rafmacalaba/data-use-mentions-extended huggingface dataset:
One of the contributions of this paper is to update previous results with more recent data. Using data between 1982 and 2002, we confirm the findings of previous studies: we find that longitudinal wage changes due to either changes in import competition or offshoring within the same industry range from zero to _positive_ and significant, albeit small in magnitude. For example, a 10 percent increase in the number of workers employed by US firms in low-income countries had virtually no impact on wages across all skill groups. However, a 10 percent increase in offshoring to high-income countries is associated with a small increase in wages of less-skilled workers of between 0.1 and 0.2 percent. Likewise, the impact of an increase in import penetration is negligible and not statistically significant when we focus only on manufacturing workers.

However, we also find significant employment reallocation across sectors in response to import competition and offshoring. This finding is consistent with a number of studies, which show that the intensified competition brought about by trade liberalization has led to industry rationalization: the least productive firms exit the industry, and remaining firms shed excess labor.<sup>1</sup> The question left unanswered by these studies is what happens to the workers that are displaced. If most of the downward pressure from globalization on wages occurs in general equilibrium, then the downward pressure on wages resulting from workers leaving manufacturing and entering the service sector would be missed by looking only within manufacturing.

This is a key limitation of the existing literature. Using individual-level data we are able to examine the impact of globalization on wages both within the manufacturing sector and across sectors and occupations. We begin by identifying the within- _occupation_ effect of trade on wages. If it is more costly for workers to change occupations than industries, then the wage impact of traderelated job switches could be significant at the occupation level while being negligible within manufacturing. We find a significant divergence between results for occupational exposure and

> 1 See for example Pavcnik (2002), Paus et al.
until mid-2002 (end 2004 for M0). The velocity of M0 and M1 then rose steadily, while that of M2 peaked in October 2008 then went on a downward trend. The third phenomenon is a decrease in the velocity of broad money starting from the end of 2008, which reflects the beginning of deepening of the financial system from the early 2000s. Financial deepening is reflected in the rising share of savings and time deposits in the public’s portfolios and the declining velocity of M2, reflecting its faster growth relative to nominal GDP.

**Figure 1: Trends of velocity of money**

<!-- Start of picture text --> 7.50<br> 7.00<br> 6.50<br> 6.00<br> 5.50<br> 5.00<br>M1 velocity M2 velocity M0 velocity<br>Jul-97 May-98 Mar-99 Jan-00 Nov-00 Sep-01 Jul-02 May-03 Mar-04 Jan-05 Nov-05 Sep-06 Jul-07 May-08 Mar-09 Jan-10 Nov-10 Sep-11 Jul-12 May-13 Mar-14 Jan-15 Nov-15 Sep-16 Jul-17 May-18 Mar-19 Jan-20<br><!-- End of picture text --> _Source_ : Authors’ construction using data from NBE’s database.

7
shocks may occur at the household or village level. At another, shocks may occur at a national or region level. A better understanding of the impacts of shocks at different spatial scales requires knowledge of how food markets operate. Weather disturbances in areas that are connected to national and international markets have a much more muted may impact compared to disturbances that occur in remote rural areas.<sup>35</sup> Further, weather disturbances in food-surplus areas may engender food scarcity in other parts of a country.<sup>36</sup> A drought-contingent targeting framework needs to incorporate this information.

While the data we have used in this study are freely available, collecting these data requires substantial public investment and capacity. Our overarching objective is to leverage nationally representative, comparable and publicly available data to construct frameworks that are transparent and straight-forward to communicate. We believe that these are essential prerequisites to designing shock-sensitive government systems that are both credible and sustainable.

> 35See, for example, Baffes et al. (2017), Hill and Fuje (2017), and Baez et al. (Forthcoming).

> 36Baffes and Kshirsagar (forthcoming) develop a quantitative approach to understanding _market systems_ in Tanzania — in particular, they identify markets that are the main suppliers of maize and rice, and markets from which demand shocks originate.

