rafmacalaba/data-use-mentions-extended huggingface dataset:
elasticity for commodity i, and<sup>_s_</sup> _i_<sup>its budget share. Because income</sup> and substitution effects do not counteract, a first order or second order approximation of the welfare loss would respectively overestimate and underestimate the true change in welfare resulting from the introduction of VAT. We proceed to assess the welfare loss with alternative welfare measures.

# **6  Welfare Analysis**

# **6.1 Welfare Loss: Measurement and Estimates**

Equipped with the parameter estimates from the QUAIDS, we compute the compensating variation, by simulating the impact of price changes. The CV is a measure of welfare change, consistent with consumer theory. It is defined as the amount of income to be given to a household after a price change so that its members experience the utility level they had before the price change. Economic theory and recent econometric research suggest that the exact CV is a better metric for welfare analysis than its more popular competitors - the first and second order approximations (J. Banks et al., 1997, 1996, C. Nelson et al., 2011). The CV is computed with the following equation: 17
Policy Research Working Paper 9338

# **Abstract**

This study connects two important findings in Sub-Saharan Africa. First, digital technologies such as mobile money have become widespread and have increased investment by businesses, especially in East Africa. Second, women-owned business in the region significantly lag their male counterparts in capital investments. Using data for 16 Sub-Saharan African economies, the study connects the two findings by exploring whether mobile money use by women-owned firms increases their investment. The findings indicate that the positive relationship between mobile money use and investment is largely driven by women-owned firms and is statistically insignificant for men-owned firms. Potential channels of these effects are explored. Women-owned firms that use mobile money to transact with suppliers are more likely to invest. Mobile money also seems to facilitate greater provision of customer credit and generally greater demand for more credit by women-owned firms. Such patterns are not observed for men-owned firms.

This paper is a product of the Global Indicators 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 aislam@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 collect data on sources of revenue beyond local tax collection as well as federal transfers, the primary source of municipal budgets in Brazil. Revenue collected from non-tax sources and federal transfers could influence local tax collection in different directions. On the one hand, access to revenue and resources beyond the local tax base may limit municipal governments’ incentives to collect additional revenue from local sources, especially if collecting that revenue comes at a political cost. On the other hand, external resources may promote municipal capacity to enforce tax regulations and facilitate payments. Either way, we include data on these external resources to account for their influence on tax collection. other sources evaluate the extent to which public spending contributes to well-being. Finally, we include an indicator for local GDP per capita to account for local economic activity that we expect to be associated with tax revenue. Including local GDP per capita in models of tax collection also captures economic and resource based unobservables that could influence both the adoption of participatory institutions and tax outcomes. The 18
# **_Baseline data, population and employment_**

The population and residential data shown in Figure 2 are based on the Population and Housing Census of 2010 (BBS 2011) at the union level. To best fit the model, we take households as the unit of observation and then re-scale the resulting spatial distribution of households by average household size to generate a spatial distribution of population. Following BBS (2011) we break the population down into those who report having completed secondary education or tertiary education, whom we define as skilled; the remainder of the working age population is defined as unskilled. We assume that the household head is the primary provider of labor, in which case 36 percent of households are classified as skilled. We also classify households into occupants of modern or traditional housing. This classification is based on building type, and the census reports four types of materials, namely, pucka, semi-pucka, kutcha and jhupri. We identify pucka housing as modern housing, which classifies 42 percent of the housing of the Greater Dhaka area as modern. Thus, we know, for each union, the number of households of each type (skilled/ unskilled) in housing of each type (modern/traditional).

Our data for the productive side of the economy are built up from employment data at the establishment location taken from the Economic Census 2013 (BBS 2014). The Economic Census reports 4-digit industry codes (BSIC 2009) and allows us to establish employment in manufacturing, non-tradable-services and tradable-services in each union. Tradable-services include financial services, professional and business services, information services, hospitals, universities, and air, railway and seawater transport related services. Additionally, the Population and Housing Census 2010 shows a significant proportion of households working in agriculture. We assume those who work in agriculture will work and live within the same union and are low-skilled, and we take the number of households classified as employed in agriculture from this data set.

# **_Transport Network_**

The transport network is mapped in
<!-- Start of picture text --> Procedures required to establish a Days required to establish a<br>foreign-owned company foreign-owned company<br>Sanaa High Income OECD<br>High income OECD ae<br>Sub-Saharan Attica Global average<br>Global average Sub-Saharan Africa<br>East Asia and the Pacific South Asa<br>and MiddleNow Ata Fastabt Asia and<br>Latin Amesica ope<br>and Caribbean Latin Americaand<br>South Asia the Caribbean<br>Li] 5 Li] 5 0 10 20 B71) 40 50 60 bi<br>7 - - -<br>IB @ Days required of domestic and foreign companies<br>IB Procedures required of domestic and foreign companies 1 Additional days required only of foreign companies<br>Additional procedures required only of foreign companies (including international trade license)<br>(including intemational trade license)<br>- DI fons desabase 2012. Source: FY Regulations database 2012.<br><!-- End of picture text -->
Figure 4 plots the ratio of government debt to GDP (―debt ratio‖) from 1997 to 2010. It peaked at 106 percent in 2002 after rising by some 23 percentage points of GDP starting in 1997. It then fell by some 20 percentage points between 2002 and 2009.

