rafmacalaba/data-use-mentions-extended huggingface dataset:
attention to road transport, they distinguish various road types (tolled and free highways, secondary roads and urban roads). Additional realism comes from separately incorporating itinerary and distance-related costs (fuel, tolls, vehicle and tire depreciation and maintenance) from time-related costs (driver wages and accommodation expenses, insurance and loading/unloading times). Since both are expressed in the same monetary units, the authors can compute general transport costs in levels between any two nodes within France by minimizing the total of distance- and time-related shipping costs. The information used in this calculation comes from surveys (e.g., maintenance costs), administrative data (e.g., driver wages) and price statistics on intermediate inputs used in trucking (e.g., fuel).

In a similar exercise for Spain, Zofío et al. (2014) impute distance (per km) and time (per hour) costs for road freight transportation in Spain at the NUTS-3 level of geographic aggregation. As Combes and Lafourcade (2005), they consider a representative 40 ton articulated truck, which accounts for about 80% of road freight transport in Europe. They convert time costs (Euro/hr) to the same unit as distance 10
we exclude children ( �� �0 ) as they should not contribute to the family income, and that there is no intercept in the formula: where �� is an error term that depends on the household.

For most countries, the �� are all positive as expected and – since there is no intercept – they can be interpreted as the average per capita income brought to the household by each category of people. In some countries �� �0 or �� �0 in the regression, suggesting that people unable to work (category 7) or the elderly (category 8) may reduce the income of other household members, possibly due to caretaking obligations. In these cases, however, we force �� �0 or �� �0 and we re‐estimate the other �� .

The per‐capita revenue in each household _j_ can therefore be expressed as:

# 3. The impact of natural disasters

We model the impact of the following six natural disasters on people’s income: cyclones, storm surges, tsunamis, floods, droughts, earthquakes. The share of people affected by each event is calculated based on data from GAR (for earthquakes, tsunamis, cyclones and storm surges) and GLOFRIS (for floods and drought). The impact on people’s income is the same as for the resilience indicator, for each country. We represent the vulnerability bias between poor and non‐poor households but no exposure bias.

For each natural disaster, we model each event, in each country, independently. For floods we have data for events with return periods between 5 and 1,000 years. For droughts, we have events with return periods of 5, 25, 50 and 100 years. For cyclones, storm surges, earthquakes, and tsunamis, we model events with return periods between 2 and 1,500 years. The assumption that each event can only happen alone and once every year means that we underestimate the real impact.

All events but droughts
# **Table 1  Governance Environment, Macroeconomic, and Reserve Adequacy Variables**

|Variable|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|**Governance**|**environme**|**nt variable**|**s**|||
|Government Effectiveness|204|0.0|1.0|-2.4|2.2|
|Central bank independence|144|2.8|1.2|0|4|
|Corruption Perceptions Index|179|43.3|18.9|9|87|
|Governance Pillar Score|143|5.7|2.1|1.1|9.2|
|**Macroe**|**conomic va**|**riables**||||
|Country risk score|121|46.6|16.3|13|89|
|Number of currency crisis (1971–2017)|164|1.5|1.5|0|7|
|Current account balance (% of GDP)|212|-3.0|9.0|-34|34.8|
|<br>**Reserve**|**adequacy v**|**ariables**||||
|Reserves to GDP (%)|162|0.2|0.2|0.6|2.0|
|Reserves to M2 (%)|126|0.4|0.3|0.0|1.9|

**_Governance Variables._** We use various indices to measure the broader governance environment in which a central bank operates. First, we use the Government Effectiveness index, as compiled by the World Bank.
Note that, for developing countries, the best indicator of ICB governance seems to be on ICB practice (usually measured by turnover of ICB governors) rather than on the quality of the legal framework - which is the best indicator for OECD countries. (See Section 2.5 for further discussion.)

- **_2) Recent developments in the literature on telecommunications reform and regulation.  This is increasingly showing that independent regulatory agencies are associated in developing countries with higher investment (eg mainline penetration rates) and faster productivity growth either directly and/or by augmenting the effects of liberalization/competition and privatization._**

In this context, Gutierrez (2003) is important in demonstrating the contribution to effective estimation of the effects of regulation that can be made by using an index of regulatory indicators. (See Section 2.4 for further discussion.)

- **_3) The results from studies of electricity and Latin American concession contracts provide some preliminary support for the effectiveness or regulatory agencies in promoting investment and/or preventing major concession contract problems._**

The position is similar to that of investigating the effects of telecom regulation 3-4 years ago when developing country telecom regulators were, on average, still very young and when there was little purpose-designed data with which to test the impact of regulation on industry outcomes. For electricity, the lack of available and comparable data on investment in transmission and distribution is a significant problem.

