rafmacalaba/data-use-mentions-extended huggingface dataset:
541|||||
|Firms Without Previous Loans|55,065|||||
|Credit Program Users|40,901|7|14|9|7|
|Firms With Previous Loans|30,937|||||
|Firms Without Previous Loans|9,964|||||

This table reports summary statistics of the different samples used in this paper, i.e., formal firms sample, active firms sample, default model sample, and selection and leverage model sample. The sample of active firms corresponds to the set of firms with positive sales during 2019. The default model sample corresponds to the set of firms used to estimate the default model. The selection and leverage model sample corresponds to the set of firms used in the selection and default analysis in this paper. Columns 1 to 5 show, for each sample, respectively, the number of firms with data, the share of firms and aggregate employment they represent in the economy, the share of aggregate bank credit stock they capture, and the share of aggregate value added they generate. Employment and value added are calculated by aggregating data from tax records of all firms in Chile. Firms are classified across size categories based on their annual sales, according to the criteria defined by the tax authority. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. On the other hand, firms without previous loans are those with no credit records in the banking system throughout the same period.

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|**Box 1: Background on Surveys**<br>**Productivity and Investment Climate Survey (World Bank):**PICS utilizes a standard core<br>questionnaire intended to calculate firm productivity. The PICS sample conducted in Serbia is different<br>from the standard one as it does not cover exclusively manufacturing. The survey was administered to<br>managers of firms and consisted of a core set of questions as well as several modules that can be used to<br>explore specific aspects of the country’s investment climate and links to firm-level productivity. The<br>core survey had 11 sections:|
|---|
|�<br>_General information about the firm_: ownership, activities, and location;<br>�<br>_Sales and supplies_: imports and exports, supply and demand conditions, and competition;<br>�<br>Investment climate constraints: evaluation of general obstacles;|
|�<br>_Infrastructure and services_: power, water, transport, computers, and business services;|
|�<br>_Finance_: sources of finance, terms of finance, financial services, auditing, and land ownership;<br>�<br>_Labor relations_: worker skills, status and training, skill availability, over-employment,<br>unionization, and strikes;|
|�<br>_Business-government relations_: quality of public services, consistency of policy and<br>administration, customs processing, regulatory compliance costs (management time, delays, bribes),<br>informality, and capture;|
|�<br>_Conflict resolution/legal environment_: confidence in legal system and resolution of credit<br>disputes;<br>|
|�<br>_Crime_: security costs, cost of crimes, and use and performance of police services;<br>�<br>_Capacity, innovation, and learning_: utilization, new products, planning horizon, sources
structured interviews with representatives from the private sector before and after the RCT. Although there were some (seemingly isolated) complaints about a select few inspectors using the RCT as an opportunity to demand additional bribes and delay clearance, the RCT does not appear to have induced a widespread change in the prevalence of extortive practices. Second and related, we use fraud records and tax adjustments made by the

> 4 While detailed comments appear to help inspectors detect fraud among declarations for which valuation advice was not issued, they do not change significantly most inspector actions and customs outcomes for declarations for which valuation advice is also issued. This may in part be due to imprecise estimates given a very limited number of declarations selected for valuation advice.

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Policy Research Working Paper 9504

# **Abstract**

Transportation networks underpin socioeconomic development by enabling the movement of goods and people. However, little is known about how flooding disrupts transportation systems in urban areas in developing country cities, despite these natural disasters occurring frequently. This study documents the channels through which regular flooding in Kinshasa, the Democratic Republic of Congo, impacts transport services, commuters’ ability to reach their jobs, and the associated economic opportunity costs from travel delays. This assessment is based on transit feed specification data sets collected specifically for this analysis under normal and flooded conditions. These data sets were combined with travel survey data containing travelers’ socioeconomic attributes and trip parameters, as well as a high-resolution flood maps. The results show that (1) flood disruptions cause increases in public transit headways and transit re-routing, decreases in travel speeds, and thus travel time delays, which translate into substantial economic costs to local commuters; (2) accessibility to jobs decreases under flooded conditions, hindering the establishment of an integrated citywide labor market; (3) there are spatial clusters where some of the poorest commuters experience among the highest travel delays, highlighting socio-spatial equity aspects of floods; (4) certain road segments are critical for the transport network and should be prioritized for resilience measures; and (5) the estimated daily cost of flood disruption to commuters’ trips in Kinshasa is $1,166,000. The findings of this assessment provide disaster mitigation guidance to the Office des Voiries et Drainage under the Ministry of Infrastructure, as well as strategic investment recommendations to the Ministry of Housing and Planning.

