rafmacalaba/data-use-mentions-extended huggingface dataset:
- 25 -

infrastructure placement. In addition, there are few timeseries data measuring transport accessibility.

50. The paper particularly contributed to two issues. First, the paper applied spatial data and techniques to generate detailed georeferenced connectivity data in the region, using micro shipping data over 10 years. As shown, this is a useful approach to measure historical changes in transport accessibility at the highly disaggregated level. The same approach can be used everywhere as long as shipping data are available. Second, using such granular connectivity data, a dynamic panel regression model was applied to estimate the unbiased impact of transport connectivity on firm agglomeration. This is an alternative approach to the IV technique which is used in recent empirical studies investigating various impacts of transport infrastructure (e.g., Datta, 2012, Donaldson, 2018).

51. For the Caucasus and Central Asian countries, regional integration and transport connectivity are of vital importance. The region experienced significant economic transition during the 1990s. Since the early 2010s, a number of large transport investments have been made to reestablish regional connectivity. The paper cast light on such a dynamic period of time in the region and examined how seemingly improved transport connectivity affected the spatial distribution of firms, i.e., agglomeration economies.

52. It is found that the system GMM regression could control for the endogeneity problem to generate consistent estimation results. The systematically generated lagged differences and levels of the endogenous variables were found to be valid instruments. As expected, it is shown that agglomeration economies are significant in the region. Firms tend to be located where other firms are also located. The agglomeration process was also found to be highly persistent. Thus, the existing firm clusters are likely to continue growing, attracting more new enterprises. The evidence is consistent with the economic geography literature. However, it was also found that populated areas in the region suffer from congestion diseconomies. Holding everything else constant, firms are less likely to choose populated areas. This indicates that large cities are faced with
PISA.** The third simulation uses OECD’s (Organization for Economic Co-operation and Development) PISA, which measures learning outcomes of 15-year-olds in reading, math, science, and other skills such as collective problem-solving. Unlike the Learning Poverty analysis, which focuses on outcomes at the primary level, the analysis of education proficiency based on PISA focuses on student achievement of 15-year-olds, who are generally enrolled at the lower secondary level. Focusing on reading proficiency scores, we simulate how the share of children performing below minimum proficiency (PISA Level 2 or 407.47 points) could potentially change due to school closures and mitigation effectiveness of remote learning.

- **Outcome 4: Lifetime Economic Loss.** The fourth simulation presents the results from LAYS in monetary terms. As done in <u>Azevedo, Hasan et al. (2021), we use expected earnings information</u> from <u><mark>ILO (2020)</mark></u> and <u><mark>World Bank (2020)</mark></u> , together with the expected long-run return to education. We also compute aggregate results by bringing all expected earnings losses to their present value, assuming a work life of 45 years and a 3% discount rate. To make these results more realistic, we also adjust the aggregate loss by the expected adult survival rate (following the World Bank HCI) and the fact that not all workers will always be in gainful employment.

See Annex C for the percentage of population covered for the key outcome variables.

## Outcome 1. Learning Poverty

In the previous release of Learning Poverty results, baseline Learning Poverty was 52.7% for low- and middle-income<sup>19</sup> countries in 2015. In the current release, baseline global Learning Poverty for these

> 18 To learn more about the Learning Poverty indicator update see Azevedo, Montoya et al. (2021). For Learning Poverty,
**Figure 3.7:** Mortgage Penetration and Economic Growth in SSA

<!-- Start of picture text --> 0<br>-1.5 -1 -0.5 0 0.5 1<br>-0.5<br>-1<br>-1.5<br>-2 y = 0.1383x - 1.2774<br>R2 = 0.0696<br>GDP growth (%)<br><!-- End of picture text --> <!-- Start of picture text --> R2 = 0.0696<br>-2.5<br><!-- End of picture text --> _Source_ : Authors’ calculation based on a collection of data from a survey, the World Bank and Housing Finance in Africa yearbooks 2010, 2011 and 2013. Available at: <u>www.housingfinanceafrica.org (accessed: Sept</u> 08, 2014).

_Note_ : variables are in logarithmic terms on both axes.