Of course, having an effective regulator may be necessary but is by no means sufficient to generate sustained growth and this may help explain why the results have, so far, been a lot less than conclusive. Besides an effective regulatory agency, the following issues arise

- A well-designed industry structure is crucial – good regulation cannot overcome bad design of industry and market structures;

- The industry and the regulatory arrangements must be based within an effective governance framework – effective regulation cannot take root in corrupt systems or ones where the law courts are unreliable; and

- Commercialization has to be accepted
indicate infant mortality to be below 100 per 1000 live births in all 20 countries for which post-2005 DHS data is available and to be falling in 18 of those 20 countries. This paper focuses on infant mortality, but similar trends are found for under-5 mortality. Among the 20 countries with recent DHS data, the surveys show average annual rates of decline of 3.6 percent in infant mortality (under age 1) and 4.2 percent in under-5

> 1 Data is shown for all countries in Sub-Saharan Africa which have had at least two DHS surveys, including at least one since 2005. Other estimates are available from other surveys as well as composite estimates from the United Nations Inter-agency Group on Child Mortality. We focus on the DHS estimates because they are more clearly comparable over time and across countries than estimates assembled from a mix of sources.
country-level determinants can be grouped into the following categories: macroeconomic stability; business environment broadly defined to include the quality of governance, institutions and regulatory burden on private firms; human capital or availability of skilled workers; open and competitive markets that allocate resources across firms and industries based on comparative advantage and productivity. For firm-level factors, some of these include managerial practice and talent (Bloom and Renen 2007), quality of workers and capital (see, for example, Svyserson 2011 for a review of the related literature), innovation and learning by doing (see for example, Hall 2011 and the literature cited therein), international trade and foreign investment (De Loecker 2007, Van Biesebroeck 2005, Griffith et al. 2002), firm size and age (Tybout 2000, Dhawan 2001, Bradford et al. 2001, De and Nagaraj 2014), etc. As mentioned above, how relevant these factors are for understanding the middle-income trap remains to be explored.

This study adds to the existing literature by analyzing the differences in labor productivity between the UMICs and the HICs using unique firm-level survey data that have been compiled by the World Bank’s Enterprise Surveys (ES). Using this data set not only enables a more detailed and nuanced analysis of the differences in labor productivity across countries in different income groups, but also provides us an opportunity to explore the determinants of labor productivity at the firm and country levels simultaneously. Further, we employ the Oaxaca-Blinder decomposition – which decomposes the productivity gap into an endowment effect and a structural effect – to better understand the difference in the mean level of a variable between two groups and the factors that contribute to the difference. The decomposition is a two-step process. In the first step, we estimate the size and statistical significance of the mean level of labor productivity in UMICs minus the same in the HICs (henceforth, productivity gap). A positive value of the productivity gap implies higher labor productivity in the HICs than the UMICs and vice versa. We estimate the productivity 4
structure from agriculture to manufacturing. We adopt a similar measure in our study. The sectoral value-added data used to compute the structural change index is taken from the UNCTAD Statistical Database.

<mark>For the productivity convergence</mark> , we use a measure of labor productivity efficiency computed as a country’s labor productivity relative to the global labor productivity frontier. To compute the global labor productivity frontier, we consider all the countries in the world as contained in the Penn World Table 10.1. The global labor productivity frontier in a year is then defined as those 9
>6 12 300<br>20<br>10 250<br>5<br>8 200<br>15<br>4<br>6 150<br>3<br>10 4 100<br>2<br>2 50<br>5<br>1 0 0<br>0 0<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 Sierra Leone Liberia Niger Guinea-Bissau Mali Burkina Faso Mauritania Gambia Senegal Benin Ghana Cote d'Ivoire Cape Verde Togo Nigeria<br><!-- End of picture text --> _Source_ : World Bank, including AICD analysis.

Another factor driving the expansion of the internet market is a long-standing cybercafe culture, dating back more than a decade (Lohento 2000). According to the national statistical office there are around 200 cybercafes in the country charging on average 344 CFA per hour ($0.72) (INSAE 2010). Most have an ADSL broadband connection, making it cheaper for a user to go to a cybercafe for broadband access than to obtain their own home connection. Dial-up accounts for only 6 percent of fixed Internet subscriptions; most connections are by fixed wireless CDMA or WiMAX (ATRPT 2009.