This paper is a product of the Global Facility for Disaster Reduction and Recovery. 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 yiyi_he@berkeley
This paper analyzes key linkages between competition, innovation, productivity and inclusive growth, both conceptually and empirically, using firm-level data across OECD and developing countries. The paper‘s principal empirical finding is that innovation and the resulting increases in productivity do lead, when spurred by a competitive business environment, to more inclusive growth. While much of our policy focus here is on the ramifications for vulnerable young firms of a competitive business environment, we also find empirical indications of the efficacy of the policy agenda in support of innovation that includes encouragement of skills and

> 1 Although the concept of  ̳inclusive growth‘ has received considerable attention in the economics literature, there is no widely accepted definition for it. Because of increasing concerns about rising income inequalities and claims that the poor in many parts of the world have not been benefitting much from economic growth, the term inclusive growth is often used interchangeably with a host of other terms, including  ̳broad-based growth,‘  ̳shared growth,‘ and  ̳pro-poor growth.‘  For some of the pertinent definitional issues see Tang (2008).

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2.5 points on the 1-7 scale) is associated with an increase in the probability of graduating of about 2 percentage points. Results on _Institutional Capacity_ and other variables are affected very little by this substitution of Polity indicators for Freedom House.

It could be argued that “institutions” should be limited to a narrower set most directly relevant for economic development, and that political institutions may influence graduation decisions only indirectly, if at all.<sup>20</sup> Accordingly, in regression (iii) we exclude the Polity indicators as well as _Political Freedoms_ . The main results from Table 3 still hold. In particular, the coefficients on income and institutional ratings (ICRG) remain statistically significant and of a similar magnitude. In fact, the ICRG coefficient rises somewhat, presumably because of the substantial correlation between the ICRG and political ratings.

Regression (iv) of Table 5 includes tax revenue as a share of GDP. This measure is often called “tax effort” and interpreted as an indicator of administrative capacity (see for example Brautigam, Fjelstad, and Moore 2008). Greater tax effort may encourage graduation through a second channel, separate from this “institutional” interpretation: governments with access to higher tax revenues have a greater potential to reduce poverty through progressive expenditure and transfer programs.<sup>21</sup> We do not include tax effort in most of our regressions because it is available in a comparable series only beginning in 1990, and its inclusion cuts the sample size by nearly half. Despite the relatively small sample, its coefficient in regression (iv) is positive and significant. A standard deviation increase in tax effort – about 8 percentage points – is associated with an increase in the probability of graduating of about 1 percentage point. Results for tax effort are robust to inclusion in the regression of the Polity democracy index, in column (v) of Table 5.

Regression (vi) includes a standard indicator of financial development, M2 (a measure of money supply)
# **Intergovernmental Fiscal Transfer** in Nine Countries: Lessons for Developing Countries

# Jun Ma

# PART L INTRODUCTION

This paper provides an overview of the intergovernmental fiscal transfer mechanisms in nine major industrial and developing countries, with special reference to the design of equalization transfers. The countries selected are the United States, Canada, the United Kingdom, Australia, Germany, Japan, Korea, India, ancl Indonesia. Most of these countries have relatively developed formula-based transfer systems, and represen **t** he major varieties of transfer systems adopted in the world. The three sections in Part I present a brief review of the economic rationales and basic criteria for designing an intergovernmental transfer system. The following nine sections in Part II discuss the mechanisms adopted by these nine countries, respectively. Part Im compares and contrasts the nine countries' transfer systems and, based on the comparison, attempts to draw implications for developing countries that are considering or are in the process of reforming their intergovernmental transfer systems. It classifies the transfer formulas into four categories, analyzes the data requirements of each type of formula, and uses illustrative examples to show how the calculations should be implemented. A few implementation issues, including the transitional arrangement from an old to a new system, are also considered in this part. Part IV presents an illustrative equalization transfer model for China and the simulatiom results using 1994 data. The appendix of this paper discusses a number of country cases on fiscal transfers from state (provincial) level governments to lower level governments.

In this paper, we use "grant" and "transfer" interchangeably to refer to paynent of funds from one level of the governmen **t** o another.

# 1.1. Economic Rationales for Intergovernmental Transfer

The literature of fiscal federalism suggests several economic rationales for intergovernmental transfers: 1 A. Addressing vertical fiscal imbalances. In most countries, the national government retains the major tax bases, leaving insufficient fiscal resources to the subnational See Broadway et al (1993), Shah
|Figure 3.4.Trade flows in EAPP in 2015 (TWh)|42|
|---|---|
|Figure 3.5.Net imports as a share of domestic demand (percentage)|43|
|Figure 3.6. Power generation mix|44|
|Figure 4.1.East Africa‘s ICT backbone infrastructure|47|
|Figure 4.2.Proposed fiber optic connectivity in East Africa|53|
|Figure 5.1.Spending for regional infrastructure as a share of GDP|59|
|Figure 5.2.Spending for regional infrastructure as a percentage of national infrastructure spending|59|

