rafmacalaba/data-use-mentions-extended huggingface dataset:
3 wide database combining and updating several financial data sets. The database is made publicly available at www.worldbank.org/financialdevelopment and http://data.worldbank.org/data- <u>catalog/global-financial-development</u> _._ The paper, however, goes beyond just compiling data. It aims to answer some substantive questions using the data. The questions addressed in this paper are: How can one empirically characterize different characteristics of financial systems? How can one compare financial systems across countries and regions and through time? How have financial systems been affected by the global financial crisis, and what are the key trends in the aftermath of the crisis?

To measure and benchmark financial systems, the paper develops several measures of four characteristics of financial institutions and markets: (a) the size of financial institutions and markets (financial depth), (b) the degree to which individuals can and do use financial institutions and markets (access), (c) the efficiency of financial institutions and markets in providing financial services (efficiency), and (d) the stability of financial institutions and markets (stability). These four characteristics are measured both for financial institutions and financial markets (equity and bond markets), thus leading to a 4x2 matrix of financial system characteristics. The paper then uses these measures to characterize and compare financial systems across countries and over time and to assess the relationship between these measures of the financial system and key financial sector policies.

In focusing on these four characteristics of financial institutions and markets, the paper seeks to provide empirical shape and substance to the complex, multifaceted and sometimes amorphous concept of the ―functioning of financial systems.‖ The paper recognizes that financial depth, access, efficiency, and stability might not fully capture all features of financial systems and it makes no attempt to construct a single, composite index of financial systems. Rather, the paper uses these four characteristics as a basis for describing, comparing, and analyzing financial systems around the
2016m05 2016m07 2016m09 2016m11 2017m01 2017m03 2017m05 2017m07 2017m09 2017m11<br><!-- End of picture text --> Source: Shymanovich, Shcherbina, Chubrik (2018) However, the dynamics of household incomes do not correspond fully to the dynamics of household consumption (Figure 5). The scale of consumption reduction during the crisis years was much lower than the scale of income reduction, and it started with a half‐year delay. This difference can be attributed to savings behavior. The volume of bank deposits stopped growing in the second half of 2015 and steadily diminished afterwards if measured in relation to the cash income of the households. The spike at the beginning of 2016 is explained by currency exchange rate movements: as most of the deposits were denominated in US dollars, the depreciation of the national currency was reflected in their growth if measured in relation to incomes in Belarusian rubles.

An additional piece of evidence suggesting that households used up their savings during the crisis comes from the currency market data. In 2015, households became net sellers of foreign currency, selling US$130 million (for comparison, households bought US$1,385 million in 2014). This tendency continued in 2016 and 2017, when households sold US$1,895 million and US$1,759 million, respectively, according to the National Bank of Belarus. The currency sold may have come from cash and deposit savings as well as from remittances. Since the majority of remittances to Belarus come from Russia, and Russia also experienced an economic crisis following a major devaluation in 2014, remittance inflows to Belarus in 2015 and 2016 dropped to US$932 million and US$937 million in 2015 and 2016 compared to US$1,231 million in 2014 (World Bank, 2019).

Furthermore, the faster recovery of consumption in comparison to income is believed to be supported by consumer lending. Chubrik (2018) argues that consumer lending accounted for 1.2–1.5 percentage points 5
mention basic monitoring requirements such as a management information system (MIS). When monitoring was mentioned, it usually involved collecting extremely imprecise indicators, and even this was done irregularly. The most important reasons given for the paucity of effective monitoring were poor human and technical capacity and the lack of sufficient funding. On the crucial question of whether monitoring and evaluation data are a guide to learning by doing, only 14 percent of the projects explicitly outlined procedures that would be implemented if the monitoring and evaluation data indicated that a project had gone off track.

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Next, we look at US firm-level data to understand if we see similar effects in the corporate sector. Using annual financial statements from Compustat for US publicly listed companies, in Figure 6B, we present evidence on sales and short-term borrowing, aggregated across all firms. In constructing the sample, we drop Canadian listings that are included as part of the Compustat U.S. domestic file, firms incorporated outside the U.S., ADRs, and financial services (NAICS 52 or SIC industries 60-64 and 67) resulting in a balanced panel of 3338 firms that had non-missing _Sales_ (Income Statement data item #12) and _Debt in Current Liabilities_

> 20 As alternate measures we also use Loans and Leases by Commercial Banks, Commercial and Industrial Loans, and Consumer Loans. Loans and Leases by Commercial Banks is a sub-category of Bank Credit and includes Commercial and Industrial Loans, Real Estate Loans, Consumer Loans, Fed Funds, Repurchase Agreements with Brokers and other loans; Commercial and Industrial Loans is the corporate lending sub-component of Loans and Leases by Commercial Banks.

