rafmacalaba/data-use-mentions-extended huggingface dataset:
The index measures five dimensions:

1. _Data Use_ pillar assess a range of services that connect data users and producers and facilitates dialogue between them.

2. _Data Services_ pillar provide an indicator of the quality of data releases, the richness and openness of online access, the effectiveness of advisory and analytical services related to statistics and the availability and use of data access services such as secure microdata access.

3. _Data Products_ pillar assess how well the NSS covers the social, economic, environmental and institutional domains of the Sustainable Development Goals.

4. _Data Source_ pillar reflects the availability and frequency of major censuses and surveys mandated by national statistical acts as well as sources of data that are produced outside the NSS, such as administrative data, geospatial data, private sector data and citizen generated data.

5. _Data Infrastructure_ pillar represents the institutional framework for the statistical system and includes legislation, governance, standards and methods, skills within the statistical system and among the users, and partnerships.

The SPI is constructed as an equally weighted sum of these five components. Each component consists of a set of categorical variables that ensure that the SPI is additively decomposable by subsets of variables and by subsets of countries (or regions). The total SPI score and the scores of each of the four components range from 0 to 1.

The SPI has four main advantages over the SCBI:

- It includes richer and more comprehensive dimensions, covering aspects ranging from data generation, curation, and dissemination to data analysis.

- It has 51 indicators versus 25 in the SCBIs.

- It covers more than 200 countries. The SBCI covers fewer than 150 countries and includes no high-income countries.

- The SPI is built on a conceptual and theoretical framework. The theoretical principles of the SCI are not clearly formulated.

7
/mark>_ <mark>, 17(S4): S1–S28.</mark>

- <u><mark>Jappelli, T. (1999). “The Age-Wealth Profile and the Lifecycle Hypothesis: A Cohort Analysis with</mark></u> <mark>a Time Series of Cross-Sections of Italian Households.”</mark> _<mark>Review of Income and Wealth</mark>_ <mark>, March 1999, Available at SSRN: https://ssrn.com/abstract=173653.</mark>

- <u>Jorgenson, D. (2012). “The World KLEMS Initiative,”</u> _International Productivity Monitor_ , Centre for the Study of Living Standards, 24: 5-19.

- <u>Kiersztyn, A. (2021). “Who moves from fixed-term to open-ended contracts? Youth employment</u> transitions in a segmented labour market.” _Acta Sociologica_ , _64_ (2): 198-214.

- <u>Koning, P. and H. Vethaak (2019). “Decomposing Employment Trends of Disabled Workers.”</u> _IZA Discussion Paper Series_ 12775: Bonn, Germany.

- <u>Kuhn, M. and G. Ploj (2020). “Job stability, earning dynamics, and life-cycle savings”</u> _CEPR Discussion Paper_ 15460: London, United Kingdom.

- <u>Madanizadeh, S., and H. Pilvar (2019). “The Impact of Trade Openness on Labour Force</u> Participation Rate.” _Applied Economics_ 51(24): 2654–68.

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0.35)|
|Mean number of houses in village with brick|0.298|0.159|
|walls|(0.46)|(0.11)|
|Mean number of houses in village with more|-0.355|-0.0584|
|than 5 rooms|(0.52)|(0.54)|
|Fraction of surveyed households with a|1.118|-0.274|
|television|(0.41)|(0.17)|
|Observations|200|200|
|_R_<sup>_2_</sup>|0.78|0.69|

In villages with a larger share of community radios, brick walls are almost significantly more common. Again, the magnitude of the effect is small: a one standard deviation in the fraction of households with brick walls (19 percent) is associated with only a three percent greater share of community radios. These variables are also not jointly significant in this specification. Furthermore,
Policy Research Working Paper 6444

# **Abstract**

Transport costs are widely considered an important barrier to local economic activity but their impact in developing countries is not well-studied. This paper investigates the role of inter-city transport costs in determining the income of Sub-Saharan African cities, using two new data sources. Specifically, it asks how important access to a large port city is for the income of hinterland cities in 15 countries. Satellite data on lights at night proxy for city economic activity, and shortest routes between cities are calculated using new road network data. Cost per unit of distance is identified by world oil prices. The results show that an oil price increase of the magnitude experienced between 2002 and 2008 induces the income of cities near a major port to increase by 6 percent relative to otherwise identical cities 500 kilometers farther away. Cities connected to the port by paved roads are chiefly affected by transport costs to the port, while cities connected to the port by unpaved roads are more affected by connections to secondary centers. These are important findings for economic development in Sub-Saharan Africa since the majority of its population growth over the next few decades is expected to be in urban areas.

