rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. Introduction**

In 2018, for the first time in nearly two decades, Latin America and the Caribbean’s middle class became the largest socioeconomic group. It increased from more than a fifth of the LAC population in 2000 (21.6 percent) to more than a third in 2019 (37.6 percent), based on 2011 PPPs. However, during the pandemic, there was a rapid decline in the size of this group in most countries. As a result, LAC is no longer a middleclass region. This group shrunk by four percentage points in 2020, excluding Brazil, representing 13 million people falling into poverty. Moreover, this decrease reached similar levels as those in 2013. Peru, Colombia, and Argentina drove this significant reduction in 2020.<sup>3</sup> Governments must continue targeting policies to support the most vulnerable populations, particularly after the COVID-19 pandemic, followed by the Russian Federation–Ukraine war, which significantly impacted the region. Thus, it is important to accurately measure the size of the vulnerable population, monitor its evolution, and know where they live and their characteristics.

The major challenge regarding estimating the LAC region’s vulnerable and middle class is the identification of the lower and upper thresholds defined initially by Lopez-Calva & Ortiz-Juarez (2014) and Ferreira et al. (2013). To do so, the principal data that allow for the comparability of different countries’ living standards are purchasing power parities (PPPs). In May 2020, the International Comparison Program (ICP) published new 2017 PPPs. The 2017 PPPs reflect the most recent relative price differences across a wide range of countries around the world. Jolliffe et al. (2022) assessed the impact of the 2017 PPPs on global poverty by updating the three international thresholds: the $1.9 2011 PPP line to $2.15 2017 PPP per person per day, $3.2 2011 PPP to $3.65 2017 PPP per person per
across almost all income groups except for lower-middle-income countries. These results should be interpreted with caution due to a limited sample at the country-year level and a lack of a generally consistent cross-country source of major natural disasters.

To address important caveats of the cross-country results regarding data granularity and quality, we also present results from a country-specific example. Using banking sector data at the regional level, we study the impact of typhoons on NPL ratios between 2011 and 2018 for the Philippines, a country highly exposed to climate related natural disasters. We find that a 1 percentage point increase in the typhoon damage ratio, defined as the amount of damage divided by regional GDP, could lead to an increase in the NPL ratio as large as 2.3 percent, an estimate larger in magnitude than these found for the cross-country sample. On balance, although the magnitude of our empirical results should be taken with caution due to concerns on data quality and granularity, the qualitative relationship between disaster episodes and NPLs seems reasonably robust.

Beyond the effects on bank asset quality, we also find tentative evidence that severe climate and environmental related natural disaster episodes may be associated with several other key banking sector variables, such as net interest margin, capital adequacy, credit to the private sector and liquidity-to-deposit ratio. For certain definitions and time horizons, these disaster episodes are found to be associated with: 1) increases in net interest margins, and hence higher cost of financial intermediation; 2) an increase in regulatory capital adequacy; 3) an increase of bank credit to the private sector; and 4) a decrease in liquid assets relative to deposits. While we do not identify a specific causal chain, our results point to multiple channels from physical risks to financial

> 1The World Bank country regional classification is available at the following link: https://datahelpdesk.worldbank. org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

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structure and industrial growth patterns, but overall financial development and the component of overall financial development explained by the legal environment help explain industrial growth patterns, especially the formation of new firms.

Finally, our previous results concerning financial development and financial structure are not due to peculiar characteristics of industries in the U.S. We use the external dependence as calculated for a sample of Canadian firms, which RZ note is the only other country for which firm-level flow of funds are available. We confirm our results concerning financial structure. However, using the Canadian data, we cannot confirm the results concerning the legal-based view and the results on the financial services view are weakened. These results might be partly explained by the fact that we have data for only 27 industries in the Canadian sample, whereas there are at least 36 industries in the text specification. Furthermore, the sample size drops from 1222 to 702. Thus, with some qualifications, the robustness checks confirm the text's main conclusions: (1) industries that are heavily dependent on external finance do not grow faster in bank-based or market-based financial system, (2) externally dependent industries do, however, tend to grow faster in countries with better-developed financial systems and especially in economies that efficiently project the legal rights of outside investors, and (3) overall financial development and the legal protection of investors stimulates industry growth primarily by facilitating new firm formation.