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in section 4.3. The results presented here are robust to the choice of cross-dimensional threshold.

> 11 The ENAHO survey uses the main native language spoken by respondents as the criteria for ethnic classification. The survey includes the following groups: Aymara, Quechua, other Indigenous (Ashaninka, Awajún, Bora y Shipibo-Konibo) and Non-Native (Spanish, Portuguese, other foreign language).

15
.00%<br>20.00%<br>0.00%<br>1 2 3 4 5 6 7 8 9 10<br>-20.00%<br>-40.00%<br>Direct Taxes Indirect Taxes Pension Contributions<br>Non-Pension Contributions Education Health<br>Net Benefit Position Net Cash Position<br>market income plus pension<br>Taxes/transfers as share of gross<br><!-- End of picture text --> _Source_ : World Bank staff calculation using BLSS 2017.

In the presence of multiple taxes and benefits, assessing the progressivity of individual fiscal interventions can be complex. To determine whether a particular intervention is redistributive, we rely on a few different concepts: the Kakwani index, the marginal contribution, and the concentration of each intervention.<sup>25</sup> The effect of specific fiscal interventions is summarized in appendix Table A.6.

> 25 Concentration can be measured using a coefficient that summarizes the concentration curve of a tax or transfer. The curve shows the cumulative percentage of households, ranked from poor to rich by market income, against the cumulative percentage of the tax paid or transfer received by centile. The Kakwani index is defined as the difference between the concentration coefficient of the tax and the Gini for market income. The marginal contribution of a tax

19
opinions. Many of these problems are obvious, but some of their manifestations are not. For example, a simple "yes/no" question can accidentally be manipulative or suggest to the respondent that there is a correct response by not including all possible responses in the question itself. A question that simply asks, "Do you do x?" may make the respondent more likely to answer "yes" since the alternative was not presented. One possibility is to ask instead, "Do you do x, or not?" (Fowler 1995). An induced response is more of a concern when one answer to a question is more socially acceptable than another. We discuss issues in asking sensitive questions below.

Asking multipurpose questions-questions that ask about more than one issue or along more than one dimension-turns out to be unfortunately simple and common. For example, the World Business Environment Survey (World Bank 1998) asks respondents to "rate the overall quality, integrity, and efficiency of services delivered by the following public agencies or services." Although quality, integrity, and efficiency are probably highly correlated, they are three separate issues and should be asked separately. Sometimes, however, the problem is not so obvious. "Agree/disagree" questions, for example, usually ask the respondent to rate how much she agrees or disagrees with a statement. The survey asks the respondent whether she "strongly agrees, agrees, disagrees, or strongly disagrees." The problem with this scale is that it combines an emotional component ("strongly") with the more important cognitive component. Instead, the survey should ask whether the respondent "completely agrees, generally agrees, generally disagrees, or completely disagrees" (Fowler 1995).

Careful survey writing can minimize problems of the sort described above. Other problems are more complicated. Small differences in question wording, for example, can generate large differences in responses. A particularly well-known example is from a 1941 experiment in which matched samples of respondents were asked one of
. If the change in the rate of protection is negative during a period of rising world prices, countries are seeking to insulate their markets from the increase in prices. If it is positive, policymakers are compounding the increase in world prices with an increase in protection, which may be due to the correction of past “errors”: If domestic prices fall below policymakers’ desired long-run level of protection, or if a policy that insulated the domestic market from world markets and a subsequent exogenous shock—such as a harvest shortfall—has caused the domestic price to rise relative to the world price.

C.D. Median and interquartile range in the change for protection rates for rice, wheat, and maize in EMDEs (C) and LICs (D).
from non-resource sectors. Public investment surges, often debt- financed during periods of booming commodity prices, tend to fizzle out quickly when external conditions deteriorate.

For SSA as a whole, investment growth averaged 3.3 percent a year in 2011-21—almost half of its annual average in 2000-08 (figure 17.A). Rapid public investment growth cooled after 2014, and private investment decelerated sharply. For example, investment growth in Ethiopia averaged almost 28 percent a year in 2008-14, driven by exceptionally rapid public infrastructure investment (World Bank 2015). However, investment growth slowed sharply to just 9.3 percent in 2015-21 because of elevated public sector debt, unfavorable external environment, and rising insecurity. Severe economic slowdowns in the region’s two largest economies, Nigeria and South Africa, had adverse spillovers on investment across the region as well. In 2021, investment growth was below its 2000-21 average in almost half of SSA countries, and negative in about 16 percent of countries (figure 17.B).