This paper is a product of the Environment and Energy Team, Development Research 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 author may be contacted at adam.storeygard@tufts.edu.

_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
they should phase out within a decade, allowing the unsubsidized market to take over. (An exception is made for subsidies targeted to institutions serving the poorest and most costly customers.) Our analysis of global data shows that subsidies in fact continue to be important in microfinance, even for older institutions. Summing across the 1,335 institutions, the total subsidy – both implicit and implicit -- was $4.9 billion per year.<sup>4</sup> Of the total subsidy, 76% went to the 932 institutions that are older than ten years. Most (99.95%) of the subsidy takes the form of equity grants and cheap capital rather than direct donations. We conclude with reflections on next steps for a more transparent policy conversation around the optimal use of subsidy in the microfinance market.

# **1. Method and data**

The data are from the global database of microfinance institutions collected by the MIX Market. Within the microfinance sector, the MIX Market is responsible for collecting and disseminating financial data on microfinance institutions, and its database is the largest industry data source on the finances of microfinance institutions.<sup>5</sup> The raw data reflect local reporting standards, and the MIX Market adjusts the data to help ensure comparability across institutions when measuring financial performance. We begin with the MIX Market adjustments and then make further adjustments. MIX Market adjustments are made for inflation, the cost of subsidized funding, current-year cash donations to cover

> 4 The data use the most recent observation in the period.

> 5 Participation in the MIX database is voluntary, and the microfinance institutions in the sample tend to feature institutions that stress financial objectives and profitability (though the database has become more broadly representative as it has expanded over time). The skew is shown by Bauchet and Morduch (2010) who calculate that the average operational self-sufficiency ratio (a measure of organizational efficiency) of institutions reporting to the larger, socially-focused Microcredit Summit Campaign database is 95
# **3.4 Incorporation of carbon tax**

A carbon tax is levied on the price of fossil fuels to cause a reduction of carbon dioxide emissions emanating from the use of fossil fuels. The carbon tax is modeled as an excise tax that adds to the unit price of the fossil fuels in proportion to their carbon contents. We consider an upstream implementation of the carbon tax whereby no user can escape its payment. The tax is already included in the prices paid by all users of the fossil energy good. This suggests that all firms pay the carbon tax. The government collects revenue from the carbon tax that may be recycled somehow in the economy. We will discuss in the next section different options to recycle carbon tax revenues considered in this study.

# **3.5 Data**

One of the main challenges to analyze policy impacts on the informal economy is data. Despite the large informal economy, developing countries do not systematically collect data regarding the informal economy. Even if they conduct surveys occasionally, the results are not made available to the general public. A CGE analysis addressing the informality requires a social accounting matrix that explicitly accounts for the informal sector. Fortunately, the National Statistical Office (NSO) developed an input-output (I-O) table for the year 2013 that captures the informal sector based on surveys conducted by the same office. However, the data have not been published. We worked with the NSO to prepare a SAM explicitly identifying the informal sectors where possible. The original I-O table available from NSO has 107 production sectors and the same number of commodities. A production sector is divided into two categories – formal and informal. Based on the objective of our study, we developed a SAM with 18 sectors and 18 commodities by aggregating and disaggregating the original I-O table as required. Each sector in the SAM has two types of producers – formal and informal. It is not possible to distinguish the formal and informal categories as the data is not available to track the distinction. Moreover
In such a market the risk of losing a job may be low, but the prospects of finding a new one are poor. The expected duration of unemployment spells will be long and level unemployment will be elevated despite low inflows into joblessness. If inflows into joblessness increase (e.g. due to economic downturn, restructuring, or demographical pressures), unemployment will reach a high level.

A typical example of a flexible, dynamic labor market is the U.S. Indeed, 66 percent of the unemployed find a job within a year (Boeri and Terrel, 2002). The median duration of unemployment fluctuates around 10 weeks and correspondingly the incidence of long term unemployment is very low (less than 10 percent).