**Figure 4. Government Debt and Fiscal Deficit (1997-2010)**

<!-- Start of picture text --> ( % of GDP)<br>12 120<br>10 100<br>8 80<br>6 60<br>4 40<br>2 20<br>- 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Foreign currency debt/GDP Rupee debt/GDP Fiscal Deficit (left axis)<br>Source: CEIC data.<br><!-- End of picture text --> To get a better sense of the underlying factors driving this debt trajectory, we carried out a decomposition exercise aimed at apportioning changes in the debt ratio over the period 19982010 to the primary fiscal deficit, real GDP growth, the real interest rate and the real exchange rate.<sup>6</sup> In addition to these main drivers of debt dynamics, the effect of revenues from privatization ( ̳divestment‘) and other, unidentified factors are shown in the last two rows of Table 1. The way to interpret the numbers in the table is as follows: the first row gives the actual change in the debt ratio for each sub-period in percentage points of GDP per year (the reason for such ―annualization‖ is that the sub-periods are not equal in length). For example, over 19982004, debt increased by an average rate of 2.8 percentage points of GDP per year, while it fell by some 3 percentage points per year over 2006-08. The numbers in the other rows give the average annual impact of each underlying factor for each sub-period in percentage points
Policy Research Working Paper 8824

# **Abstract**

Abstract: This study assesses the redistributive impact of fiscal policy––including expenditures and taxation––in the Arab Republic of Egypt. Using a broadly applied methodology, a fiscal incidence analysis is conducted using survey and government data for fiscal year 2015. Evidence shows that Egyptian fiscal policy reduces income inequality, and that among individual fiscal programs, the largest reduction is due to public expenditures on the primary education system. Compared with similar countries, Egypt’s overall fiscal policy placed it in the median of the distribution of inequality reduction. Fiscal policies in Egypt also led to a decrease in poverty, mostly from the flagship Tamween program. Poverty and inequality could be reduced more effectively if the country would shift away from spending on untargeted energy subsidies to more targeted transfers. The large gap between the government’s expenditures and revenues helps explain the positive outcomes on poverty and inequality but poses challenges in the long term.

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 glaraibarra@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
the 59 countries that collect SME lending data on a regular basis, 86 percent are low and middleincome countries. Table 1 provides detailed information from the survey.

In a number of countries where data are not collected through regular reporting, they are collected through surveys of financial institutions or by estimating lending volume in credit registries. Credit registries contain loan level data and allow estimation of a variety of SME statistics using loan size as a proxy for an SME definition. In 13 countries, including Argentina, Tunisia and Brazil, regulators use public credit registries to estimate the volume of SME lending. In 23 countries regulators conduct periodic surveys of financial institutions to monitor SME lending. The frequency of surveys varies across countries. For example, Estonia, Singapore and Armenia conduct such surveys annually, Algeria and Tunisia monthly, while Uganda does it on an _ad hoc_ basis. Financial institution surveys can be an important tool for a regulator to collect information not only on SME lending volumes, but on other aspects of SME finance such as fees and number of applications received and rejected, all of which are essential for the implementation of SME finance reforms and programs. In 10 countries, 8 of which are in the Western African Monetary Union, regulators combine information from the credit registries and periodic surveys from financial institutions.

There is a clear regional pattern in reporting SME data. More than half of the countries in Asia and Africa report that they collect information on SME finance on a regular basis. These are also regions where regulators tend to identify access to finance as a priority. Financial regulators in high-income countries and in Latin America and the Caribbean are the least likely to collect data on SME finance, as some other agency is usually assigned this task. Most importantly, the survey was able to collect data on the volume of SME financing from 50 countries, which enables us to assess cross-country variation in the levels of SME financing.

# **2.1 SME definitions**

One of the main challenges in performing a cross-country analysis of SME
s companies have the managerial, organizational and technical capacity to shift from a competitive model based on the production of simple products with low wage labor to a competitive model based on the production of sophisticated products utilizing sophisticated production processes and high wage, skilled labor.

The second set of variables measures, in effect, measures the extent of cluster development in a particular country. As the report observes, “In developing countries, clusters are normally shallow or underdeveloped. Firms compete on the basis of cheap labor or local natural resources and they depend heavily on imported components, machinery and technology. Specialized local infrastructure and institutions are absent. As economies advance, clusters advance and deepen to include suppliers of specialized inputs, components, machinery and services; specialized infrastructure; and institutions providing specialized training, education, information, research, and technical support....The challenge for an economy is to move from isolated firms to an array of

> 22  Michael E. Porter, “Building the Microeconomic Foundations of Prosperity: Findings from the Microeconomic Competitiveness Index,” in Peter K. Cornelius (ed.), <u>The Global Competitiveness Report 2002-2003, Oxford University Press 2003, p. 24.</u>