22
The main reason for not registering stated by each business varies greatly between cities in the same country. For example, in Lao PDR, less than 6% of businesses in Pakse report time and fees as the primary reason compared to around 32% in Vientiane. In Zambia, 35% of businesses in Ndola claim that a lack of information about the registration process is the primary reason for not formalizing, whereas this figure is only 10% in Kitwe. Taken together, these findings suggest that the institutions and constraints influencing the motivations of businesses about formalization tend to be city- or region-specific. This is not altogether surprising given that the authorities usually tasked with ensuring compliance with licensing and registration tend to operate at the municipal or provincial level (Bruhn and McKenzie, 2014).

# **4. Conclusions**

This paper introduces and provides a descriptive analysis of business-level survey data of informal businesses in twenty-four cities across seven countries (India, Iraq, Lao PDR, Mozambique, Somalia, Zambia, and Zimbabwe) collected since 2017. The surveys were conducted through face-to-face interviews of owners and managers of informal businesses following the ACS methodology adapted to study this segment of the population. This methodology is examined in much detail in the companion paper Aga et al. (2022). The ACS is a data collection methodology that efficiently provides representative estimates of the population indicators for informal businesses. Importantly, this survey methodology also enables estimating the overall size of the informal sector. While such analysis is beyond the scope of this paper, we note that studies using the data analyzed in this paper show that in Mozambique, for example, informal businesses outnumber formal ones by a factor of 8:1 (Jolevski and Aga, 2019).

The introduction of this cross-city, cross-country business-level database of informal businesses is one of the contributions of this paper. The data combines surveys that cover a wide range of topics through interviews that are administered to a representative sample of informal businesses rendered through the enumeration process (for more details,
GDP, which is based on data from the UNU-WIDER Government Revenue Dataset (GRD), 2020.<sup>12</sup> Panel B in Table 1 describes the four indicators used in the construction of the DEA input index. Panel C lists additional factors which are included in the post-DEA regression analysis (pairwise correlations between these variables are reported in Table A1 in the appendix).

The core sample which is used in the frontier analysis consists of 118 countries, comprising 25 LICs, 45 LMICs, and 48 UMICs. In addition, we have data on 58 HICs as well as 6 countries with revenues above 50 percent of GDP which we exclude as outliers in the frontier analysis, so that the maximum sample size amounts to 182 countries. For most countries, data for the variables listed in Panels A and B of Table 1 are available annually for the years 2008 to 2019. To limit the role of temporary fluctuations and measurement error, most of the analysis is based on values for 4-year periods, which we construct as the mean values of the years 2008-2011, 2012-2015, and 2016-2019 for each country (for ease of exposition, all tables and figures refer to these time periods, even if the data for a particular indicator are only available for a subset of these years).

**Table 1.** List of Variables and Data Sources

|**Indicator**|**Description**|**Source**|
|---|---|---|
|**_Panel A: Revenues_**|**_(DEA output)_**||
|Revenue|Government revenue, excluding grants (% of GDP)|ICTD/UNU-WIDER,<br>Government Revenue Dataset|
|**_Panel B: Economic f_**|**_undamentals (DEA input index)_**||
|Per-capita income|GDP per capita, PPP (constant 2017 international $)|World Bank|
|Share
# **The Worldwide Governance Indicators:**