**Figure 3.8:** Mortgage Penetration and Gini Coefficient in SSA

<!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2 y = -0.489x + 0.4524<br>0.1 R2 = 0.0143<br>0<br>-0.02 0 0.02 0.04 0.06 0.08<br>Mortgage penetration (%)<br>Gini coeficient function of mortgage depth<br>Linear (Gini coeficient function of mortgage depth)<br>Gini coeficient (%)<br><!-- End of picture text --> _Source_ : Authors’ calculation based on a collection of data from a survey, the World
<!-- Start of picture text --> 40 50<br>= 35 45 =<br>r= 40 5<br>'E 30 B<br>5&?2 3530 =2Q<br>a a<br>2 20 235<br>7&<br>815 20 =<br>2 ua te 221 126 sg<br>Fwd=c 10 7.9 8.3 8.6 9.0 9.4 9.8i 10.2 10.7 155-3<br>g561 64 67 707: 7.3 76 : 10 »°o<br>00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Years<br>Urban population (million) lm Kinshasa population (million)<br>—=®=— Urban population share (%)<br><!-- End of picture text -->
when migrant stocks are roughly split between the two (and/or are predominantly in other countries).<sup>4</sup> We also gathered data on the stock of migrant workers around the world. Where such data is available, it generally refers to the total number of migrant workers _present_ in a specific country. Given our interest in remittance _receipts_ , however, we needed data on the total number of migrant workers _originating_ from a specific country, regardless of which country they migrated to. Such data are available from the OECD’s _Database on Immigration and Expatriates_ , but only for the year 2000, and they only cover migrant workers residing in OECD countries. This latter restriction, however, may not be too problematic, given that the OECD is likely the source for the bulk of remittances to developing economies.

Other variables used in the estimation include: per capita domestic output; per capita output in the country where most of a country’s migrant workers are located (“main host output”); an indicator denoting the presence of a dual exchange rate system (“dual exchange rate dummy”); country-specific financial risk (“financial risk”); the 3- firm concentration ratio for the banking sector (“bank concentration”); and a dollarization dummy. The Appendix provides further details on precise definitions and sources.

Table 1 reports summary statistics for the cost and financial variables. In terms of transactions costs, countries in Latin America have the lowest exchange spreads, while countries in South Asia have the lowest fees. Sub-Saharan Africa performs poorly all indicators.

> 4 The results are robust to using only cost data from the United States.  There is no full sample for the United Kingdom data, as costs from the U.K. to some Latin American countries were missing.

14
Policy Research Working Paper 6160

# **Abstract**

This paper examines the relationship between firm performance and growth and the business environment in the countries of the South Asia Region—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—using firm-level data from the World Bank’s Enterprise Surveys. The analysis uses an approach in which the responses of firms to questions about the quality of the business environment can be interpreted as shadow prices: estimations by managers of the cost imposed on the firm by inadequacies of an aspect of the business environment—public inputs such as regulation, physical infrastructure, availability of skilled labor, macroeconomic conditions, rule of law, etc.—for the growth of their firm. The analysis finds, in line with this approach, that higher-productivity and better-performing firms in the region, and in particular firms that recently expanded their employment and created jobs, report significantly higher constraints in terms of the supply of public inputs. The authors discuss the differences across countries in the importance of various industries, how they relate to various firm characteristics, how informal and rural sector firms are constrained by public inputs, and how firms in the South Asia Region countries compare with firms in the rest of the world.

This paper is a product of the Human Development Department, South Asia 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://econ.worldbank.org. The author may be contacted at m.e.schaffer@hw.ac.uk.

_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.
, & Freer, J. E. (2015). A High-resolution Global Flood Hazard Model.</mark> _<mark>Water resources research</mark>_ <mark>,</mark> _<mark>51</mark>_ <mark>(9), 7358-7381.</mark>

- Schiavina, M., Freire, S., Alessandra C., & MacManus, K. (2023). GHS-POP R20–3A - GHS Population Grid Multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] <u>https://doi.org/10.2905/2FF68</u>

<u>2-5B5B-4A22-8F40-C41DA8332CFE</u>

- Schiavina, M., Melchiorri, M., & Pesaresi, M. (2023). GHS-SMOD R20–3A - GHS Settlement Layers, Application of the Degree of Urbanisation Methodology (stage I) to GHS-POP R2023A and GHSBUILT-S R2023A, multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] https://doi.org/10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA

- Schiavina, M., Melchiorri, M., Pesaresi, M., Politis, P., Carneiro Freire, S.M., Maffenini, L., Florio, P., Ehrlich, D., Goch, K., Carioli, A., Uhl, J., Tommasi, P. & Kemper, T. (2023). GHSL Data Package 2023. Publications Office of the European Union, Luxembourg. https://dx.doi.org/10.2760/098587

- Smith, A., Bates, P. D., Wing, O., Sampson, C., Quinn, N., & Neal, J. (2019). New estimates of flood exposure in developing countries using high-resolution population data. _Nature Communications_ , _10_ (1), 1-7. https:/
excessive risk taking. Available data so far provides inconclusive evidence of significant impact of NIRP on bank profitability. While some bank surveys indicate a perception among survey participants that NIRP are having an adverse impact on banks’ net income, bank lending margins remain within post-crisis ranges and credit growth has improved, albeit sluggishly in most cases. An event study also shows that the initial reaction of bank equity prices to NIRP announcements has been muted in most cases, while financial sector CDS spreads have generally declined on the announcement day.