# **6. Conclusions**

This paper examined the following questions: Do industries that depend heavily on external finance grow faster in bank-based or market-based systems? Are new firms more likely to form in a bank-based or market-based financial system? Alternatively, is it the overall level of 26
# **4 Discussion and Policy Implications**

Drawing on a variety of new and existing data sources, this paper has aimed to shed light on the patterns and trends of public spending on infrastructure in low- and middle-income countries.

Overall, public expenditure on infrastructure in the developing world was found to be low and declining, reaching its lowest levels since 2010 at just below 1 percent of GDP in 2018. Infrastructure accounts for a low and stable share of no more than 5 percent of total government expenditure. However, investment in infrastructure accounts for a somewhat higher share of 15 percent of overall public investment; albeit reaching higher shares of around 30 percent for some countries during limited time windows.

There is evidence that low-income countries, despite their lower overall levels of public expenditure, were inclined to devote significantly higher shares of public expenditure and public investment to infrastructure at least up until 2015, but with a marked decline subsequently. Over the period 2010-18, there has been a marked sectoral shift in public expenditure for infrastructure, with spending on roads halving over this period even as spending on electricity has doubled.

It is striking that these numbers are much lower than previous high-level aggregated estimates of developing country infrastructure spending presented in Fay et al. (2019). However, there are important differences in the methodology that can adequately explain the differences. First, this analysis is limited to actual spending data and does not rely on any estimates involving assumptions or extrapolations. Second, the current data set does not include any of the known high infrastructure spending countries such as India and China, which due to their large size tend to inflate global estimates of infrastructure spending. In that sense, the current research provides a higher resolution more accurate understanding of spending patterns in the developing world outside the largest middle-income countries.

Low levels of public expenditure on infrastructure are further exacerbated by low budget execution ratios. Comparing budget allocations to executed amounts shows that countries manage to execute 94 percent of the budget allocation for capital projects in roads but only
shock of the COVID-19 prompting authorities to respond faster and at a larger scale. We use four indicators: the 2019 current account balance from the IMF World Economic Outlook (April 2020); the 2019 general government net lending (borrowing) taken from the IMF World Economic Outlook (April 2020);  and the 2018 total private-sector debt and total public sector debt as a percentage of GDP sourced through the World Bank and IMF Global Debt databases.

**Banking sector characteristics.** The initial state of the banking sector may also influence the reaction function of financial sector authorities. We explore the role of the following variables: regulatory Tier 1 capital to risk-weighted assets (IMF Financial Soundness Indicators, end-2019), ii) non-performing loans as a percent of total gross loans (IMF Financial Soundness Indicators, end-2019), iii)  domestic credit to the

> 13 https://www.bis.org/statistics/payment_stats/commentary2011.htm.

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Table A.15: Using _gross_ (instead of net) marriage payments

|**Dependent variable:**|**Household per **<br>**(fnancial institutions **<br>(1)|**capita saving in 2007**<br> **+ cash + interest earned)**<br>(2)<br>FE for #children|
|---|---|---|
|_Firstborn girl_ * _Expected gross payment by bride_|662.60*<br>[356.92]|656.62*<br>[357.88]|
|_Firstborn girl_ * _Expected gross payment by groom_|1066.17<br>[2129.19]|1094.13<br>[2148.93]|
|_Firstborn girl_|-2572.92<br>[3757.24]|-2539.77<br>[3787.71]|
|N||2,840|

NOTES: Instead of net dowry expectation in specification (2), here we use two gross dowry variables: _Expected gross (wedding) payment by bride_ and _Expected gross (wedding) payment by groom_ . Expected gross payment (in INR 10,000) by bride (groom) are defined as the average value of gifts given by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Household data is restricted to households where all children have the same mother. Each column is a separate regression. Column (2) also controls for indicators for the number of children. _Firstborn girl_ indicates that the firstborn child of the household is female. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.
- 13 -