# **Methodology and 2024 Update**

Daniel Kaufmann* and Aart Kraay** The World Bank

*President Emeritus, Natural Resource Governance Institute, and Senior Fellow at Results for Development and The Brookings Institution; **Chief Economist, Prosperity Practice Group, World Bank akraay@worldbank.org.  The data for the Worldwide Governance Indicators are available at www.govindicators.org.  A note on self-citation: _This paper draws extensively on our earlier joint work over the past 25 years to design, implement, and assess the WGI methodology.  In addition to the 2024 update of the WGI discussed in this paper, the limited new or updated analysis in this paper is clearly indicated as such.  The remainder of the paper should be viewed as a summary of our previous work and not original research.  A full listing of our earlier working papers and journal articles on the WGI is included in the references of this paper._ A verified reproducibility package for this paper is available at <u>htps://reproducibility.worldbank.org.  The views expressed here are the authors’ and do not reflect the official</u> views of the World Bank, its Executive Directors, or the countries they represent.
|
|**Panel B. Peer(average) characteristics**|||||
|Age|24.59|1.70|24.66|1.81|
|Percentage of female peers|0.56|0.27|0.62|0.30|
|Percentage of peers by mother's education:|||||
|Less than primary|0.12|0.06|-|-|
|Primary school|0.15|0.06|0.15|0.14|
|High school|0.18|0.07|0.28|0.19|
|Higher education|0.06|0.06|0.52|0.24|
|Unknown|0.49|0.13|0.05|0.06|
|Percentage of peers with children|-|-|0.27|0.18|
|Peers' socioeconomic index (std)|-|-|0.01|0.37|
|Peers' administrative variables(PCA Score)|-0.22|1.57|0.14|1.36|
|**PanelC. Own andpeers’previous labor market outcomes**|||||
|Percent of time employed before graduation/entry|46.32|43.50|19.02|36.24|
|Average peers' percent of time employed before graduation/entry|49.13|13.50|19.02|21.26|
|Employed at least one month before graduation/entry|0.64|0.48|0.26|0.44|
|Pct. ofpeers employed at least one month beforegraduation/entry|0.65|0.18|0.26|0.25|
|**Panel D.Outcomes**|||||
|Average
# **7 Conclusion**

This paper has developed a simple but consistent measure of the degree to which countries are connected to the international air transport network: the _Air Connectivity Index_ (ACI). Our measure captures important features of that network, such as its hub and spoke structure, and the dual importance of the number and strength of flight connections. As expected, the ACI correlates strongly with important economic measures on both the input and output sides, including the degree of policy liberalization in air services markets, and specialization in parts and components trade as a proxy for trade openness in high value to weight sectors.

There are a number of ways in which future work can expand on our findings. As a starting point, we hope to update the index on a regular basis, to give policymakers and analysts consistent information on connectivity over time. This approach will enable them to track performance, and examine the impacts of policies designed to improve the air transport environment, and boost connectivity.

Second, there is scope for detailed econometric work looking at the extent to which air connectivity determines trade outcomes, and the pattern of specialization across countries. We would expect countries with strong connectivity to specialize in industries such as perishable goods and networked components, which are intensive in their use of high speed transport services. The results presented in this paper can only be regarded as suggestive, and should be investigated more thoroughly in the future.

Third, we expect air transport connectivity to influence exporters' choice of transport mode. This is an issue that can be investigated using data on imports by mode for two major markets, namely the US and the EU. To the extent that a country's ACI score is influenced by its policy environment—and particularly the degree of liberalization of air services markets—there may be scope to reduce distortions in the modal choices of exporters and transport operators by improving connectivity.

37
||**1.002***|
|||[0.193]|**[0.202]**|[0.660]||**[0.507]**|
|Business service||0.047|0.131|-0.263||0.000|
|||[0.248]|[0.252]|[0.658]||[0.000]|
|ln (wait days for permit)||0.031|0.061|-0.208||**0.432****|
|||[0.061]|[0.065]|[0.249]||**[0.216]**|
|ln (distance to market)||0.089|**0.119****|-0.072||-0.004|
|||[0.056]|**[0.060]**|[0.139]||[0.153]|
|**Dummy**<br>Rural|-0.186|-0.253|**-0.333***|-0.424|0.496|0.155|
||[0.169]|[0.177]|**[0.195]**|[0.550]|[0.624]|[0.494]|
|Observations|325|324|291|33|69|69|
|R-squared|0.091|0.218|0.180|0.623|0.150|0.410|

_Source:_ 2010 Yemen RIC Survey; _Notes:_ Sampling weights are used; standard errors in parentheses. *, **,
have been offered loans or who would have been required to put up collateral (Feyen et al., 2020). Similarly, another study in the United States found that more individuals were offered loans at an affordable interest rate when the lender incorporated previous transaction data into risk assessments (Jagtiani and Lemieux, 2018).