Labor markets in transition economies are in sharp contrast to the dynamic labor market, such as the US.<sup>6</sup> Expectedly, flows from employment to unemployment are higher, as transition economies undergo rapid restructuring, which be its very nature is associated with a high rate of job destruction. For example, the flows from employment to jobs in Poland or Russia in the mid 1990 were roughly twice those in the US. However, the outflow rate from unemployment to jobs is also much lower in the transition economies than in the U.S., showing that the rate of job creation is too low to absorb the accumulated unemployment pool. The chance that a worker finds a job within a year after entering unemployment is in virtually all transition economies substantially smaller that in the US. Roughly speaking, the probability that an unemployed persons find a job is in transition economies half that in the US. Thus, all else being equal, if the outflow rate from unemployment to jobs in Poland, say, were the same as in the US then the

> 6  Data on labor flows is available for CEE countries and Russia but not for other CIS countries.

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**b** . **A government budget composition variable** .  Given that both education and health are labor-intensive activities, the government’s labor policies will determine the efficiency with which outputs are delivered.  We choose a budget composition indicator to reflect this, in particular, the ratio of the wage bill to the total budget.  A higher ratio is expected to be negatively correlated with efficiency.

**c** . **Per-capita GDP** . We included the per-capita GDP to control for the BalassaSamuleson effect in comparing across countries.  If richer countries tend to be more inefficient (given higher wages in these countries), a negative sign is expected. However, it must be recalled that to obtain the efficiency scores in the “fist stage” we constructed an auxiliary variable (the orthogonalized public expenditure). Hence the inclusion of this variable in the second stage is an attempt to control for any remaining Balassa- Samuleson effects.

**d** . **Urbanization** . The clustering of agents make it cheaper to provide services in urbanized areas rather than in rural.  Higher degree of urbanization should reflect in higher efficiency, making positive as the expected sign of the coefficient on this variable.

**e. Prevalence of AIDS.** Based on WHO mappings of the disease, we included a dummy variable in the most severely affected countries to control for the role of this epidemic in the poor health outcomes.  Evans _et. al._ (2000) report that AIDS lowers the Disability adjusted life expectancy (DALE) by 15 years or more.  Aids also affects education outcomes both directly and indirectly (Drake, _et. al._ 2003): directly because school-age children are affected: UNAIDS estimates that almost 4 million children have been infected since the epidemic began, and two thirds have died. However, the indirect channel is relatively more important: AIDS leaves orphaned children that are more likely to drop-out of school or repeat. All these factors reflect how
> <mark>These demographic factors are highly correlated with an</mark>

> 20 <mark>The regional aggregations are different across countries, based on data availability. In some cases, the lowest administrative unit level where the data is available differs across Internet Surveys and GMD. In such cases, we harmonize at a higher level when running models or even in simply comparing indicators from the two datasets. Specifically, we define region at the state level in Brazil, province level in Indonesia and Sri Lanka, region level in Egypt, and county level in Kenya. For Türkiye, location data are missing in GMD, so region fixed effects are not used.</mark>

> 21 The specific interaction variables used in each country/wave model are available upon request.

15
leverage ratio is a step in the right direction, it is not clear whether 3% will be enough to cover bank losses should a crisis of a similar magnitude as the GFC occur again.

Reliance on internal models to compute risk-weights continues under Basel III. Figure 15 shows the percentage of countries in each ECA group adopting different approaches for calculating capital requirements for credit risk. Russia, Turkey and all countries in the CE and BC regions use an internal model for computing credit risk which determines risk-weights used in regulatory capital. Other developing countries in the ECA region have been selective in adopting some of the Basel II/III provisions, opting to use a standardized approach to calculating credit risk. As our analyses have shown, this is not necessarily a bad thing. It makes more sense to focus on a framework that is transparent and robust for computing credit risk. It is also important to keep in mind the notion of proportionality and incorporate the framework that reflects the characteristics of the local financial systems and refrain from incorporating unnecessarily complex and costly elements of Basel II/III.