> 7http://www.atrpt.bj/Observatoire/telecom2009/internet/TABLEAU%20DE%20BORD%20INTERNET%20JUIN% 202009.pdf

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<mark>deteriorated on government effectiveness and political stability. SSA tends to perform worse than all other world regions – with the exception of the Middle East and North Africa (MENA), which SSA outperforms on the indicators related to voice and accountability and political stability. SSA lags behind all other world regions on measures of government effectiveness and its position appears to have slightly deteriorated since the early 2000s.</mark>

**Figure 4. World Governance Indicators by region, 2000-2020**

<!-- Start of picture text --> Government Effectiveness (percentile rank : 0 to 100)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Worldwide Governance Indicators (WB).</mark> _<mark>Definitions:</mark>_ <mark>Percentile rank ranges from 0 (lowest) to 100 (highest).</mark> _<mark>Regions:</mark>_ <mark>EAP (East Asia & Pacific); ECA (Europe & Central Asia); LAC (Latin America & Caribbean); MENA (Middle East & North Africa); SA (South Asia); SSA (Sub-Saharan Africa).</mark> <!-- Start of picture text --> Voice and Accountability (percentile rank : 0 to 100)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>
Kathlon and Sughd regions. The share of these cotton products in total exports steadily increased from 2 to 24 percent between 1995 and 1997 before slowly declining to fall below 5 percent after 2005. The manufacture of chemicals and chemical products mainly include inorganic chemicals and plastics products.

**Figure 3. Tajikistan: Sectoral Composition of Exports, 1992-2010**

(percent of total exports) <!-- Start of picture text --> 120% 1,000<br>900<br>100%<br>800<br>700<br>80%<br>600<br>60% 500<br>400<br>40%<br>300<br>200<br>20%<br>100<br>0% 0<br>Manufacture of basic precious and non-ferrous metals<br>Growing of crops; market gardening; horticulture<br>Manufacture of textiles<br>Manufacture of chemicals and chemical products<br>Manufacture of food products and beverages<br>Scrap metal<br>Total Export (in million $, RHS)<br><!-- End of picture text --> Source: COMTRADE database and authors’ calculations.

# _Shifting Trading Partners_

There is also a shift in Tajikistan’s major trade partners (Figure 4). In 1992, the top-5 export markets were per order of importance the EU8, the CIS, the EU15+, the Central Asia and the Middle East and North Africa<sup>4</sup> . In 1997 when the number of Tajikistan trading partners covering 90 percent of its total exports jumped from 15 in 1992 to 46, the top-5 export markets were the EU15+, the CIS, East Asia and Southeast Europe. In 2010, the top-5 export markets were the CIS, the EU15+, the EU8, Southeast Europe and East Asia. The slight product diversification around 1997 illustrated in Figure 3 seems to have been accompanied with a slight market
# **Manufacturing Production Function Estimations**

Table 5 analyzes whether a large unorganized sector is associated with lower performance for Indian manufacturing establishments. Columns 1-5 consider India, and Columns 6-7 consider the United States for comparison. We use the 2000 Indian sample for this exercise. We estimate a very simple production function with log output of each establishment as the dependent variable. We use establishment weights from the surveys to create a population level estimation. We control for the basic plant inputs of log employees, log book values of capital, and log costs of materials. We exclude plants with missing values, and we continue to exclude districts that are very small in size as noted in the prior section. Regressions include industry fixed effects to capture broad differences in production techniques.

Our focus is on the district and district-industry conditions that surround each plant. In Column 1, we include the log manufacturing employment density per square kilometer in the district. This measure captures urbanization premiums commonly highlighted in economic geography literature;<sup>8</sup> we use a per square kilometer normalization as Indian districts vary in spatial size. We also add a simple measure of the concentration of the unorganized sector as the log share of district employment in the unorganized sector.

The second column further considers these two metrics defined by district-industry, similar to industry-specific agglomeration premiums in the economic geography literature. The third column refines the unorganized sector’s role by separating out the share of local workers who are not being paid. Unpaid workers are mostly family members, and the surveys directly collect evidence on the extent of their involvement in establishments. Columns 4 and 5 again split the Indian sample by traditional versus modern sectors. Standard errors are clustered by district.

The U.S. estimations in Columns 5-6 are built on the 1997 Census of Manufacturers. This dataset is a universal survey of U.S. manufacturing establishments conducted every five years. We estimate the local conditions by county and industry. We again normalize the local activity measures by the
of growth differs more markedly across the two databases: in the survey-based Povmonitor data, the standard deviation of growth (0.06) is slightly higher than that of inequality changes, while the opposite happens in the National Accounts-based Dollar and Kraay data, in which the standard deviation of growth (0.04) is lower than that of inequality changes. On the other hand, Table 8 also shows that growth and changes in inequality are nearly uncorrelated in both databases -- a conclusion confirmed by the scatter plots in Figure 3. Although the correlation coefficients in the table are of opposite signs (negative in the Dollar and Kraay data, and positive in the Povmonitor data), both are insignificantly different from zero.<sup>23</sup>

> 22 http://www.worldbank.org/povmonitor.