# List of tables

|Table 1.2.Progress and challenges for regional integration in East Africa|6|
|---|---|
|<br>Table 2.1.Relative performance of transport corridors in Africa|10|
|Table 2.2.Road condition along major transit corridors in East Africa|14|
|Table 2.3.Condition of East Africa‘s regional road network by member country, based on 2008 data|18|
|Table 2.4.Performance of East African railways, 2005|21|
|Table 2.5. Growth in containerized and general cargo traffic between 1995 and 2005|22|
|Table 2.6. Comparative port performance across African regions|24|
|Table 2.7. Comparative performance across East African ports|24|
|Table 2.8.Benchmarking air transport in EAC and other regional economic communities|28|
|Table 2.9.International and domestic connectivity for 2007 (number of city pairs served)|29|
|Table 2.10.All flights from one week in November 2007 for EAC and East Africa|30|
|<br>Table 2.11. Proportion of direct flights and speed of air service in East Africa|31|
|Table 2.12. Grading of
status of the heads of household. Finally, the flexibility provided in setting assumptions about changes in inequality comes at the price of an increased uncertainty in the results.

The two key inputs are country-specific unit record household-level data representing the distribution of living standards for a base year and a set of user-supplied projection parameters characterizing the paths of growth. Household-level data involve six variables that must be submitted to the simulator in the following order: (1) a household identifier, (2) monthly per capita consumption in local currency units for the base year, (3) household weight (or a population expansion factor), (4) an urban dummy, (5) household size, and (6) sector of employment of the household head.

For each year within the projection horizon, the per capita consumption for each household is computed recursively using a growth rate that is equal to the rate of per capita output. The latter is calculated as the real growth in gross domestic product (GDP) in the sector of employment of the head of household, minus the rate of population growth in that sector. For the first three sectors, the sectoral population growth rate is computed from the overall population growth rate and an adjustment factor that depends on the share of each sector in total employment and sector-specific growth rates of employment. The population in the residual sector is assumed to grow at the same rate as the overall population. Household weights are also adjusted recursively using sectoral rates of population growth.

Assuming that inequality within sectors remains constant, _PovStat_ computes the following indicators for the forecast horizon: (1) poverty measures of the Foster–Greer– Thorbecke family, (2) the number of people below the poverty line, (3) mean monthly per capita consumption, (4) Gini coefficients, (5) two inequality measures of the generalized entropy family, and (6) the variance of log consumption per person.

In general, the simulation framework offers the user the opportunity to control the simulation process by
**49.** The incidence of receiving credit is higher among women-operated HEs (11 percent versus 5 percent among male-operated HEs). A larger share received credit from business associations, NGOs, or donor projects, i.e., 17 percent of women-operated HEs, compared to 11 percent of male-run HEs (Table A1.15), which likely reflects the focus of microfinance institutions on lending to women. The share of those with access to loans from savings and credit cooperatives is also larger among women (Figure 4.4).

**Figure 4.4: Gender Distribution of Credit Sources Among HEs and Microenterprises, 2006**

<!-- Start of picture text --> HEs<br>0 .2 .4 .6 .8 1<br>Relative or friend<br>Rotating saving & credit group (UPATU)<br>Saving and credit co-operative (SACCO)<br>Business association, NGO, donor project<br>Bank or financial institution<br>Other sources<br>Male Female<br>Micro enterprises<br>0 .2 .4 .6 .8 1<br>Relative or friend<br>Rotating saving & credit group (UPATU)<br>Saving and credit co-operative (SACCO)<br>Business association, NGO, donor project<br>Bank or financial institution<br>Other sources<br>Male Female<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 50. Lack of access to credit for HEs is often caused by lack of a successful business strategy or investment, which would yield returns high enough to pay back the loan. In FGDs, HE operators reported that they did not apply for credit in part because they were not confident about their ability to repay the loan. This finding was corroborated by the results of the FinScope 2009 survey, which show that among the majority of HEs who have
# **3 Data**

We compiled data on salaried workers from three waves of nationally representative household surveys in Afghanistan: the National Risk and Vulnerability Assessment (NRVA) in 2007, the Afghan Living Condition Survey (ALCS) in 2014, and the Income, Expenditure, and Labor Force Survey (IEL) in 2020. The research team obtained these datasets from the National Statistical and Information Authority (NSIA) under a restricted use agreement. We believe that combining data from the three survey waves offers a more comprehensive understanding of the returns to education by accounting for macro-level shocks, which can drive results in a single period. The analytical sample includes working-age individuals (14–65 years old) who reported their monthly income. NRVA 2007 reports labor force participation and wages for household members 16 years of age and older, while ALCS 2014 and IEL 2020 report labor information for household members aged 14 years of age and older. To calculate annualized income, we first converted the monthly income reported in Afghan currency to United States dollars using the official exchange rates from the annual statistical books published by the NSIA. We then multiplied the converted amount by 12 to estimate yearly income. To simplify the interpretation of returns to schooling on annualized income as a percent change in a person’s income, we took the natural logarithm of the annualized income.