**Figure 15: Approach for Calculating Capital Requirements for Credit Risk as of 2016**

<!-- Start of picture text --> EU CE & BC WB SC CA Russia Turkey EE RoW<br>Simplified standardized Standardized Internal ratings-based (IRB)<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for WesternBalkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world
br>4<br>3<br>2<br>1<br>0<br>F. Air pollution, mean annual exposure:<br>countries<br>Micrograms of PM2.5 per cubic meter<br>60<br>1990 Latest OECD U.S.<br>50<br>40<br>30<br>20<br>10<br>0<br>Malaysia Thailand Vietnam Indonesia Philippines China Lao, PDR Cambodia Myanmar<br>Malaysia China Thailand Indonesia Cambodia Vietnam Philippines Lao, PDR<br>China Myanmar Vietnam Thailand Cambodia Indonesia Malaysia Philippines Mongolia Fiji<br><!-- End of picture text --> Sources: Environmental Performance Index; World Economic Forum; World Development Indicators, World Bank. A. Ranking of 140 countries according to the quality of their infrastructure. 1= best, 140 = worst.

B. The Environmental Performance Index (EPI) is constructed through the calculation and aggregation of 20 indicators reflecting national-level environmental data, including child mortality, wastewater treatment, access to drinking water, access to sanitation, and air pollution average exposure to PM2.5. These indicators use a “proximity-to-target” methodology, which assesses how close a particular country is to an identified policy target. Scores are then converted to a scale of 0 to 100, with 0 being the farthest from the target (worst observed value) and 100 being closest to the target (best observed value).

C. Latest data are for 2014.

D. 1= extremely underdeveloped; 7= well developed and efficient by international standards.

E.F. This measures the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas. Latest
not identical across all regions and countries, but the World Bank’s Data for Goals group undertook considerable effort to develop a harmonized data set to facilitate cross-regional and cross-country comparisons.

In addition to harmonization, a further methodological challenge is the limited representativeness of the surveys overall and within countries, as they were conducted via phone and used different sampling methodologies. In particular, most countries in Sub-Saharan Africa used a sampling frame based on a previous survey and explicitly sought to interview household heads, while surveys in Latin America and the Caribbean used random digit dialing to collect data (Table 3). This complicates comparisons of individual-level characteristics such as employment across regions. Because of the challenges regarding non-representative sampling of individuals, we report results under two different weighting methods. We rely on the household weights in the HFPS data for our main results and complement them with another weighting method as a robustness check. As a robustness check, we add an additional individual-level adjustment to the weights based on the World Bank’s Global Monitoring Database (GMD).<sup>1</sup> Using this method, we assign an inverse probability weight to each individual observed in the data to make the distribution of individual characteristics (age, gender, education, and urban status) more aligned with the GMD.<sup>2</sup> In the robustness check, we examine how outcomes compare when we rely only on the household weights in the HFPS data and our second weighting method and generally find similar results.

We first report results on high-level measures of work stoppage based on questions that ask respondents whether they were working pre-pandemic and whether they were working at the time of the survey. We find that work stoppage was common. Taking a simple average across countries, 34% of respondents reported stopping work. The average across countries in our data is 21% in the EAP region, 29% in the ECA region, 48% in the LAC region, 45% in the MENA region, and 26% in the
income or a consumption survey and note that it is measured in constant 2017 $PPP, per person per day.

An additional challenge is how to handle the effect of very low or zero measured income when constructing distribution-sensitive welfare measures. Specifically, our welfare measures are based on the ratio z/yi which is undefined when reported income is zero and can be very large when reported income is close to zero, to the point of dominating the index calculated as the average of these ratios across all individuals.<sup>22</sup> To address this problem, we bottom-code all binned observations at $0.50 per day. We arrive at this value by applying the consumption floor methodology outlined in Ravallion (2016) to estimate minimum consumption levels. Applying this method to consumption surveys in PIP, we find that essentially no country has a daily per

> <u>Methodology). For countries without any household survey data, it is assumed that their income distribution is equal</u> to the population-weighted average income distribution in their region.

> 20 The dataset is available at <u>https://datacatalog.worldbank.org/search/dataset/0064304/1000-Binned-GlobalDistribution.</u>

> 21 When zeros in consumption are reported in surveys, they typically are treated as unit non-response. Regarding incomes, Hlasny, Ceriani and Verme (2022) note that more than 75 percent of the 354 surveys from the Luxembourg Income Study that they examine, contain income values taking the value of zero.

> 22 The concern that distribution sensitive measures are highly sensitive to low incomes is not new. Cowell and VictoriaFeser (1996) discuss this issue in the context of data contamination (e.g., measurement error), using influence functions to demonstrate the extent to which a poverty or inequality measure is changed by small errors in the data.

16