**Figure 6: Relation between international achievement score and per capita** GNP, **circa early** 1990s

<!-- Start of picture text --> 60<br>0<br>0 0 0 0<br>0<br>O ~ ~ ~~~~ %o 0<br>50 ~~~~~0 0 0 0O<br>t500 0<br>0~~~~<br>0 ~~~~~~~~0<br>00<br>240<br>0<br>0<br>0<br>200' 400 S0O 1500 3000 10000 20000<br>Pe oapita GNP<br><!-- End of picture text -->

# **2.4 Efficiency of the education system**

By efficiency we refer to two aspects of the education system's operation: student flow patterns, and output per unit of public spending on education.

_Efficiency of student flow._ The available data pertain to the first 8 grades of schooling. In primary education, high dropout rates imply inefficient systems because children who quit before completing the cycle, or who do so with less than four years of schooling, are unlikely to become permanently literate and numerate. Grade repetition also leads to wastage because repeaters use at least twice as much resources as other pupils to attain the same amount of education. To measure the combined influence of these problems we construct an index of student flow efficiency which is benchmarked against a system in which no child repeats or drops out.<sup>15</sup> An index of 100 means that all pupils complete primary school and that no one repeats; a smaller value, say 60, means that with a given amount of resources the system produces only 60 percent as many primary school completers as a system with no dropouts and no repeaters. The relation between the index and the per capita
in sq|
|**Dependent Variable: Real Earnings**<br>Without SGRY control<br>11.544_∗∗∗_<br>11.627_∗∗∗_<br>9.295_∗∗∗_<br>[3.108]<br>[3.016]<br>[2.635]|8.258_∗∗_<br>8.664_∗∗∗_<br>6.224_∗∗_<br>[3.279]<br>[3.143]<br>[2.746]|0.656<br>0.322<br>2.378|[2.208]<br>[2.216]<br>[2.247]|-0.930<br>-1.080<br>1.119<br>[2.327]<br>[2.235]<br>[2.162]<br>59,130<br>59,130<br>59,130|No<br>Yes<br>Yes<br>No<br>No<br>Yes<br>No<br>No<br>No<br>– 2008 (Rounds 61 and 64) and program expenditure data for SGRY<br>ia.<br>ch column presents results from a separate regression. All regression<br>sample is composed of all adults aged 18 to 60 interviewed from July<br>article. All estimates are computed using sampling weights. The stan<br>REGA: National Rural Employment Guarantee Act.|
|_× Dry × Star states_|_× Rainy × Star states_|_× Dry × Oth states_||_× Rainy × Oth states_|ols<br>ols FEs<br>l<br>analysis based on NSS 2004<br
14 The improvement in the export performance of developing countries during the 1990s was due mainly to increased demand in the commodities they exported and to a much lesser extent to diversification or increases in the market shares of their traditional exports. While this is true for the developing countries as a whole, different regions had different experiences: both Asia and Latin America improved their competitive position. By contrast Sub-Saharan Africa experienced slow growth in world trade for its export basket, reflecting the continued decline in primary commodity prices and slow demand growth for these commodities. The same was true for the least developed (World Bank, 2000).

In the decade of the 2000s, the trends of the 1990s continued in many ways— except, of course, for the increase in African exports of raw materials, a relatively rapid growth of Latin America exports of foodstuffs and an expansion of exports of fuels from developed countries and the BRICS (See Appendix Tables B-1 and B-2). During this period practically developing income groups and regions improved their competitiveness with respect to developed countries overall and in most sectors (Hanson, 2011).