Most central banks have introduced complementary policy measures to mitigate the potentially negative impact of NIRP on bank profitability. For example, in the case of the ECB, the implementation of NIRP was part of a package of measures including exceptionally favorable refinancing conditions for banks as well as expanded asset purchases. The BOJ, Riksbank, DNB, and SNB also implemented additional measures to alleviate pressure on bank earnings stemming from NIRP.

With the rising share of negative-yielding debt securities, pension and life insurance companies could struggle to generate adequate returns to meet their long-term liabilities. NIRP, as other unconventional monetary policies, could also encourage excessive risk-taking, which could 45
even in the absence of information on the relevant preference.

Second, in spite of its large intrinsic value, longevity is ignored by all global multidimensional poverty measures. Mortality data are often available but these data are not used. One difficulty is that the joint distribution between mortality and other achievements is almost always missing. However, this does not mean that indicators that ignore mortality yield better cross-regions comparisons, quite the contrary. There exists several conceptually sound indices that integrate mortality into poverty measures while assuming that all individuals within a given region face the same mortality risks. These indices are “hybrid” in the sense that they account for the joint distribution between some but not all variables considered.<sup>2</sup>

> 2For instance, assume that the joint distribution between two variables x1 and x2 (say income and education) is known, but the joint distribution between variable x3 (mortality) and the other two variables is unknown. Consider an index that aggregates x1, x2 and x3 while taking into account the joint distribution between x1 and x2. This index is “hybrid” in the sense that it accounts for the joint distribution between some but not all variables considered.

3
<br>20<br>15<br>10 10<br><!-- End of picture text -->

**Note:** The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. The imputed poverty rates for TA 1 use the estimated parameters based on NPS 2020/21. 1000 simulations are implemented. All estimates are obtained with the population weights. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line from NPS 2020/21 deflated to 2022 prices. The sample size of TA 1 is selected as a percentage of the target survey varying from (randomly selected) 10% to 100% of the TA sample (with the sample size in parentheses). The sample size of NPS 2020/21 is selected as a percentage of the base survey sample, varying from (randomly selected) 10%, 20%, 30% to 100% of the NPS 2020/21 sample (with the sample size in parentheses). The total sample size of TA 1 is 711 households, and the sample size of NPS 2020/21 is 4,644 households.

70
give the patient aspirin. Das et al. (2012) show that the average interaction for this standardized patient lasts 3.5 minutes, with doctors asking 3 questions and completing 1.5 examinations. Das et al. (2012) suggest that a minimally correct treatment would require the doctor to complete at least one of these actions but would not penalize the doctor for additional unnecessary tests and medicines, even if they are contraindicated or harmful. Standardized patients in their study receive such a minimally correct treatment in 31.2 percent of interactions and unnecessary or harmful treatment in 55.2 percent.

Figure 1 shows that the significant deficits in care uncovered in Das et al. (2012) hold for multiple conditions and study sites. For each study, the top bar shows the share of patients receiving the minimally correct treatment and the bottom bar the share who received an antibiotic, which was inappropriate for all the conditions represented in the figure and is therefore a measure of an unnecessary and potentially harmful treatment. Across these studies, 40-90 percent of standardized patients are incorrectly treated, which means that they are treated for entirely the _wrong thing_ —for instance, asthma or pneumonia instead of a heart attack. More stringent definitions that penalize the use of unnecessary medicines or require providers to administer _all_ the required components of the treatment reduce the fraction correctly treated to less than 5 percent (and for many conditions 0-1 percent). Banerjee et al. (2023) use data from five standardized patient studies to show that one consequence of these deficits in care is that 70-85 percent of all out-of-pocket expenditures can be attributed to incorrect care or overtreatment. Interestingly, 52-78 percent of this avoidable medical expenditure is due to misdiagnosis and incorrect care rather than over-treatment based on a correct diagnosis—a conclusion that holds equally for health care providers in the salaried public sector and in the fee-for-service private sector.

# Capacity Constraints

Three types of capacity constraints have been evoked to explain these deficits-- _overcrowding, lack of equipment_ and _lack of adequate medical
|
|Cyclicality of real|||||||
|GDP|0.164***|0.289***|0.229**|-0.042|0.188***|0.167***|
||(0.049)|(0.099)|(0.109)|(0.120)|(0.059)|(0.150)|
|Observations|1,954|653|357|221|440|283|
|# countries|116|39|21|13|26|17|

Notes. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table D.1 allows comparing the different determinants of procyclicality across regions. SSA and LAC, the more procyclical regions, show the lowest credit to private sector ratios. SSA has the highest perception of corruption. Also, it is the region with the highest degree of ethnic and religious fragmentation. Also, SSA has the most unequal distribution of resources, after MENA. All the conditioning factors point at SSA as the one with the highest procyclicality bias.

> 15 This is equivalent to relaxing the common know-how (common intercept for each country), and of common technology (common coefficient for each country) which are critical when using macro data.