While alternative data can help potential borrowers access credit, it also has been found to be beneficial to financial institutions, based on evidence that it is “at least as predictive as that held by credit bureaus” (World Bank, 2022). Evidence from Kenya found that using payment and transaction history in creditworthiness assessments reduced the share of nonperforming loans, 14
# ANNEX 7: SUBSIDY DEPENDENCE INDEX FOR RURAL FINANCE INSTITUTIONS<sup>5 4</sup>

Two main problems face the analyst who must rely on conventional accounting data to measure the financial performance of rural finance institutions (RFIs): the difference between the expense and income (including reimbursement of specific expenses by state or donor) captured and reflected in the RFI income statement and those expenses and incomes not recorded in the RFI income statement, and the lack of a design in conventional accounting practices to reflect and appropriately report on all types of subsidies received by an RFI.

Conventional accounting practice measures the cost of funds priced at their actual cost. The opportunity cost of an RFI's borrowed funds - that is, the cost the RFI would have to pay for its funds if access to concessional funds were eliminated - is not taken into account. The SDI calculation assumes that the volume of the RFI's outstanding loan portfolio remains unchanged. Hence, the change is caused by substituting concessional borrowed funds with voluntary savings, obtained at a market deposit interest rate. Thus. if the central bank loans to an RFI at 2 percent, conventional accounting practices list the cost of the loan at 2 percent p.a. However, if the cost of alternative non-concessional funds is 12 percent p.a., then the SDI considers the 10 percent difference in interest rates on those funds and identifies this subsidy received by the RFI. The rationale is that if the subsidized RFI paid only 2 percent p.a. on central bank rediscounting facilities instead of the prevailing market deposit rate of 12 percent p.a., the accounting profit and the financial ratios measuring the RFI's profitability would not convey that such ratios were only obtained due to the significant subsidy embodied in the cheap central bank rediscounting facilities. Providing an RFI with concessional funds is the most common method of subsidization, yet calculating the value of the subsidy implicit in the RFI's concessionally borrowed funds requires information not included in the RF'
overview of the smallest exporters, while the customs data is likely to be much more exhaustive. As a result, the export propensity calculated for the smallest size class of firms is likely to provide a misleading pattern. Note that from Section 4.3 onwards, when we study the export destinations and product concentration, we lower the threshold to 1000 USD per year.

The number of globally engaged firms in Rwanda has trended up noticeably over the 2008 to 2016 sample period, as depicted in Figure 4. This growth is broadly based, showing up in the three major sectors that we consider—agriculture, mining, and manufacturing—and using various thresholds to identify globally engaged firms. Overall, only 43 firms exported more than 10,000 USD in 2008 and this increased to 101 in 2016, which represents an increase by 135% over 9 years.

In spite of this increase, the total number of exporters is still remarkably low, even in 2016. This is especially true considering that the total of 101 includes all firms selling a mere 10 000 USD per year. This total amounts to a single exporter per 120,000 inhabitants or one exporter per 91 million USD of GDP.

The number of exporters grew strongly after the latest global recession (in 2009), more than doubling from 2010 to 2014. In more recent years, the growth has stalled and in the last two years of the sample period the total number of globally engaged firms even declined. It is more likely to be the result of civil unrest in Burundi and armed violence in the DRC in 2015 and 2016, than because of incomplete reporting in the most recent years. We base this conclusion on several pieces of evidence. First, aggregate statistics for our sample line up rather well with the aggregate data from the World Bank. Second, in conversations with manufacturers in Rwanda, they often highlighted the troubled political situation in the two neighboring countries. Third, results in tables below show that the decline in the last two years is most
How does Lesotho’s performance compare against its relevant country groups, especially low-income countries? What do outcome indicators tell us about the relative strengths and weaknesses of Lesotho’s infrastructure vis-à-vis various income and geographical comparator groups of countries? Where are the largest deviations—positive and negative––from the benchmark group and other comparators? What is Lesotho’s comparative performance in rural and urban areas? And how does one interpret some of these comparisons within Lesotho’s country-specific context in order for it to be useful for policy purposes? The answers to these questions are likely to provide important building blocks towards a more complete assessment of the state of infrastructure in Lesotho, its performance, and any need for reform.