Investment fell by 0.7 percent per year, on average, in South Africa in 2011-21, compared with over 9 percent average annual growth in 2000-08, reflecting a sharp deterioration in the country’s economic fundamentals stemming from the lack of policies to tackle underlying structural constraints, including substantial inefficiencies in state-owned enterprises (SOEs), high unemployment, and the energy crisis triggered by worsening power cuts. Investment by SOEs has played a major role in South Africa, representing almost 45 percent of all public sector capital expenditure in 2014-20, although this share has declined over time. Much of the recent weakness in public investment spending can be attributed to Eskom, a public utility supplying electricity, which has had significant governance and profitability problems, although it still accounts for about a half of all capital expenditure by SOEs (Statistics South Africa 2021).

Among oil exporters, investment growth also slowed significantly after 2014 in Angola, Chad, and Nigeria, and turned negative in Equatorial Guinea, where oil production fell
the scale-up of social protection programs in humanitarian contexts (fragile and conflict-affected populations), which we define in this paper as humanitarian assistance interventions.

> 1 For more information on humanitarian crises, see Humanitarian Coalition (last accessed December 2021). 2 In more stable developing contexts, a vast literature in social sciences shows that social protection programs help reduce poverty and inequality, enhance livelihoods, and have long-term positive impacts on human capital development (Baird et al. 2014; Bastagli et al. 2016; Davis et al. 2016; Handa et al. 2018; Hidrobo et al. 2018). 3 According to the 2020 Global Humanitarian Assistance Report, there is a clear upward trend in the use of social protection programs in the form of cash and vouchers. For example, the use of cash-based transfers in humanitarian crises has doubled since 2015, amounting to a total volume of US$5.6 billion or 17.9 percent of the global humanitarian assistance in 2019. However, cash and voucher transfers still account for a minor share of humanitarian assistance, with the majority of the humanitarian portfolio being provided in-kind (CaLP 2020; Overseas Development Institute 2015).

2
importance. This seems consistent with our previous findings that (a) entry barriers seem to matter more for countries exhibiting low levels of institutions which are usually economies which are more reliant on industry rather than services, and (b) entry barriers appear to me most important for sector which are lying further away from the technology frontier which is most likely the case for countries which are less‐ developed and incidentally also more dependent on industry than services.

# **4.2.5  Exogenous US I/O coefficients**

As our discussion on country‐specific or EU‐wide input‐output coefficients indicated, either approach could still run the risk that there could be cases where a higher downstream productivity term of sectors leads to higher input sourcing within or across EU countries. To reduce this endogeneity problem, we use completely exogenous input‐output coefficients from the US. When we do this, the detailed 6‐digit Input‐Output Use Tables from the US Bureau of Economic Analysis (BEA) are employed to compute these sourcing coefficients and are consequently concorded into our NACE classification system. They are then mapped with our regulatory regulations variables and used in the regressions.<sup>27</sup> The results of our regression using these concorded US coefficients are shown in Table 10 where we replicate the baseline model using the Ackerberg and Hsieh and Klenow TFP specifications. The results show a consistent pattern in that conduct regulations are across all columns highly significant. Note that for the Ackerberg measure when entered the various regulatory variables separately or together, conduct regulation is the only regulatory variable that comes out negatively significant. This is somewhat different when looking at the Hsieh and Klenow TFP. In there, both the overall, entry and conduct regulations are highly significant, also when entered jointly. However, looking at the coefficient sizes of all coefficients, conduct regulations appear to have and almost twice as large impact on downstream TFP compared with entry regulations. Both outcomes across the two TFP measures reassures us that our baseline
food production–or the value of agricultural endowments more generally–trigger conflict. We show that a case like of Ukraine where causality runs from conflict to food production, can be analyzed using satellite imagery to generate measures of agricultural production as outcome variables as well as indicators of conflict beyond what is routinely discussed in the literature and, for the latter, demonstrate that our measure outperforms measures of conflict used routinely in other studies.

Second, most of literature focused on direct impacts of conflict, sometimes on long-term outcomes such as human capital rather than production. The massive population movements, economic disruption, and input shortages experienced by Ukrainian farmers due to the war suggest that, in addition to direct effects, indirect ones need to be considered. Constructing a panel of VCs provides an expedient way to obtain an estimate of indirect effects in a way that avoids selection issues based on the assumption that all changes over time not explicitly controlled for can be directly or indirectly attributed to the conflict. While this implies our estimate is an upper bound, it provides a basis for further scrutiny or disaggregation, ideally complemented by a more structural approach towards identifying channels through which such effects materialize.

Finally, while much of existing analyses are ex post, often involving significant time lags, use of proxies such as NDVI at a high level of granularity allows our estimates to be used as a basis for measures (e.g., cash or in-kind transfers or opening up of markets) to help attenuate immediate conflict effects and in doing 3
economic outcome at the household level and is associated with income growth and development. Second, given the possibility of negative

> 24 In practice, however, some plant-year observations have recorded fewer than ten workers. This could perhaps be because once an establishment crosses the ten-employee threshold and has been surveyed, it remains part of the census even if its size shrinks below this threshold. It should be noted that the year 2005 is peculiar because CSA sampled only some industries in this year.