> 23 Similar evidence is provided by Deininger and Squire (1996), Chen and Ravallion (1997) and Easterly (1999).

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). This suggests that fast poverty reduction will be hard to achieve without declines in inequality, especially in very unequal countries.<sup>3</sup> This paper reassesses the roles of growth and inequality for poverty reduction from a different perspective, based on the use of a parametric approach to model the size distribution of income. Thus, the paper follows an abundant literature spanning over a century -- from Pareto (1897) to Gibrat (1931), Kalecki (1945), Rutherford (1955), Metcalf (1969), Singh and Maddala (1976) and Bourguignon (2003) -- that has attempted to approximate the distribution of income using a variety of functional forms. Specifically, the paper uses a large cross-country database including both industrial and developing countries and spanning almost 40 years to test the null hypothesis that the size distribution of per capita income can be described by a lognormal density.

> 1 This, of course, need not always be the case, since many policies are likely to be both growth-promoting and equality-enhancing. But some empirical evidence suggests that not all policies have this feature (Barro 2000, Lundberg and Squire 2003, Lopez 2004), and some may force policy makers to face a trade off between faster growth and increasing inequality.

> 2 Specifically, Ravallion (1997) interacts the growth rate with one minus the initial Gini coefficient, whereas Ravallion (2004) considers a distributional term of the form (1-Gini)<sup>θ</sup> with θ>1, to incorporate possible nonlinear interaction effects between the growth elasticity of poverty and initial inequality. 3 A similar point is made by Bourguignon (2004), who presents a simulation model calibrated on Mexican data. When no change takes place in the distribution of income, the model shows that a per capita growth rate of 3 percent per year over a 10-year period lowers the poverty rate by about 7 percentage points. When the same growth rate is accompanied by declining inequality (a reduction in the
Policy Research Working Paper 10061

# **Abstract**

This paper explores the link between jobs, access to Finance, and informality. Using longitudinal firm-level data for countries in the Middle East and North Africa, it documents that jobs creation is positively associated with access to finance. At the same time, the findings show that access to finance is lower for firms that are more exposed to competition from informal firms. As a possible mechanism underlying this result, the paper provides evidence that firms that suffer informal competition have worse expectations on future sales growth, which in turn are associated with fewer loan applications.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 emanuele.brancati@uniroma1.it, michele.dimaio@uniroma1.it, and arahman@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
maquila-- might reduce migration.<sup>2</sup> In a times series context, Hanson and Spillimbergo (1999) find that border apprehensions are responsive to US-Mexico wage differentials, a finding confirmed by McKenzie and Rapoport (2003) using a retrospective survey. Davila and Saenz (1990) find a negative relationship between lagged maquila employment on the border and monthly border apprehensions as a proxy for migration pressures. Unfortunately, more direct trade data on FDI and trade are available neither at the level of periodicity or span to offer enough degrees of freedom to permit inference with any confidence. Further, the massive peso crisis beginning roughly concomitant with the signing of NAFTA led to a sharp spike in unemployment and a 25% fall in wages. These likely induced flows to the US that complicate inference on the more modest effects that NAFTA might have had in the opposite direction.

For these reasons, this paper focuses on evaluating the mechanisms through which NAFTA related variables might work using data on migration flows _within_ Mexico where the 2000 census data permit careful analysis and the 32 by 32 permutations offer substantial degrees of freedom. Figure 1 shows the rates of net migration (net flows as a fraction of the population in the initial period) by state and figure 2 FDI by state. A casual look suggests that, in fact, there is a relationship between the magnitude of FDI and migration to a locale. In investigating this possibility for FDI and other trade variables more rigorously, we make three contributions to the literature on migration more generally, and then specifically on the impact of trade and FDI flows on migration.

First, we offer the first estimates of determinants of migration flows within Mexico and make some advances in the less developed country (LDC) migration literature. In line with recent innovations in the industrialized country literature, we generate proxies for both the level of amenities and costs of living and find their influence to be statistically significant. Further, contrary to much of the literature either in the industrialized or developing world
<br>Rual Western<br>9% 26% 21% 43% 19% 12% 27% 42%<br>Hill<br>Rural Eastern<br>30% 21% 46% 12% 13% 29% 46%<br>Hill<br>Rural Eastern<br>17% 22% 54% 18% 11% 31% 40%<br>Terai<br>Rural Western<br>17% 15% 64% 20% 13% 30% 37%<br>Terai<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>Income Share Income Share<br><!-- End of picture text --> Source: World Bank Staff Calculation based on Nepal Living Standard Surveys 1995/96 and 2010/11

> 22 Agricultural income includes agricultural wages and net farm income. Non-agricultural income is a sum of nonagricultural wages and self-employment income. Capital income includes housing and non-agricultural rental income and also subsumes other miscellaneous incomes.

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