The overall weak export performance of the LDCs, in the 1980s and 1990s was largely due to their dependence upon a small range of primary commodities (usually two or three), for the bulk of their export earnings. On average the top three LDC export commodities account for over 70 percent of the each country’s exports, and with the exception of Bangladesh, few countries have any significant exports of manufactures. On average, manufactures, mainly textiles and clothing, constituted about 20 percent of total LDC exports in the 1990s (WTO, 1997). Although these exports grew substantially over this period, it was not possible to overcome the weak
In a first stage, we assume that the occupation polarization is due only to changes of the parameters (“utility” returns) linking individual characteristics to specific occupations. To do that individual characteristics are kept at the levels they had in the initial year, but the parameters are changed from those of the initial year to those of the final year. This simulation assumes, for example, that college graduates at the end of the period have a different probability than college graduates at the beginning of the period to be in high‐skill occupations, but that the share of college graduates in the pool of workers is constant in the two periods. This simulated occupation structure is then used to calculate a simulated (“intermediate‐parameters”) earnings distribution. This is done by recalculating the earnings for each worker who has switched occupation with the returns to the occupations kept equal to those of the initial year.

In a second stage, we assume that the occupational structure changes only because the characteristics of the labor force are shifting. This exercise produces another simulated (“intermediate‐characteristics”) earnings distribution and accounts for the share of occupational changes and earnings inequality driven by factors such as changing age and gender composition of the labor force or educational upgrading, under the assumption that the probability of having a certain occupation conditional on individuals’ characteristics is the same as in the initial year. In a final stage, the model allows to simulate changes in earnings driven directly by changes in returns to specific occupations and workers (“intermediate‐ returns”).

Comparing the position of these three simulated intermediate earnings distributions – the “parameters”, the “characteristics”, and the “returns” one – with respect to the initial and the final distribution allows to decompose the total distributional change into parts that are associated to each of these specific factors.

Using repeated cross‐sections of household surveys for Germany, Poland and Spain from the early 1990s to 2013, this paper shows that this period was characterized by a significant
payments) changes the nature of the game from a social dilemma into a coordination game. Existing theory predicts that threshold group payments are (weakly) more likely to result in better conservation outcomes (Isaac _et al._ , 1989; Andreoni, 1998; Barrett and Dannenberg, 2014); we develop new theory to prove that conservation outcomes can actually be worse under threshold group payments – if and only if uncertainty about the relationship between conservation effort and tree survival rates is sufficiently large. We find that trees survival rates are higher with linear than with threshold group payments – possibly due to the harsh circumstances for saplings to survive in Burkina Faso. We use both endline surveys as well as a lab experiment to gain more insight into the underlying mechanism.

We find that the threshold incentives can significantly improve the conditions for cooperation (as they induce higher trust among group members, result in more equal effort contribution and give rise to more frequent group meetings for maintenance planning). Our theory suggests that the fact that the thresholds have not outperformed the linear group payment scheme may have been due to two reasons. One is that not all thresholds are equilibria – it may not be optimal to coordinate on reaching the highest ones – and hence cooperation may have started (too) late. The second is that uncertainty about the effectiveness of maintenance activity in ensuring tree survival is substantial, and hence that the marginal benefits of putting in effort may actually become lower under threshold group payments than in the presence of a linear group payment scheme.

We find suggestive evidence in support of both mechanisms in our field-experimental data, but additional evidence for this also comes from the lab experiment, which shows that with threshold group payments initial survival rates are lower but also that they are declining less fast over time than in the linear group payment scheme. The laboratory experiment also provides (suggestive) evidence for the claim that threshold group payments might have outperformed the linear group payment scheme had our participants in the field been given regularly updates on survival rates during
Viterbo, P., Shuttleworth, W.J., Blyth, E., Österle, H., Adam, J.C., Bellouin, N., Boucher, O., Best, M., 2011. Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12, 823–848.

- Winsemius, H.C., Van Beek, L.P.H., Jongman, B., Ward, P.J., Bouwman, A., 2013. A framework for global river flood risk assessments. Hydrol. Earth Syst. Sci. 17, 1871–1892.

**_Authors contributions_** : JR contributed to the design of the analysis, carried out the economic analysis and wrote the paper. CBG was the TTL of the Regional Study, contributed to the design of the analysis and led the discussions with the clients in Peru. XL did the network modeling. LB contributed to the analysis and discussions with the clients and helped write the paper. HM worked on the HDM4 model and helped with the Regional study.