> 25 In 2001/02, about 97 percent of the manufacturing establishments were micro firms employing fewer than 10 people. 26 Using data from six developing countries, Cusolito and Maloney (2019) document entrepreneurial dynamism through entry and exit rates, and again Ethiopia stands out in terms of high exit rates.

8
account for the potential intervening e↵ect of individual-level optimism or general state of mind on the day of the interview, as these could theoretically bias perceptions of public policy or directly a↵ect individuals’ propensity to trust, and therefore interfere with our results. Results are robust to the inclusion of this additional control and show that satisfaction with policy outcomes and processes is tied to individuals’ trust in institutions, independent of their perception of their own situation.

Table 6: Correlates of trust (ordered logit model): controlling for optimism bias

||(1)|(2)|
|---|---|---|
||Institutional|Institutional|
||trust|trust|
|Satisfaction with|0.247***|0.246***|
|security services|(0.0526)|(0.0522)|
|Satisfaction with|0.249***|0.245***|
|social services|(0.0854)|(0.0871)|
|Satisfaction with|0.269***|0.268***|
|water and sanitation|(0.0542)|(0.0534)|
|Satisfaction with|0.167**|0.166**|
|economic performance (Index)|(0.0709)|(0.0708)|
|Integrity perceptions|0.638***|0.635***|
|(Index)|(0.108)|(0.106)|
|Equity perceptions|0.126***|0.122***|
||(0.0313)|(0.0302)|
|Policy credibility|0.348***|0.346***|
||(0.0584)|(0.0585)|
|Positive perceptions||0.0338|
sometimes with (proxies of) controls for student ability and/or school quality. Equation (1) is the standard approach, as in, for example, Patrinos and Psacharopoulos (2020) with the easy interpretation that  3 is closely related to the rate of return to one additional year of schooling. However, this approach forces the proportional increase in labor earnings from one year of schooling to be the same whether that year is at the primary level or the postsecondary level. Many studies, including in Ghana, find that returns are higher for higher levels of schooling, especially postsecondary schooling. See, for example, Urzúa (2019) or Turkson and Baffour (2020) for Ghana. To account for this, we estimate a semiparametric version of the same model: 2 ln(earnings) =  0 +  1  experience +  2  experience + _f_ ( years of schooling ) +  , (2) adding controls for region and area (urban/rural) of residence and mother’s and father’s level of schooling.

There is an extensive literature criticizing regressions such as equations (1) and (2), the most common criticism being ability bias: those with greater innate ability are both more likely to complete more years of schooling and have greater labor earnings. Since neither equation (1) nor equation (2) includes ability, the omitted variables bias for  3 is positive. Despite this intuitive argument, an early review of the literature by Griliches (1977) argues that ability bias is likely to be small in equation (1) and may be overwhelmed by larger negative biases. Card (1999) echoes that argument and uses twins data to estimate that the bias in the average return to schooling is about 10 percent when using OLS. Card also finds that many instrumental-variables estimates are _higher_ than OLS estimates, casting doubt on the ability to find an easy econometric fix for the OLS bias.

Another potential source of bias in equations (1) and
sector.<sup>25</sup> Formal mechanisms such as SALM committees are necessary to ensure that the coordination and exchange of information are sustainable in the long term and do not rely on personal or _ad hoc_ arrangements. For this reason, ALM committees (ALCO) are common practices in banks and other financial institutions, while dedicated committees have been in place in the public sector for the coordination of other policies, such as debt management committees (debt, monetary and fiscal policies) or macro prudential committees (financial sector).

The survey explored the existence and composition of an ALCO and/or risk management coordination committees at all levels. Overall, half of the respondents indicate that they have a high‐level risk management committee. Only four (Canada, Switzerland, U.K. and Uruguay) reported ALCO‐like committees to manage sovereign assets and liabilities (see graph 6).

Others mentioned coordination at least once a year between the minister of finance and the governor of the CB, or in the absence of a committee, the existence of a specific team, often in the Treasury, responsible for advising the government on ALM issues.

<!-- Start of picture text --> Graph 6 ‐ High Level Risk Committee<br>14 13<br>12 11<br>10<br>8<br>6<br>4 3<br>2<br>0<br>Yes No Other<br><!-- End of picture text --> Source: Responses to SALM survey Responses varied on the composition of the high‐level risk committees. The heads of the Treasury (5) or of the debt management office (6) are often mentioned as chair or member of the committee. Fewer countries report on the participation of the minister (3) or deputy minister (3). The governor of the central bank was mentioned once, with other officials from the central bank identified in a few more cases