> 16 When balanced, we have 17 observations per country (2000-2016), and six coefficients to be estimated, excluding the intercept. The **xtmg** command by Markus Eberhardt (https://sites.google.com/site/medevecon/code) is considered to regress the MG estimator on STATA software.

22
Managers.

**Table 8: East Africa Deal Volumes by Ticket Size and Geography (2007-2014)**

||**<US$1M**|**US$1-5M**|**US$5-10M**|**US$10-50M**|
|---|---|---|---|---|
|**Kenya**|**10**|**28**|**5**|**7**|
|Tanzania|0|6|5|1|
|Ethiopia|0|2|3|1|
|Uganda|1|6|1|0|
|Rwanda|1|2|0|0|
|**Total**|**12**|**44**|**14**|**9**|

_Source: KPMG/EAVCA_ The largest deals that have closed in East Africa have taken place in Kenya's oil and gas sector. The KPMG/EAVCA survey did not log any deals over US$50 million, but in 2015 there was one such deal in East Africa that was recorded by EMPEA: Helios Investment Partners made a US$100 million growth equity investment into Africa Oil Corp, headquartered in Vancouver, Canada, but with operations in Kenya. The largest known deal in East Africa since 2008 also involved Kenya's oil and gas sector, when Warburg Pincus made a US$600 million growth equity investment in Delonex, a global company based in London, but with a significant East Africa and Kenya presence. Kenyan fund managers that are capable of investing in excess of US$50 million per transaction include Actis Capital, ECP, and Helios Investment Partners. They tend to back large businesses with existing operations in East Africa or they invest in businesses with aspirations to expand into multiple markets in the region.

Although PE investors in Kenya and
## V.2 Effects of banking crises and epidemics

The effects of banking crises on the growth of TFP, investment, and employment tended to be short-lived (figures 10.D-F and 11.A-F). Five years after the average banking crisis, neither investment growth nor employment growth were statistically significantly lower than otherwise; only TFP growth was still significantly lower. Epidemics were associated, even five years later, with statistically significantly lower TFP growth, investment growth, and—in contrast to recessions and banking crises—potential labor supply growth. The effect of epidemics on investment growth after five years was somewhat stronger, and the effect on TFP growth weaker, than the effects of recessions (figures 10.D-F).

Banking crises had larger long-term adverse effects on TFP growth, investment growth, and employment growth in advanced economies than EMDEs, possibly reflecting the larger role of finance in, and greater financial development of, advanced economies. Conversely, epidemics had larger long-term adverse effects on these variables in EMDEs than in advanced economies, in part perhaps because EMDE governments and central banks had less policy room to dampen the economic effects of epidemic disruptions (figures 11.A-F).

# VI. Conclusions

Potential growth, the growth an economy can generate at full employment and full capacity, is critical for a sustained increase in living standards. It also anchors the calibration of macroeconomic policies. This study introduced the most comprehensive international database of potential growth, including the nine most widely used measures of potential growth for up to 173 countries over 1981-2021. At the global level, all measures point to a steady decline in potential growth in the past decade. This decline was internationally widespread, with potential growth in 2011-21 falling below its 200010 average in 70 percent of countries. The decline in potential growth between 2000-10 and 2011-21 was almost as large in advanced economies (0.8 percentage point per year) as in EMDEs (1.0 percentage point per year).

The study also presented an application of the new database
efforts in the realm of policy and programming, at the local and national levels; failure to do so will merely prolong the precarious role of women in the economic life of the nation.

# 2. Female Labor Force Trends in Post-Liberalization India

## 2.1 Database and Method

Confining our analysis to the post-liberalization era in India, we consider the NSSO data sets for all rounds between Round-50 (1993-94) and the latest available, Round-68, that was collected in 2011-12. In order to calculate labor force participation trends, we utilize information on Usual Principal and Subsidiary Status (UPSS) of the activity classification provided in these data sets.<sup>2</sup> Total FLFP rates, as well as those for rural and urban areas are provided below in Table 2. For the purposes of this study, we consider the working age population as inclusive of those in the age group of 15 years and above.

Table 2: Female Labor Force Participation Rates, Using UPSS for Ages 15 and Above (%)

||Rural|Urban|Total|
|---|---|---|---|
|1993-94|49.0|23.7|42.6|
|1999-00|46.0|21.0|39.5|
|2004-05|49.4|24.4|42.7|
|2009-10|37.8|19.4|32.6|
|2011-12|35.8|20.5|31.2|

> 2 Activity status’ is the activity which a person reports for a given reference period, with regard to their participation in economic and non-economic activities. According to this, a person could be in one of the following three broad activity statuses during a reference period: (i) working or being engaged in economic activity (work) as defined above; (ii) being not engaged in economic activity (work) but either making tangible efforts to seek '