability of the elderly to earn income from rental and may also limit their scope for encouraging inter-generational transfers from their children (who would be prospective heirs).

A look at the sources of income by age cohort among the elderly emphasizes differences in financial support between the young and old elderly. Elderly who are in their early 60s are far more likely to support themselves primarily from labor income, while elderly over age 70 depend far more on financial support from family members. It is notable that nearly a quarter of the elderly between age 70 and 75 continue to report labor income as their main source of support. Apart from the family support and labor income, no other source of support varies significantly by age cohort of the elderly. Moreover, no other component plays a particularly significant role.

> 4The _dibao_ is a minimum living standards guarantee program that provides subsidies to households living below a locally determined minimum income threshold. Households are identified for support through community-based targeting.

7
on the northeastern states was motivated by the strong concentration of displaced people in this part of the country during our study period, while in parts of the country other than the Northeast displacement was not dominant. Thus, this focus is unlikely to be a limiting factor for the interpretation of the results presented below.

Information on the roster of host locations to be surveyed in every round is derived from information collected on a daily basis by key informants on the ground (the Emergency Tracking Tool). Our data comprise 3,444 unique locations.<sup>10</sup> Section 6 provides additional descriptive statistics and information about these data.

_IDP profiling data._ We also use data from the Nigeria IDP Survey project _Profile of Internally Displaced Persons in North-East Nigeria 2018_ to calculate descriptive statistics for the displaced population and the population of host communities. These survey data were collected between June and August 2018, with the objective of obtaining a socioeconomic profile of IDPs and host communities in northeastern Nigeria. The survey contains information on poverty level, education, access to essential services, employment, livelihoods, and perceptions. The total sample contains 1,510 host households and 1,437 IDP households. The sample excluding Borno includes 270 host and 250 IDP households. The survey was conducted in 111 enumeration areas randomly selected from the six northeastern states in a number proportional to the size of the displaced population in each state, and it covers displaced households living in both camps (48 enumeration areas) and host communities (77 enumeration areas). The sampling frame was derived from data on IDP counts collected by the IOM as part of the DTM 2017 project.

# **3.2 Descriptive Statistics**

Table 1 shows descriptive statistics by survey year for women and men. It shows the main outcome variables used for this analysis, which we classify as financial and nonfinancial outcomes (see Table A2 in the appendix for an overview of the outcome variables). We restrict the sample to married men and women to analyze
rich set of financial indicators for both privately and government owned entities. However, it does not have an indicator to differentiate whether a firm is a BOSs or a private firm.<sup>7</sup> To identify which firms in the ORBIS financial module can be defined as Businesses of the State (BOS), we merge the WB BOS database with the ORBIS financial module. BOSs are defined as firms with 10% or more state direct or indirect participation, whereas POEs are defined as those with no state participation or state shareholding below 10%.

This paper focuses on European countries in which there is consensus in the literature on having a higher coverage in the ORBIS financial data despite the caveats of completeness of information (Kalemli-Ozcan et al., 2015), (Bajgar et al., 2020), (Cusolito, et al., 2022). We cover a subset of 16 European countries over the period 2011-2020 for which we have simultaneously i) ORBIS firm-level data, ii) the WB BOS database for the specific country, and iii) representativeness of at least 60% of the national employment in the financial module in ORBIS.<sup>8</sup> We compare the performance of BOSs and POEs through a multivariate approach following Kabaciński, Kubiak, & Szarzec (2020) and Chan, Chen, & Wong (2018) analyzing operational performance measured by labor productivity and three aspects of financial performance: profitability, financial liquidity, and return on investments. We measure labor productivity as operating revenues per worker. Financial performance is assessed through financial liquidity using

> 5 For instance, C is considered as a BOS through 3 layers of ownership in the case where the Ministry of Finance owns (by 10% or more) a company A, which owns company B (by 10% or more), which also owns company C.

> 6 The WB BOS database does not provide information on golden shares or veto powers, so the level of participation and thresholds proposed are used
, not year of registration (since it is only observed once a firm enters the dataset). Bootstrapped standard errors clustered at the region level in parentheses; *** p<0.01, ** p<0.05, * p<0.1 <u>Sources: Data from Serbia’s Financial Accounts Registry and 2006 Life in Transition Survey</u>

# **5. Results**

We find a positive effect of the reform on the number of new firms. This effect is statistically significant at the 1-percent level (table 7, columns 1 and 2). That is, we find that the post-2005 increase in new firm registration was greater in regions with high distrust in courts than in regions with low distrust in courts. The magnitude of the coefficient in column 1 implies that going from the lowest level of distrust (0.43) to the highest level (0.77), the reform increases the number of registered firms by 34% (exp(0.861*0.77)/exp(0.861*0.43)-1=0.34 ). Since the average number of firms registering in a region-year cell is 559, this increase corresponds to 190 additional firms per 12
886|
|Delay Effect|_βD_+_βDK_|0.585**<br>(0.257)|_βD_|0.455**<br>(0.203)|0.471|

Notes: Of the total of 1,658 panel observations, 614 correspond to households with children up to age 10. The specifications include baseline values as covariates when available. Column 1 (2) reports the structural parameter estimates of the permanent, transitory and delay effect of the following NLS regression for households with (without) children: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βPK_ **1** _icK_ + ( _βT_ + _βTK_ **1** _icK_ ) _e_<sup>_−δT_(</sup><sup>_t−Tic_)</sup> + ( _βD_ + _βDK_ **1** _icK_ )( _e_<sup>_−δD_(</sup><sup>_Tic−Te_)</sup> _−_ 1)� + _Xic_<sup>_′θ_+</sup> _εict_ . The last column reports the p-value of a test that the coefficient _βjK_ = 0, for _j_ = _P_ , _T_ or _D_ . Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

31