> 23  Ibid., p. 29

> 24    Ibid., p. 25

26
a 120-hour training program focused on soft skills (mainly on communication skills) with two programs, one focused on technical skills (English and software), and another one mixing soft and technical skills for recent college graduates in the Arab Republic of Egypt. They find that the program focused on soft skills does not improve labor market outcomes, whereas the program that combines soft and technical skills has larger effects on income and on the probability of finding better jobs (that require speaking English) even in the long run. One limitation of these two studies is that they do not have measures of soft skills, and thus they cannot test whether the lack of effects is observed even when soft skills are improved.<sup>7</sup> We can rule out significant effects of the training on an index of 14 soft-skill measures after 15

> 6Adhvaryu et al. (2023) study on-the-job soft skills training among Indian garment workers and find significant productivity gains, which they attribute to improved teamwork and collaboration, but no effects on wages or work retention. Acevedo et al. (2020) test the effects of internships combined with either vocational and soft skills training or only soft skills training for at-risk youth in Dominican Republic. They find lasting improvements in personal skills and expectations for women (stronger for the full treatment) and not for men, and improvements for labor market outcomes for women but only in the short run (similar for both treatments). Allemand et al. (2023) evaluate a training program focused on activating conscientiousness for construction workers in Senegal and find that it reduces job turnover. Barrera-Osorio et al. (2020) estimate the effects of two versions of vocational training emphasizing hard or soft skills for relatively poor vocational training students in Colombia. They find that the program focusing on technical skills does better at improving labor market outcomes in the short run, but that focusing on soft skills helps youth sustain employment over the longer term.

> 7Recent evidence has demonstrated that soft skills are malleable in different settings
# ANNEX C Multivariate filters

The unobserved components model can be expanded to include additional indicators of domestic demand pressures to help identify the output gap (Benes et al. 2010). The most commonly used indicators are inflation and the unemployment rate. Specifically, the univariate model (1-4) is further augmented with a Phillips Curve relationship between inflation and output gaps (equation 5), an Okun’s Law relationship between unemployment rates and output gaps (equations 6-9), a relationship between capacity utilization and output gaps (equations 10-13), and a set of equations describing the Taylor rule (equations 14-17).

Given the large variation in available data across economies, switches are employed to add selected equations to each country model based on the country’s specific dataset. If house prices or the unemployment rate data is not available for a specific country, the relevant equations would not be included. At minimum, all countries have output, inflation, and commodity price data.<sup>24</sup>

## Model components

The Phillips Curve relates inflation to the output gap, controlling for the impact of supply side shocks such as import prices on domestic inflation.

where _π_ t is annualized quarter-on-quarter inflation at time t, _π_ mt is import price inflation at time t, and YGAPt is the output gap at time t. Expectations are assumed to be an average of adaptive and rational expectations, weighted by _ρ_ . Inflation expectations are linked to fixed horizon forecasts of inflation from Consensus Economics where available.<sup>25</sup> Okun’s Law relates the unemployment gap UGAPt (defined as the difference between the actual unemployment rate Ut and the equilibrium, or natural, unemployment rate _Ū_ t in equation 6) to the output gap (in equation 7) as: Following Blagrave et al. (2015), the equilibrium unemployment rate process is specified in deviation from steady state. Equation (8) specifies the process for Ut . It implies that following a shock, the non-accelerating inflation rate
the importance of establishing a diverse SCD team early in the process, starting before the concept note stage. Commenting on a concept note or a draft SCD after it is written can be useful, but the likelihood of making significant change, is lower, because SCDs follow a very short timeline. For social inclusion issues to be well reflected, SCD task leadership should bring experts on board early on.

3. **Building collective understanding** . Collaboration across teams is likely to lead to a richer collective understanding of social inclusion from both empirical and policy standpoints. Creative use of household survey data where social experts and poverty specialists collaborate, can lead to the kind of analysis that this paper has highlighted from Bulgaria, Myanmar, Sri Lanka, Panama and other countries. Intensive discussions within SCD teams also orient those members who may not reflect naturally about social exclusion, and help them realize that outcomes in their sectors may be stymied because of the exclusion of certain groups.

4. **Deepening coalitions and in-country partnerships** . While desk reviews and data analysis are an essential first step, they are not substitutes for in-country engagement and consultation. The SCD process can help country teams in deepening partnerships in an atmosphere of trust and transparency, especially around issues that may have earlier seemed too political, too fuzzy or out of bounds. Clearly, such engagements are 25
Policy Research Working Paper 7912

# **Abstract**

This paper examines the economic and social implications of the current system of residential electricity subsidies in Pakistan, and assesses the potential to improve the system’s outcomes through alternative targeting and program design. The analysis is multi-disciplinary in nature, drawing on national household survey data, electric company data on household electricity consumption, a welfare database, and a specially commissioned qualitative assessment of household and service provider attitudes and experiences. Affordability is only one of many concerns among electricity users, with reliability of supply and customer service being arguably more important. The analysis finds that targeting could be improved considerably by allocating subsidies according to proxy-means test scores using an existing national proxymeans test database. Providing a flat credit rather than a price subsidy could also alleviate certain governance concerns. The paper concludes with some guidance on how to carry out these reforms based on international experience.

This paper is a product of the Social Protection and Labor Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at twalker@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