The next section discusses the data, their caveats, and the choice of the comparator groups of countries. The following sections discuss preliminary assessments of infrastructure performance in electricity, water and sanitation, information and communications technology, and roads transportation. The concluding section contains summary remarks.

# **The benchmarking database**

The data used for comparing Lesotho’s infrastructure performance comes from the World Bank research data base that was recently developed with a specific objective to be used for this type of benchmarking exercise (Eustache and Goicoechea, 2005). The database pulls together the latest available observations from a number of well documented, specialized data sources that contain more detailed data with longitudinal information. The data base aims to measure _sector performance_ by focusing on sector _outcomes_ — **_access, affordability/pricing, and quality_** (technical or perceived). It also provides information on sector institutional reform. Data covers 207 countries (although coverage varies by sector and indicator)<sup>1</sup> with a limited number of variables<sup>2</sup> measuring sector outcomes.

For the purpose of benchmarking Lesotho, we compare its available indicators of infrastructure performance with all the world geographical comparator groups--Sub-Saharan Africa, OECD, South Asia, East Asia & Pacific, Middle
observed change in scores: (i) changes in the quantification methodology, (ii) actual changes in policy regime, and (iii) changes in sectoral scope.

- i. For this first level of analysis we use the 2008-11 raw policy data and aggregate these scores alternatively with the original methodology previously used by the World Bank and the improved 2016 methodology (i.e. introduction of the algorithm and use of the CES aggregation method),

> 27 Cross-sectoral differences in the relevance of Mode 4 measures are accounted for at the level of individual measure scores.  For instance, a quantitative limit on intra-corporate transferees attracts a restrictiveness score of 0.125 in professional and transport subsectors and a score of 0.25 in all other sectors where the use of ICTs is more widespread.

> 28 WALI is a synthetic measure of the openness of the air transport policy of a given economy. It is calculated as an average of the air liberalization indices of all the Air Services Agreements concluded by that economy, weighted by the respective traffic they cover. WALIs are computed for all four ALI weighting systems. Further assumptions are required to utilize the BASA-based WALI for gauging restrictiveness in air <u>cargo transportation.  We assume that (1) roughly 50% of air cargo travels in the belly hold of passenger</u> aircraft, and (2) mode 1 trade using dedicated cargo carriers is essentially open.  Combining both, we use a weighted average of (0.5*rescaled_WALI + 0.5*0) for mode 1 air cargo transport. It is also important to note that the same index was used for 2008-11.

18
occupational status only in this paper. Highest parental education was **also** classified into four categories: (a) vary little or no schooling, (b) primary school, **(c)** secondary school, **and** (d) college, university or some form of tertiary education. **Because** paternal and maternal educational attainment **were** also highly correlated (r - .58), we aaalyzed the *affects of maternal educational attainment only. District level per capita income **was** derived from World Bank estimates.

Student educational expectations were measured by a single item that asked about the number or year more of full-time education the student expected to complete.

Parental encouragement was measured by a four-item index composed of Likert-type statements a-king students to describe their parent's intarest in and encourageme for mathematics achievement, for example "My parents encourage me to learn **as** much mAthematics as possible"; response alternatives ranged from "Exactly like" the student's parent (- 1) to "Not at all like" the student's parent (- 5). The four items comprised a single factor, with principal component factor loadings ranging from .72 to .83 and communality of 2.43. A low score represented greater perceived parental support.

Student attitudes. Three indices of student attitudes were analyzed: (a) perceived mathematics ability, (b) perceived usefulness of mathematics, and (c) motivation toward mathematics achievement. All were developed from a factor analysis of the student attitude survey, which contained Likert-type items having response alternatives ranging from "Strongly disagree (- 1)" to "Strongly agree ( 5)." Factors were initially identified through varimax factor analyses, and then confirmed through principal component analyses, from which factor scores were constructed. Perceived mathematics ability (or mathematics

- 10 -