The surveys report individuals’ education levels, which we convert into years using the official education system structure. School education in Afghanistan follows a 6-3-3 system: 6 years of primary education, 3 years of secondary education, and 3 years of high school. In tertiary education, individuals can pursue either a 2-year associate degree or a 4-year undergraduate degree, followed by a graduate degree.

We report the descriptive statistics for the analytical sample in Table 1. Column 1 provides pooled summary statistics, while columns 2 to 4 show survey-specific statistics. The average annualized income is $1,892, and 10 percent of the sample is women. Two-thirds of salaried workers are employed in the
pesos (US$323) per eligible worker. For comparison, the average daily minimum wage in 2009 was 53 pesos, or US$3.35 (CONASAMI 2015).

The wage subsidy program received 744 applications, of which 339 are approved, going to 396 plants for preserving 309,206 jobs ( _Secretaría de Economía_ 2012). These numbers correspond to 3.8% of employers and 34% of permanent employees in eligible industries.<sup>4</sup> Applications that were not approved did not meet the eligibility requirements and/or firing restrictions of the program.

The total amount of funding disbursed through the program was about 1 billion pesos (US$63 million), corresponding to about US$160,000 per plant on average. Comprehensive information on individual subsidies is not available, but partial data from the Ministry of the Economy on 203 beneficiary plants suggests that amounts ranged from 20,670 pesos (US$1,307) to 50.6 million pesos (US$3.2 million – given to Volkswagen Mexico), with a median of about 1.5 million pesos (US$92,522). As a reference point for the size of this amount, the average size of a loan or line of credit among manufacturing firms in eligible sectors in the 2010 World Bank Enterprise Survey, which covers a representative sample of firms in Mexico, was about 5.4 million pesos (US$340,000), conditional on having a loan or line of credit (47% of firms).

In practice, firms typically received the subsidy many months after they limited layoffs, in part because the process of reviewing applications and disbursing funds took some time. Most funds were approved starting in June 2009 (Galhardi 2009) and some of the amounts were still paid out in later years: out of the total US$63 million, US$53 million were disbursed in 2009, US$1.5 million in 2010 and US$
recipients. Including vulnerability on the targeting raises program’s effectiveness because the share of the population living above the poverty line that may be only a shock away from becoming poor. Moreover, this share is likely to increase if the poverty rate has recently declined.

The analysis incorporated the notion of vulnerability to poverty into the estimation of poverty maps by combining small area estimation techniques with methods derived from the literature on vulnerability to poverty. Among the many approaches available for estimating vulnerability, the analysis drew on the approach of Günther and Harttgen (2009), which enables the calculation of welfare variability based solely on cross-sectional data. This approach divides vulnerable households into two groups: first, poverty-

> 1 For a complete review of the relevant literature, see Rao and Molina (2015). See Molina, Corral Rodas, and Nguyen (2022) for a review of the application to poverty measurement.

> 2 Targeting areas where poverty rates are highest helps minimize errors of inclusion—that is, some households that are not poor are included in a program by error—because these areas have smaller shares of nonpoor households.

4
and branches of research institutes that form part of the RAS. These institutions – conduct many of the publicly financed innovation activities (MoES 2009). Gokhberg and Kuztnetzova (2010) assess using Rosstat data that the number of publicly owned R&D performing units in Russia is over 70% which in turn perform the bulk of R&D in the country.

In line with the proportion of government R&D funding, the Russian government owns around 70% percent of the around 4,000 institutions performing R&D. As of 2009, the state owned around 2,600 R&D organizations, including research institutes affiliated with the RAS and state-owned enterprises performing R&D (Gokhberg and Kuztnetzova, 2010), which are classified as business entities in official statistics (Table 3). The number of research institutes has risen in recent years, mainly as a result of splits and spinoffs rather than any increase in the country’s overall research capacity. Thus, the structure of

> 3 The sectors prioritized in the Concept 2020 development plan do not correspond one to one with the patent technological fields. However they are composed of a series of sub-fields of technology. For example ICT is composed of the following sub-fields of the Electrical field: Telecommunications, Semiconductors, Computer technology and Audio-visual technology

> 4 Though later reduced, in 2007, the government invested $5 billion into a new state corporation in nanotechnology, RUSNANO.

12