**_Acknowledgements_** : Special thanks go to peer reviewers of the Regional Study Marianne Fay, Andreas Dietrich Kopp and Baher El-Hifnawi; and to Augusto de la Torre, Daniel Lederman, Jose Luis Irigoyen and Aurelio Menendez for their overall supervision. The team also benefited from very useful comments provided by Siobhan Murray, Adrien Vogt-Schilb and Stephane Hallegatte.

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Evidence” IZA Discussion Papers, No. 11159.

- Cardarelli R., and L. Lusinyan. 2015. “U.S. total factor productivity slowdown: evidence from the U.S.”. IMF working paper 15/116

- Chen C-F., P. Wang and J-T Liu. 2016. “Knowledge spillovers, human capital and productivity.” Journal of Macroeconomics. Vol. 47, Part B. Pages 214-232.

- Ciccone, A., and E. Papaioannou. 2009. “Human Capital, the Structure of Production, and Growth.” _The Review of Economics and Statistics_ 91(1): 66‒82.

- Cusolito, A.P., and W. F. Maloney. 2018. “Productivity Revised. Shifting Paradigms in Analysis and Policy. _”_ Washington, DC: World Bank Group.

- De Almeida, L. A., and V. Balasundharam 2018. “On the Impact of Structural Reforms on Output and Employment: Evidence from a Cross-country Firm-level Analysis.” IMF Working Paper No. 73, International Monetary Fund, Washington, DC.

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International Financial Statistics. International Monetary Fund. Washington, DC. Accessed February 16, 2024.

<mark>Kanbur, SM Ravi, ed. 2003.</mark> _<mark>Q</mark> -squared, combining qualitative and quantitative methods in poverty appraisal_ <mark>. Orient Blackswan.</mark> <mark>Khalid, Muhammed Abdul. 2018. "Climbing the Ladder: Socioeconomic Mobility in Malaysia,"</mark> _<mark>Asian Economic Papers</mark>_ <mark>, vol. 17(3), 1-23, Fall.</mark> <mark>Khalid, Muhammed Abdul and Li Yang. 2021. "Income inequality and ethnic cleavages in Malaysia: Evidence from distributional national accounts (1984–2014),"</mark> _<mark>Journal of Asian Economics</mark>_ <mark>, vol. 72(C).</mark> 53
# **EXECUTIVE SUMMARY**

**Slowing investment growth.** Investment growth in emerging market and developing economies (EMDEs) has slowed sharply since 2010, declining from 10 percent, on average, in 2010 to 3.4 percent in 2015. Investment growth was not only well below its pre‐crisis average, but was also below its long‐term average in nearly 70 percent of EMDEs in 2015. Surveys of long‐term forecasts suggest that investment weakness is expected to persist (Figure A1).

**Factors associated with the slowdown.** In commodity importers, slowing FDI inflows and spillovers from soft activity in major economies appeared to account for much of the slowdown in investment growth. In commodity exporters, a sharp terms of trade deterioration (for energy exporters) and mounting private debt burdens were the main factors associated with the slowdown in investment growth. Political and policy uncertainty has further contributed to lower investment growth in several EMDEs.

**Spillovers.** Over the past five years, advanced economy growth has repeatedly fallen short of expectations, partly because of crisis legacies. Sub‐par growth and growth prospects in advanced‐economy trading partners and source countries for FDI into EMDEs have slowed EMDE output growth. For every 1 percentage point lower output growth in the United States or Euro Area, EMDE output growth fell 0.8‐1.3 percentage points within a year. EMDE investment growth responded about twice as strongly as EMDE output to declines in U.S. and Euro Area growth. Sluggish economic activity in major advanced economies has coincided with a policy‐driven slowdown in investment growth in China. This has contributed to weakening global commodity prices and has weighed on growth in other EMDEs through inter‐sectoral input‐output links and, indirectly, via output growth spillovers.

**“Investment‐less” credit booms.** Investment weakness has been set against the backdrop of exceptionally benign domestic (and global) financing conditions until late 2016. Policy interest